Image-text retrieval aims to capture the semantic correspondence between images and texts,which serves as a foundation and crucial component in multi-modal recommendations,search systems,and online *** mainstream meth...
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Image-text retrieval aims to capture the semantic correspondence between images and texts,which serves as a foundation and crucial component in multi-modal recommendations,search systems,and online *** mainstream methods primarily focus on modeling the association of image-text pairs while neglecting the advantageous impact of multi-task learning on image-text *** this end,a multi-task visual semantic embedding network(MVSEN)is proposed for image-text ***,we design two auxiliary tasks,including text-text matching and multi-label classification,for semantic constraints to improve the generalization and robustness of visual semantic embedding from a training ***,we present an intra-and inter-modality interaction scheme to learn discriminative visual and textual feature representations by facilitating information flow within and between ***,we utilize multi-layer graph convolutional networks in a cascading manner to infer the correlation of image-text *** results show that MVSEN outperforms state-of-the-art methods on two publicly available datasets,Flickr30K and MSCOCO,with rSum improvements of 8.2%and 3.0%,respectively.
In modern terminology,“organoids”refer to cells that grow in a specific three-dimensional(3D)environment in vitro,sharing similar structures with their source organs or *** themorphology or growth characteristics of...
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In modern terminology,“organoids”refer to cells that grow in a specific three-dimensional(3D)environment in vitro,sharing similar structures with their source organs or *** themorphology or growth characteristics of organoids through a microscope is a commonly used method of organoid ***,it is difficult,time-consuming,and inaccurate to screen and analyze organoids only manually,a problem which cannot be easily solved with traditional *** intelligence(AI)technology has proven to be effective in many biological and medical research fields,especially in the analysis of single-cell or hematoxylin/eosin stained tissue *** used to analyze organoids,AI should also provide more efficient,quantitative,accurate,and fast *** this review,we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis ***,we will summarize the development from machine learning to deep learning and the advantages of the latter,and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and ***,we will discuss the limitations of current AI used in organoid research,as well as opportunities and future research directions.
As a fundamental thermodynamic variable, pressure can alter the bonding patterns and drive phase transitions leading to the creation of new high-pressure phases with exotic properties that are inaccessible at ambient ...
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As a fundamental thermodynamic variable, pressure can alter the bonding patterns and drive phase transitions leading to the creation of new high-pressure phases with exotic properties that are inaccessible at ambient pressure. Using the swarm intelligence structural prediction method, the phase transition of TiF_(3), from R-3c to the Pnma phase, was predicted at high pressure, accompanied by the destruction of TiF_6 octahedra and formation of TiF_8 square antiprismatic units. The Pnma phase of TiF_(3), formed using the laser-heated diamond-anvil-cell technique was confirmed via high-pressure x-ray diffraction experiments. Furthermore, the in situ electrical measurements indicate that the newly found Pnma phase has a semiconducting character, which is also consistent with the electronic band structure calculations. Finally, it was shown that this pressure-induced phase transition is a general phenomenon in ScF_(3), VF_(3), CrF_(3), and MnF_(3), offering valuable insights into the high-pressure phases of transition metal trifluorides.
A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing *** method achieves precise...
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A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing *** method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable *** the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 ***,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent *** the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 *** mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.
Secure k-Nearest Neighbor(k-NN)query aims to find k nearest data of a given query from an encrypted database in a cloud server without revealing privacy to the untrusted cloud and has wide applications in many areas,s...
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Secure k-Nearest Neighbor(k-NN)query aims to find k nearest data of a given query from an encrypted database in a cloud server without revealing privacy to the untrusted cloud and has wide applications in many areas,such as privacy-preservingmachine elearning gand secure biometric *** solutions have been put forward to solve this challenging ***,the existing schemes still suffer from various limitations in terms of efficiency and *** this paper,we propose a new encrypt-then-index strategy for the secure k-NN query,which can simultaneously achieve sub-linear search complexity(efficiency)and support dynamical update over the encrypted database(flexibility).Specifically,we propose a novel algorithm to transform the encrypted database and encrypted query points in the *** indexing the transformed database using spatial data structures such as the R-tree index,our strategy enables sub-linear complexity for secure k-NN queries and allows users to dynamically update the encrypted *** the best of our knowledge,the proposed strategy is the first to simultaneously provide these two *** theoretical analysis and extensive experiments,we formally prove the security and demonstrate the efficiency of our scheme.
Self-adaptive applications are becoming increasingly attractive, with the ability to smartly understand their runtime environments (or contexts) and deliver adaptive services, for example, location-aware navigation or...
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Self-adaptive applications are becoming increasingly attractive, with the ability to smartly understand their runtime environments (or contexts) and deliver adaptive services, for example, location-aware navigation or resource-sensitive suggestions. However, due to inherent noises in the process of sensing and interpreting environmental information, there is a growing demand for guarding the consistency of collected contexts to avoid application misbehaviour and, at the same time, minimize extra costs. Existing work attempted to achieve this by speeding up the kernel constraint checking module inside the consistency guarding process. Most of these efforts were spent on reusing previous checking results or parallelizing the checking process, but they all leave one central step of constraint checking, that is, link generation, untouched. In this step, the checking engine provides reasons to explain the violation of constraints under check. It occupies a substantial part of the total time cost. Focusing on this key link generation step, we proposed MG, which deploys a rigourous analysis to automatically identify and avoid redundancy in the link generation without harming any correctness of the checking results. MG has been proven sound (always guaranteeing correctness) and complete (entirely removing redundancy). Moreover, based on our observation that MG's redundancy elimination also assists another core step of constraint checking to reduce unnecessary computation further, we additionally enhance MG with an escape-condition optimization to escape unnecessary evaluation of truth values to further improve the efficiency of constraint checking in an aspect other than link generation. We call it MG+ for distinguishing. Our experiments with synthesized and real-world consistency constraints reported that, compared with existing work, MG eliminates all link redundancy (83% to 0%), and based on it, MG+ further reduces significant truth value calculations (e.g., 49.74% reduc
Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature o...
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1 Introduction Boneh et al.[1]first systematically introduce the concept of Functional Encryption(FE)which overcomes all-or-nothing limitation of traditional public key ***,FE hides a potential problem:even if the sec...
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1 Introduction Boneh et al.[1]first systematically introduce the concept of Functional Encryption(FE)which overcomes all-or-nothing limitation of traditional public key ***,FE hides a potential problem:even if the secret key is obtained legitimately,once it is released,it can be used *** leads to that the secret key may leak information *** deal with this problem and make FE more practical,Abdalla et al.[2]recently constructed inner-product FE(IPFE)with fine-grained access control achieving strong security guarantees under standard assumptions.
Decentralized machine learning has attracted extensive research interest in recent *** to its centralized counterpart that relies on a server to collect and disseminate messages,decentralized machine learning relies o...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
Decentralized machine learning has attracted extensive research interest in recent *** to its centralized counterpart that relies on a server to collect and disseminate messages,decentralized machine learning relies on message passing among neighboring nodes,and thus avoids the communication bottleneck of the ***,most of the existing decentralized machine learning algorithms operate within data centers that are equipped with multiple CPUs and/or GPUs,assuming that the underlying communication networks are *** this paper,we implement decentralized parallel stochastic gradient descent(D-PSGD) over multiple computers connected with the Internet which is relatively *** cope with the inevitable packet losses caused by the Internet environment,we modify D-PSGD by allowing it to use stale *** experiments confirm the performance gain of decentralized machine learning over its centralized counterpart in terms of runtime,as well as the robustness of the modified D-PSGD to packet losses.
Network traffic anomaly detection plays a crucial role in today's network security and performance management. In response to the challenges in current network traffic data processing, such as insufficient structu...
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