110 research outputs found

    Shared Generative Latent Representation Learning for Multi-view Clustering

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    Clustering multi-view data has been a fundamental research topic in the computer vision community. It has been shown that a better accuracy can be achieved by integrating information of all the views than just using one view individually. However, the existing methods often struggle with the issues of dealing with the large-scale datasets and the poor performance in reconstructing samples. This paper proposes a novel multi-view clustering method by learning a shared generative latent representation that obeys a mixture of Gaussian distributions. The motivation is based on the fact that the multi-view data share a common latent embedding despite the diversity among the views. Specifically, benefited from the success of the deep generative learning, the proposed model not only can extract the nonlinear features from the views, but render a powerful ability in capturing the correlations among all the views. The extensive experimental results, on several datasets with different scales, demonstrate that the proposed method outperforms the state-of-the-art methods under a range of performance criteria

    Meta predictive learning model of natural languages

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    Large language models based on self-attention mechanisms have achieved astonishing performances not only in natural language itself, but also in a variety of tasks of different nature. However, regarding processing language, our human brain may not operate using the same principle. Then, a debate is established on the connection between brain computation and artificial self-supervision adopted in large language models. One of most influential hypothesis in brain computation is the predictive coding framework, which proposes to minimize the prediction error by local learning. However, the role of predictive coding and the associated credit assignment in language processing remains unknown. Here, we propose a mean-field learning model within the predictive coding framework, assuming that the synaptic weight of each connection follows a spike and slab distribution, and only the distribution is trained. This meta predictive learning is successfully validated on classifying handwritten digits where pixels are input to the network in sequence, and on the toy and real language corpus. Our model reveals that most of the connections become deterministic after learning, while the output connections have a higher level of variability. The performance of the resulting network ensemble changes continuously with data load, further improving with more training data, in analogy with the emergent behavior of large language models. Therefore, our model provides a starting point to investigate the physics and biology correspondences of the language processing and the unexpected general intelligence.Comment: 23 pages, 6 figures, codes are available in the main text with the lin

    Accelerated and Deep Expectation Maximization for One-Bit MIMO-OFDM Detection

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    In this paper we study the expectation maximization (EM) technique for one-bit MIMO-OFDM detection (OMOD). Arising from the recent interest in massive MIMO with one-bit analog-to-digital converters, OMOD is a massive-scale problem. EM is an iterative method that can exploit the OFDM structure to process the problem in a per-iteration efficient fashion. In this study we analyze the convergence rate of EM for a class of approximate maximum-likelihood OMOD formulations, or, in a broader sense, a class of problems involving regression from quantized data. We show how the SNR and channel conditions can have an impact on the convergence rate. We do so by making a connection between the EM and the proximal gradient methods in the context of OMOD. This connection also gives us insight to build new accelerated and/or inexact EM schemes. The accelerated scheme has faster convergence in theory, and the inexact scheme provides us with the flexibility to implement EM more efficiently, with convergence guarantee. Furthermore we develop a deep EM algorithm, wherein we take the structure of our inexact EM algorithm and apply deep unfolding to train an efficient structured deep net. Simulation results show that our accelerated exact/inexact EM algorithms run much faster than their standard EM counterparts, and that the deep EM algorithm gives promising detection and runtime performances

    Contactless Haptic Display Through Magnetic Field Control

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    Haptic rendering enables people to touch, perceive, and manipulate virtual objects in a virtual environment. Using six cascaded identical hollow disk electromagnets and a small permanent magnet attached to an operator's finger, this paper proposes and develops an untethered haptic interface through magnetic field control. The concentric hole inside the six cascaded electromagnets provides the workspace, where the 3D position of the permanent magnet is tracked with a Microsoft Kinect sensor. The driving currents of six cascaded electromagnets are calculated in real-time for generating the desired magnetic force. Offline data from an FEA (finite element analysis) based simulation, determines the relationship between the magnetic force, the driving currents, and the position of the permanent magnet. A set of experiments including the virtual object recognition experiment, the virtual surface identification experiment, and the user perception evaluation experiment were conducted to demonstrate the proposed system, where Microsoft HoloLens holographic glasses are used for visual rendering. The proposed magnetic haptic display leads to an untethered and non-contact interface for natural haptic rendering applications, which overcomes the constraints of mechanical linkages in tool-based traditional haptic devices

    Intergeneric transfer of ribosomal genes between two fungi

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    <p>Abstract</p> <p>Background</p> <p>Horizontal gene transfer, also called lateral gene transfer, frequently occurs among prokaryotic organisms, and is considered an important force in their evolution. However, there are relatively few reports of transfer to or from fungi, with some notable exceptions in the acquisition of prokaryotic genes. Some fungal species have been found to contain sequences resembling those of bacterial genes, and with such sequences absent in other fungal species, this has been interpreted as horizontal gene transfer. Similarly, a few fungi have been found to contain genes absent in close relatives but present in more distantly related taxa, and horizontal gene transfer has been invoked as a parsimonious explanation. There is a paucity of direct experimental evidence demonstrating the occurrence of horizontal gene transfer in fungi.</p> <p>Results</p> <p>We found a fungal field isolate from rice (<it>Oryzae sativa</it>) that contains ribosomal DNA sequences from two species of fungal rice pathogens (<it>Thanatephorus cucumeris </it>and <it>Ceratobasidium oryzae-sativae</it>). This field isolate has four types of ribosomal DNA internal transcribed spacers (ITS), namely pure ITS of <it>C. oryzae-sativae</it>, which was dominant in this field isolate, pure ITS of <it>T. cucumeris</it>, and two chimeric ITS, with ITS1 derived from <it>C. oryzae-sativae </it>and ITS2 from <it>T. cucumeris</it>, or ITS1 from <it>T. cucumeri</it>s and ITS2 from <it>C. oryzae-sativae</it>. The presence of chimeric forms indicates that the intergeneric hybrid was not merely composed of nuclei from the parental species, but that nuclear fusion and crossing over had taken place.</p> <p>Conclusion</p> <p>Hyphae of <it>T. cucumeris </it>and <it>C. oryzae-sativae </it>are vegetatively incompatible, and do not successfully anastomose. However, they parasitize the same host, and perhaps under the influence of host enzymes targeted to weaken pathogen cells or in dying host plant tissue, the fungal hyphae lost their integrity, and normal vegetative incompatibility mechanisms were overcome, allowing the hyphae to fuse. Based on the presence of other similarly anomalous isolates from the field, we speculate that these types of intergeneric hybridization events and occurrences of horizontal gene transfer may not be so rare in the field.</p

    Ferroptosis in hematological malignant tumors

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    Ferroptosis is a kind of iron-dependent programmed cell death discovered in recent years. Its main feature is the accumulation of lipid reactive oxygen species in cells, eventually leading to oxidative stress and cell death. It plays a pivotal role in normal physical conditions and the occurrence and development of various diseases. Studies have shown that tumor cells of the blood system, such as leukemia cells and lymphoma cells, are sensitive to the response to ferroptosis. Regulators that modulate the Ferroptosis pathway can accelerate or inhibit tumor disease progression. This article reviews the mechanism of ferroptosis and its research status in hematological malignancies. Understanding the mechanisms of ferroptosis could provide practical guidance for treating and preventing these dreaded diseases

    Comparison of the Differential Diagnostic Performance of Intravoxel Incoherent Motion Imaging and Diffusion Kurtosis Imaging in Malignant and Benign Thyroid Nodules

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    ObjectiveThis study aimed to compare the diagnostic capacity between IVIM and DKI in differentiating malignant from benign thyroid nodules.Material and MethodsThis study is based on magnetic resonance imaging data of the thyroid with histopathology as the reference standard. Spearman analysis was used to assess the relationship of IVIM-derived parameters D, f, D* and the DKI-derived parameters Dapp and Kapp. The parameters of IVIM and DKI were compared between the malignant and benign groups. Binary logistic regression analysis was performed to establish the diagnostic model, and receiver operating characteristic (ROC) curve analysis was subsequently performed. The DeLong test was used to compare the diagnostic effectiveness of different prediction models. Spearman analysis was used to assess the relationship of Ki-67 expression and parameters of IVIM and DKI.ResultsAmong the 93 nodules, 46 nodules were malignant, and 47 nodules were benign. The Dapp of DKI-derived parameter was related to the D (P &lt; 0.001, r = 0.863) of IVIM-derived parameter. The Kapp of DKI-derived parameter was related to the D (P &lt; 0.001, r = -0.831) of IVIM-derived parameters. The malignant group had a significantly lower D value (P &lt; 0.001) and f value (P = 0.013) than the benign group. The malignant group had significantly higher Kapp and lower Dapp values (all P &lt; 0.001). The D+f had an area under the curve (AUC) of 0.951. The Dapp+Kapp had an AUC of 0.943. The D+f+Dapp+Kapp had an AUC of 0.954. The DeLong test showed no statistical significance among there prediction models. The D (P = 0.007) of IVIM-derived parameters and Dapp (P = 0.045) of DKI-derived parameter were correlated to the Ki-67 expression.ConclusionsIVIM and DKI were alternative for each other in in differentiating malignant from benign thyroid nodules

    Alterations in cellular metabolisms after TKI therapy for Philadelphia chromosome-positive leukemia in children: A review

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    Incidence rates of chronic myeloid leukemia (CML) and Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukemia (ALL) are lower but more aggressive in children than in adults due to different biological and host factors. After the clinical application of tyrosine kinase inhibitor (TKI) blocking BCR/ABL kinase activity, the prognosis of children with CML and Ph+ ALL has improved dramatically. Yet, off-target effects and drug tolerance will occur during the TKI treatments, contributing to treatment failure. In addition, compared to adults, children may need a longer course of TKIs therapy, causing detrimental effects on growth and development. In recent years, accumulating evidence indicates that drug resistance and side effects during TKI treatment may result from the cellular metabolism alterations. In this review, we provide a detailed summary of the current knowledge on alterations in metabolic pathways including glucose metabolism, lipid metabolism, amino acid metabolism, and other metabolic processes. In order to obtain better TKI treatment outcomes and avoid side effects, it is essential to understand how the TKIs affect cellular metabolism. Hence, we also discuss the relevance of cellular metabolism in TKIs therapy to provide ideas for better use of TKIs in clinical practice
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