Fitting magnetic field gradient with Heisenberg-scaling accuracy

We propose a quantum fitting scheme to estimate the magnetic field gradient with $N$-atom spins preparing in W state, which attains the Heisenberg-scaling accuracy. Our scheme combines the quantum multi-parameter estimation and the least square linear fitting method to achieve the quantum Cram\'{e}r-Rao bound (QCRB). We show that the estimated quantity achieves the Heisenberg-scaling accuracy. In single parameter estimation with assumption that the magnetic field is strictly linear, two optimal measurements can achieve the identical Heisenberg-scaling accuracy. Proper interpretation of the super-Heisenberg-scaling accuracy is presented. The scheme of quantum metrology combined with data fitting provides a new method in fast high precision measurements.Comment: 7 pages, 2 figure

Blooms of the woloszynskioid dinoflagellate Tovellia diexiensis sp nov (Dinophyceae) in Baishihai Lake at the eastern edge of Tibetan Plateau

Freshwater red tides due to dinoflagellates have caused spectacular and regular &quot;summer reddening&quot; in recent years in Baishihai Lake, a temperate, meromictic, meso- or oligotrophic, high-altitude, landslide-dammed, deep lake located at the eastern edge of Tibetan Plateau in China. Based on morphological and molecular analyses, the causative organism has been identified as a new woloszynskioid dinoflagellate, Tovellia diexiensis Q. Zhang et G.X. Liu sp. nov. The vegetative cells are 20-32 mu m long and 16-24 mu m wide. They have a hemispherical episome and a broadly rounded hyposome with a short characteristic antapical spine. Usually cells are bright red due to the presence of numerous red-pigmented bodies, which often masked the yellowish green discoid chloroplasts. The amphiesma of motile cells comprise mainly quadrilateral, pentagonal or hexagonal thin plates, arranged in 4-5 latitudinal series on the episome, 1 in the cingulum and 4 on, the hyposome. Molecular phylogenies based on small subunit ribosomal DNA and large subunit ribosomal DNA (LSU) indicate T diexiensis from Baishihai Lake to belong to the family Tovelliaceae, which was monophyletic in our LSU phylogenies. During the bloom-forming period in 2005, cell density of T diexiensis reached 9.15 x 10(5) cells L-1. Astaxanthin and its diester were found to be the major pigments in T diexiensis, resulting in a characteristic blood-red color of the water in Baishihai Lake.</p

Identification and characterization of gastrointestinal hormone immunoreactive cells in the skin and parotoids of Chinese toad Bufo gargarizans

The skin and skin secretion of Chinese toad Bufo gargarizans have long been used in traditional Chinese medicine. However, the exact types and location of bioactive substances in Bufo gargarizans skin still have not been fully elucidated. The aim of the study was to investigate the distribution and density of six types of gastrointestinal (GI) hormone immunoreactive (IR) cells in the skin and parotoids of Bufo gargarizans. Immunohistochemistry was used for qualitative and semiquantitative analysis of GI hormone presence in the dorsal and ventral skin, and parotoids of eight adult Chinese toads. Six types of IR cells were found: serotonin (5-HT), glucagon (GLU), gastrin (GAS), somatostatin (SS), pancreatic polypeptide (PP) and neuropeptide Y(NPY) IR cells. They were mainly present in the epidermis and skin glands. 5-HT-IR cells were distributed in all layers of epidermis and glands, with higher density in the glands. Glucagon was prominently expressed in the epidermis and the bottle-shaped glands of parotoids; however, it was not present in the granular glands of skin and parotoids. The distributions of GAS and SS-IR cells were similar since they were present mainly in mucous, granular and bottle-shaped glands, while these cell types were absent in the differentiated glands of parotoids. PP-IR cells were predominant in the granular glands and the bottle-shaped glands. The expression of NPY was high in epidermal stratum granulosum and mucous glands of the dorsal skin, the bottle-shaped glands and differentiated glands of parotoids, while NPY-IR was rarely seen in the granular glands of ventral skin, and not present in the granular glands of dorsal skin and parotoids. The expression of several types of GI hormones in the skin and parotoids of Bufo gargarizans varies depending on tissue and type of glands

Disco Intelligent Reflecting Surfaces: Active Channel Aging for Fully-Passive Jamming Attacks

Due to the open communications environment in wireless channels, wireless networks are vulnerable to jamming attacks. However, existing approaches for jamming rely on knowledge of the legitimate users' (LUs') channels, extra jamming power, or both. To raise concerns about the potential threats posed by illegitimate intelligent reflecting surfaces (IRSs), we propose an alternative method to launch jamming attacks on LUs without either LU channel state information (CSI) or jamming power. The proposed approach employs an adversarial IRS with random phase shifts, referred to as a "disco" IRS (DIRS), that acts like a "disco ball" to actively age the LUs' channels. Such active channel aging (ACA) interference can be used to launch jamming attacks on multi-user multiple-input single-output (MU-MISO) systems. The proposed DIRS-based fully-passive jammer (FPJ) can jam LUs with no additional jamming power or knowledge of the LU CSI, and it can not be mitigated by classical anti-jamming approaches. A theoretical analysis of the proposed DIRS-based FPJ that provides an evaluation of the DIRS-based jamming attacks is derived. Based on this detailed theoretical analysis, some unique properties of the proposed DIRS-based FPJ can be obtained. Furthermore, a design example of the proposed DIRS-based FPJ based on one-bit quantization of the IRS phases is demonstrated to be sufficient for implementing the jamming attack. In addition, numerical results are provided to show the effectiveness of the derived theoretical analysis and the jamming impact of the proposed DIRS-based FPJ

An Anti-Jamming Strategy for Disco Intelligent Reflecting Surfaces Based Fully-Passive Jamming Attacks

Emerging intelligent reflecting surfaces (IRSs) significantly improve system performance, while also pose a huge risk for physical layer security. A disco IRS (DIRS), i.e., an illegitimate IRS with random time-varying reflection properties, can be employed by an attacker to actively age the channels of legitimate users (LUs). Such active channel aging (ACA) generated by the DIRS-based fully-passive jammer (FPJ) can be applied to jam multi-user multiple-input single-output (MU-MISO) systems without relying on either jamming power or LU channel state information (CSI). To address the significant threats posed by the DIRS-based FPJ, an anti-jamming strategy is proposed that requires only the statistical characteristics of DIRS-jammed channels instead of their CSI. Statistical characteristics of DIRS-jammed channels are first derived, and then the anti-jamming precoder is given based on the derived statistical characteristics. Numerical results are also presented to evaluate the effectiveness of the proposed anti-jamming precoder against the DIRS-based FPJ

How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers

As a booming research area in the past decade, deep learning technologies have been driven by big data collected and processed on an unprecedented scale. However, privacy concerns arise due to the potential leakage of sensitive information from the training data. Recent research has revealed that deep learning models are vulnerable to various privacy attacks, including membership inference attacks, attribute inference attacks, and gradient inversion attacks. Notably, the efficacy of these attacks varies from model to model. In this paper, we answer a fundamental question: Does model architecture affect model privacy? By investigating representative model architectures from CNNs to Transformers, we demonstrate that Transformers generally exhibit higher vulnerability to privacy attacks compared to CNNs. Additionally, We identify the micro design of activation layers, stem layers, and LN layers, as major factors contributing to the resilience of CNNs against privacy attacks, while the presence of attention modules is another main factor that exacerbates the privacy vulnerability of Transformers. Our discovery reveals valuable insights for deep learning models to defend against privacy attacks and inspires the research community to develop privacy-friendly model architectures.Comment: Under revie

Establishment of core collection from apricot germplasm in China

This study aimed at establishing a core collection based on the analysis of data from simple sequence repeat (SSR) alleles and morphological and agronomical traits (MOR) of the primary core collection from apricot germplasm resources. The index of genetic diversity, and frequency ratios of retention and loss of the alleles were studied between cluster and random sampling methods at five sampling ratios. The results demonstrate that the cluster sampling method preceded random sampling, and cluster sampling of SSR combined with MOR at the rate of 80% was the best sampling strategy among all the sampling methods. Based on this sampling strategy, 120 accessions were selected as the core collection of apricot, which retained 100% alleles in the primary core collection and 100% phenotypic characters. The core collection developed had also been evaluated by using the data of six quantitative traits, which showed that the established core collection could well represent the genetic diversity of the original collection of 1501 apricot accessions.Keywords: Apricot, core collection, sampling strategy, simple sequence repeat (SSR) molecular markerAfrican Journal of Biotechnology Vol. 12(37), pp. 5577-558

AG-CRC: Anatomy-Guided Colorectal Cancer Segmentation in CT with Imperfect Anatomical Knowledge

When delineating lesions from medical images, a human expert can always keep in mind the anatomical structure behind the voxels. However, although high-quality (though not perfect) anatomical information can be retrieved from computed tomography (CT) scans with modern deep learning algorithms, it is still an open problem how these automatically generated organ masks can assist in addressing challenging lesion segmentation tasks, such as the segmentation of colorectal cancer (CRC). In this paper, we develop a novel Anatomy-Guided segmentation framework to exploit the auto-generated organ masks to aid CRC segmentation from CT, namely AG-CRC. First, we obtain multi-organ segmentation (MOS) masks with existing MOS models (e.g., TotalSegmentor) and further derive a more robust organ of interest (OOI) mask that may cover most of the colon-rectum and CRC voxels. Then, we propose an anatomy-guided training patch sampling strategy by optimizing a heuristic gain function that considers both the proximity of important regions (e.g., the tumor or organs of interest) and sample diversity. Third, we design a novel self-supervised learning scheme inspired by the topology of tubular organs like the colon to boost the model performance further. Finally, we employ a masked loss scheme to guide the model to focus solely on the essential learning region. We extensively evaluate the proposed method on two CRC segmentation datasets, where substantial performance improvement (5% to 9% in Dice) is achieved over current state-of-the-art medical image segmentation models, and the ablation studies further evidence the efficacy of every proposed component.Comment: under revie