Partial label learning aims to learn a multi-class classifier,where each training example corresponds to a set of candidate labels among which only one is *** studies in the label space have only focused on the differ...
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Partial label learning aims to learn a multi-class classifier,where each training example corresponds to a set of candidate labels among which only one is *** studies in the label space have only focused on the difference between candidate labels and non-candidate *** far,however,there has been little discussion about the label correlation in the partial label *** paper begins with a research on the label correlation,followed by the establishment of a unified framework that integrates the label correlation,the adaptive graph,and the semantic difference maximization *** work generates fresh insight into the acquisition of the learning information from the label ***,the label correlation is calculated from the candidate label set and is utilized to obtain the similarity of each pair of instances in the label *** that,the labeling confidence for each instance is updated by the smoothness assumption that two instances should be similar outputs in the label space if they are close in the feature *** last,an effective optimization program is utilized to solve the unified *** experiments on artificial and real-world data sets indicate the superiority of our proposed method to state-of-art partial label learning methods.
Activity and motion recognition using Wi-Fi signals,mainly channel state information(CSI),has captured the interest of many researchers in recent *** research studies have achieved splendid results with the help of ma...
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Activity and motion recognition using Wi-Fi signals,mainly channel state information(CSI),has captured the interest of many researchers in recent *** research studies have achieved splendid results with the help of machine learning models from different applications such as healthcare services,sign language translation,security,context awareness,and the internet of ***,most of these adopted studies have some shortcomings in the machine learning algorithms as they rely on recurrence and convolutions and,thus,precluding smooth sequential ***,in this paper,we propose a deep-learning approach based solely on attention,i.e.,the sole Self-Attention Mechanism model(Sole-SAM),for activity and motion recognition using Wi-Fi *** Sole-SAM was deployed to learn the features representing different activities and motions from the raw CSI *** were carried out to evaluate the performance of the proposed Sole-SAM *** experimental results indicated that our proposed system took significantly less time to train than models that rely on recurrence and convolutions like Long Short-Term Memory(LSTM)and Recurrent Neural Network(RNN).Sole-SAM archived a 0.94%accuracy level,which is 0.04%better than RNN and 0.02%better than LSTM.
Biosignal representation learning (BRL) plays a crucial role in emotion recognition for game users (ERGU). Unsupervised BRL has garnered attention considering the difficulty in obtaining ground truth emotion labels fr...
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Biosignal representation learning (BRL) plays a crucial role in emotion recognition for game users (ERGU). Unsupervised BRL has garnered attention considering the difficulty in obtaining ground truth emotion labels from game users. However, unsupervised BRL in ERGU faces challenges, including overfitting caused by limited data and performance degradation due to unbalanced sample distributions. Faced with the above challenges, we propose a novel method of biosignal contrastive representation learning (BCRL) for ERGU, which not only serves as a unified representation learning approach applicable to various modalities of biosignals but also derives generalized biosignals representations suitable for different downstream tasks. Specifically, we solve the overfitting by introducing perturbations at the embedding layer based on the projected gradient descent (PGD) adversarial attacks and develop the sample balancing strategy (SBS) to mitigate the negative impact of the unbalanced sample on the performance. Further, we have conducted comprehensive validation experiments on the public dataset, yielding the following key observations: 1) BCRL outperforms all other methods, achieving average accuracies of 76.67%, 71.83%, and 63.58% in 1D-2C Valence, 1D-2C Arousal and 2D-4C Valence/Arousal, respectively;2) The ablation study shows that both the PGD module (+7.58% in accuracy on average) and the SBS module (+14.60% in accuracy on average) have a positive effect on the performance of different classifications;3) BCRL model exhibits the certain generalization ability across the different games, subjects and classifiers. IEEE
In this article, write-once-read-many-times (WORM) memory behavior of HfZrO (HZO) ferroelectric material is demonstrated. A stoichiometric Hf0.5Zr0.5O2 thin film prepared using a sol-gel process is used as a resistive...
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The industrial Internet of Things (IIoT) is changing our way of life and work. As the number of mobile devices connected to IIoT increases, users will face many security challenges such as insecure communication envir...
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The industrial Internet of Things (IIoT) is changing our way of life and work. As the number of mobile devices connected to IIoT increases, users will face many security challenges such as insecure communication environments. The certificateless signature (CLS) scheme can ensure the integrity and validity of data and provide secure identity authentication for the IIoT, and several pairing-free CLS schemes have been proposed in recent years. However, we find they are vulnerable to the signature forgery attack of malicious key generation centers by safety analysis, which does not realize the security they have claimed. To solve this problem, we show an improved CLS scheme that achieves anonymity and formally proves its security under the hardness of the discrete logarithm problem in the random oracle. Comprehensive performance analysis and comparison show that our scheme has less communication and computation costs with higher security, and our construction is suitable for scenarios with limited resources. Finally, we apply this scheme to construct a mutual identity authentication protocol in the healthcare IIoT environment. 2014 IEEE.
The experimental realization of Rydberg dressing technology in ultracold atomic systems provides another superior platform for studying novel states of matter and macroscopic quantum *** this work,based on the mean-fi...
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The experimental realization of Rydberg dressing technology in ultracold atomic systems provides another superior platform for studying novel states of matter and macroscopic quantum *** this work,based on the mean-field theory,we have investigated the ground-state phases of a two-component Bose–Einstein condensate with Rydberg interaction and confined in a toroidal *** effects of the Rydberg interaction and external potential,especially the Rydberg blockade radius,on the ground-state structure of such a system have been investigated in full parameter *** results show that the Rydberg blockade radius,which can be regarded as another controllable parameter,can be used to obtain a variety of ground-state *** interestingly,it is found that for weak Rydberg interactions,the Rydberg blockade radius breaks the spontaneous rotational symmetry of the system,leading to the formation of a discrete unit cell *** strongly interacting cases,it can be used to realize different orders of discrete rotational symmetry breaking.
Today's deep learning models face an increasing demand to handle dynamic shape tensors and computation whose shape information remains unknown at compile time and varies in a nearly infinite range at runtime. This...
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Today's deep learning models face an increasing demand to handle dynamic shape tensors and computation whose shape information remains unknown at compile time and varies in a nearly infinite range at runtime. This shape dynamism brings tremendous challenges for existing compilation pipelines designed for static models which optimize tensor programs relying on exact shape values. This paper presents TSCompiler, an end-to-end compilation framework for dynamic shape models. TSCompiler first proposes a symbolic shape propagation algorithm to recover symbolic shape information at compile time to enable subsequent optimizations. TSCompiler then partitions the shape-annotated computation graph into multiple subgraphs and fine-tunes the backbone operators from the subgraph within a hardware-aligned search space to find a collection of high-performance schedules. TSCompiler can propagate the explored backbone schedule to other fusion groups within the same subgraph to generate a set of parameterized tensor programs for fused cases based on dependence analysis. At runtime, TSCompiler utilizes an occupancy-targeted cost model to select from pre-compiled tensor programs for varied tensor shapes. Extensive evaluations show that TSCompiler can achieve state-of-the-art speedups for dynamic shape models. For example, we can improve kernel efficiency by up to 3.97× on NVIDIA RTX3090, and 10.30× on NVIDIA A100 and achieve up to five orders of magnitude speedups on end-to-end latency.
An image encryption algorithm is proposed in this paper based on a new four-dimensional hyperchaotic system,a neural mechanism,a Galois field and an improved Feistel block structure,which improves the efficiency and e...
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An image encryption algorithm is proposed in this paper based on a new four-dimensional hyperchaotic system,a neural mechanism,a Galois field and an improved Feistel block structure,which improves the efficiency and enhances the security of the encryption ***,a four-dimensional hyperchaotic system with a large key space and chaotic dynamics performance is proposed and combined with a cloud model,in which a more complex and random sequence is constructed as the key stream,and the problem of chaotic periodicity is ***,the key stream is combined with the neural mechanism,Galois field and improved Feistel block structure to scramble and diffuse the image ***,the experimental results and security analysis show that the encryption algorithm has a good encryption effect and high encryption efficiency,is secure,and can meet the requirements of practical applications.
Computational fluid dynamics was used and a numerical simulation analysis of boiling heat transfer in microchannels with three depths and three cross-sectional profiles was *** heat transfer coefficient and bubble gen...
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Computational fluid dynamics was used and a numerical simulation analysis of boiling heat transfer in microchannels with three depths and three cross-sectional profiles was *** heat transfer coefficient and bubble generation process of three microchannel structures with a width of 80μm and a depth of 40,60,and 80μm were compared during the boiling process,and the factors influencing bubble generation were studied.A visual test bench was built,and test substrates of different sizes were prepared using a micro-nano *** the test,the behavior characteristics of the bubbles on the boiling surface and the temperature change of the heated wall were collected with a high-speed camera and a temperature *** was found that the microchannel with a depth of 80μm had the largest heat transfer coefficient and shortest bubble growth period,the rectangular channel had a larger peak heat transfer coefficient and a lower frequency of bubble occurrence,while the V-shaped channel had the shortest growth period,i.e.,the highest frequency of bubble occurrence,but its heat transfer coefficient was smaller than that of the rectangular channel.
This study aimed to investigate the mechanism of mineral spontaneous combustion in an open pit. On the study of coal and mineral mixture in open pit mines, as well as through the specifc surface area and Search Engine...
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This study aimed to investigate the mechanism of mineral spontaneous combustion in an open pit. On the study of coal and mineral mixture in open pit mines, as well as through the specifc surface area and Search Engine Marketing (SEM) experiments, the specifc surface area and aperture characteristics of distribution of open pit coal sample and pit mineral mixture samples were analyzed. Thermal analysis experiments were used to divide the oxidation process was divided into three stages, and the thermal behavior characteristics of experimental samples were characterized. On the basis of the stage division, we explored the transfer law of the key active functional groups of the experimental samples. The apparent activation energy calculation of the key active groups, performed by combining the Achar diferential method with the Coats–Redfern integral method, microstructural and oxidation kinetic properties were revealed. The resulted showed that the mixed sample had high ash, the fxed carbon content was reduced, the specifc surface area was far lower than the raw coal, the large aperture distribution was slightly higher than the medium hole, the micropore was exceptionally low, the gas adsorption capacity was weaker than the raw coal, the pit coal sample had the exceedingly more active functional groups, easy to react with oxygen, more likely to occur naturally, and its harm was relatively large. The mixed sample contained the highest C–O–C functional group absorbance. The functional groups were mainly infuenced by the self-OH content, alkyl side chain, and fatty hydrocarbon in the sample. The main functional groups of the four-like mixture had the highest apparent activation energy, and the two reactions were higher in the low-temperature oxidation phase.
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