The conventional approach to scalp inspection in the hairdressing industry relies on manually interpreting scalp symptom images. Hairdressers provide treatments based on visual assessment, leading to potential inaccur...
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Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but th...
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Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.
In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of *** studies focus on optimizing base station deployment under t...
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In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of *** studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind ***,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic ***,research in this area still needs to be *** paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this *** algorithm considers the dynamic alterations in obstacle locations within the designated *** determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage *** experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle *** results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles *** 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage.
Living cells can rapidly adjust their metabolic activities in response to external stimuli,leading to fluctuations in intracellular temperature and reactive oxygen species(ROS)*** these parameters is essential for und...
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Living cells can rapidly adjust their metabolic activities in response to external stimuli,leading to fluctuations in intracellular temperature and reactive oxygen species(ROS)*** these parameters is essential for understanding cellular metabolism,particularly during dynamic biological *** this study,we present a bifunctional nanoprobe capable of simultaneous measurement of ROS levels and temperature within single *** nanoprobe features two individually addressable nanoelectrodes,with platinum(Pt)and nickel(Ni)coatings on both *** the tip,these two metal layers form a nano-thermocouple,enabling precise intracellular temperature measurements,while the Pt layer facilitates selective ROS *** dual functionality allows for real-time monitoring of cellular responses during synergistic chemo-photothermal therapy of cancer cells and zebrafish embryos subjected to mitochondrial toxic *** results demonstrate that the nanoprobe effectively measures increases in temperature and ROS levels in HeLa cells undergoing chemo-photothermal therapy,as well as in chemically stimulated zebrafish *** providing detailed analysis of submicrometer-scale temperature and ROS variations within living cells,this nanoprobe offers valuable insights into cellular processes and holds promise for early disease detection and drug development.
Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encounte...
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Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic *** study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie *** allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language *** adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better *** distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for *** proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and *** SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both *** indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English *** study helps deepen the understanding of sentiments across various linguistic *** many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.
Photoacoustic imaging,an acoustic imaging modality with potentially optical resolution in an optical turbid medium,has attracted great ***,the convergence of wavefront optimization and raster scanning in computational...
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Photoacoustic imaging,an acoustic imaging modality with potentially optical resolution in an optical turbid medium,has attracted great ***,the convergence of wavefront optimization and raster scanning in computational photoacoustic imaging leads to the challenge of fast mapping,especially for a spatial resolution approaching the acoustic deep-subwavelength *** a sparse sampling paradigm,compressive sensing has been applied in numerous fields to accelerate data acquisition without significant quality *** this work,we propose a dual-compressed approach for photoacoustic surface tomography that enables high-efficiency imaging with 3D spatial resolution unlimited by the acoustics in a turbid *** dual-compressed photoacoustic imaging with single-pixel detection,enabled by spatially optical modulation with synchronized temporally photoacoustic coding,allows decoding of the fine optical information from the modulated acoustic signal even when the variance of original photoacoustic signals is *** perform a proof-of-principle numerical demonstration of dual-compressed photoacoustic imaging, that resolves acoustic sub-acoustic-wavelength details with a significantly reduced number of measurements,revealing the potential for dynamic *** dual-compressed concept,which transforms unobtrusive spatial difference into spatio-temporal detectable information,can be generalized to other imaging modalities to re alize efficient,high-spatial-resolution imaging.
Very recently, a memory-efficient version (called MeZO) of simultaneous perturbation stochastic approximation (SPSA), one well-established zeroth-order optimizer from the automatic control community, has shown competi...
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Android applications are becoming increasingly powerful in recent years. While their functionality is still of paramount importance to users, the energy efficiency of these applications is also gaining more and more a...
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Android applications are becoming increasingly powerful in recent years. While their functionality is still of paramount importance to users, the energy efficiency of these applications is also gaining more and more attention. Researchers have discovered various types of energy defects in Android applications, which could quickly drain the battery power of mobile devices. Such defects not only cause inconvenience to users, but also frustrate Android developers as diagnosing the energy inefficiency of a software product is a non-trivial task. In this work, we perform a literature review to understand the state of the art of energy inefficiency diagnosis for Android applications. We identified 55 research papers published in recent years and classified existing studies from four different perspectives, including power estimation method, hardware component, types of energy defects, and program analysis approach. We also did a cross-perspective analysis to summarize and compare our studied techniques. We hope that our review can help structure and unify the literature and shed light on future research, as well as drawing developers' attention to build energy-efficient Android applications.
In recent years, mental health issues have profoundly impacted individuals’ well-being, necessitating prompt identification and intervention. Existing approaches grapple with the complex nature of mental health, faci...
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In recent years, mental health issues have profoundly impacted individuals’ well-being, necessitating prompt identification and intervention. Existing approaches grapple with the complex nature of mental health, facing challenges like task interference, limited adaptability, and difficulty in capturing nuanced linguistic expressions indicative of various conditions. In response to these challenges, our research presents three novel models employing multi-task learning (MTL) to understand mental health behaviors comprehensively. These models encompass soft-parameter sharing-based long short-term memory with attention mechanism (SPS-LSTM-AM), SPS-based bidirectional gated neural networks with self-head attention mechanism (SPS-BiGRU-SAM), and SPS-based bidirectional neural network with multi-head attention mechanism (SPS-BNN-MHAM). Our models address diverse tasks, including detecting disorders such as bipolar disorder, insomnia, obsessive-compulsive disorder, and panic in psychiatric texts, alongside classifying suicide or non-suicide-related texts on social media as auxiliary tasks. Emotion detection in suicide notes, covering emotions of abuse, blame, and sorrow, serves as the main task. We observe significant performance enhancement in the primary task by incorporating auxiliary tasks. Advanced encoder-building techniques, including auto-regressive-based permutation and enhanced permutation language modeling, are recommended for effectively capturing mental health contexts’ subtleties, semantic nuances, and syntactic structures. We present the shared feature extractor called shared auto-regressive for language modeling (S-ARLM) to capture high-level representations that are useful across tasks. Additionally, we recommend soft-parameter sharing (SPS) subtypes-fully sharing, partial sharing, and independent layer-to minimize tight coupling and enhance adaptability. Our models exhibit outstanding performance across various datasets, achieving accuracies of 96.9%, 97.
Cavity-assisted atom interferometry has been proposed for gravity measurement in the past decade due to its promising advantages such as low laser power consumption, high beam quality, and strong atom-photon interacti...
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Cavity-assisted atom interferometry has been proposed for gravity measurement in the past decade due to its promising advantages such as low laser power consumption, high beam quality, and strong atom-photon interactions. However, the previous cavity-related works have difficulties measuring the absolute gravitational acceleration g due to their limited cavity design and locking methods. We experimentally demonstrate a Raman-type absolute atom gravimeter assisted by a newly designed degenerate optical cavity. The gravimeter achieves a decent sensitivity of about 28.5(9)×10−8gHz−1/2 under our noisy laboratory conditions and without magnetic shielding of the atom interference area. The accuracy reaches 20 µGal within half an hour of continuous measurement. We also systematically measure and analyze the main noises affecting the g-measure sensitivity, indicating no physical obstacles to achieving higher sensitivity. Our cavity scheme is compatible and can be easily integrated with current free-space atom interferometers. By fully utilizing the cavity's advantages, it will benefit many areas in future fundamental research and fieldable applications.
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