J-aggregation and H-aggregation are identified as two classical models of function-ally oriented non-covalent interactions,and significant attention has been drawn by ***,due to the scarcity of single-crystal examples...
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J-aggregation and H-aggregation are identified as two classical models of function-ally oriented non-covalent interactions,and significant attention has been drawn by ***,due to the scarcity of single-crystal examples of H-aggregation,a comprehensive understanding of the relationship between its stacking mode and optical behaviour has been *** recent studies,two polyaromatic Schiff base compounds,Cl-Salmphen and H-Salmphen,were successfully synthe-sized,and both were found to exhibit *** thefindings,H-Salmphen was shown to display typical C─H···πinteractions,characteristic of Aggregation-Induced Emission(AIE)active molecules,whereas its halogenated counterpart was identified as behaving similar to Aggregation-Caused Quenching(ACQ)active *** types of results suggest that identical intermolecular interactions can produce differing optical *** was shed,at least in part,on the for-mation mechanisms of H-type aggregates and their luminescence properties from these ***,the high optical signal-to-noise ratio inherent to H-aggregates was utilized for the exploration of water content *** an outcome,a high-performancefluorescentfilter paper was developed,enabling easy real-time detection using a smartphone.
Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern *** detection systems often struggle to mitigate such attacks in convention...
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Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern *** detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)*** Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent *** this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN *** model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant *** adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack *** proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble *** proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in *** provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving *** comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing *** results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.
This paper introduces LLDif, a novel diffusion-based facial expression recognition (FER) framework tailored for extremely low-light (LL) environments. Images captured under such conditions often suffer from low bright...
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Total ionizing dose(TID) radiation response of the custom bandgap voltage reference(BGR)fabricated with 65 nm, 40 nm and 28 nm commercial bulk CMOS technologies is investigated. TID response is assessed employing Co-6...
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Total ionizing dose(TID) radiation response of the custom bandgap voltage reference(BGR)fabricated with 65 nm, 40 nm and 28 nm commercial bulk CMOS technologies is investigated. TID response is assessed employing Co-60 gamma ray source. The measurements indicate that the voltage reference is reduced by5.67% in 28 nm, 0.56% in 40 nm and increased by 1.28% in65 nm devices under irradiation up to 1.2 Mrad(Si) *** 48 hours of annealing, the voltage reference changes are just-1.84% in 28 nm, 0.14% in 40 nm and 1.14% in 65nm. The obtained results demonstrate that the custom BGR has naturally superior TID response due to the circuit design margins.
In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its **...
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In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its *** current WMC solvers work on Conjunctive Normal Form(CNF)***,CNF is not a natural representation for human-being in many *** by the stronger expressive power of Pseudo-Boolean(PB)formulas than CNF,we propose to perform WMC on PB *** on a recent dynamic programming algorithm framework called ADDMC for WMC,we implement a weighted PB counting tool *** compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD,ExactMC,D4,and ADDMC,where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF *** experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas.
Traffic flow prediction, an integral part of intelligent traffic systems and traffic planning, remains a significant challenge due to the nonlinear nature of time-series traffic flow data. Addressing this challenge, w...
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The density peak clustering algorithm shows good clustering performance by rapidly determining each cluster division with a high-density region as the kernel. However, the cut-off distance (dc) as the only parameter o...
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Anomalies in packet length sequences caused by network topology structure and congestion greatly impact the performance of early network traffic classification. Additionally, insufficient differentiation of packet len...
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In multi-label learning(MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. When facing such a challenge, a more practical and cheaper alternat...
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In multi-label learning(MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. When facing such a challenge, a more practical and cheaper alternative should be single positive multi-label learning(SPMLL), where only one positive label needs to be provided per sample. Existing SPMLL methods usually assume unknown labels as negatives, which inevitably introduces false negatives as noisy labels. More seriously, binary cross entropy(BCE) loss is often used for training, which is notoriously not robust to noisy labels. To mitigate this issue, we customize an objective function for SPMLL by pushing only one pair of labels apart each time to suppress the domination of negative labels, which is the main culprit of fitting noisy labels in SPMLL. To further combat such noisy labels, we explore the high-rankness of the label matrix, which can also push apart different labels. By directly extending from SPMLL to MLL with full labels, a unified loss applicable to both settings is derived. As a byproduct, the proposed loss can alleviate the imbalance inherent in MLL. Experiments on real datasets demonstrate that the proposed loss not only performs more robustly to noisy labels for SPMLL but also works well for full labels. Besides, we empirically discover that high-rankness can mitigate the dramatic performance drop in SPMLL. Most surprisingly, even without any regularization or fine-tuned label correction, only adopting our loss defeats state-of-the-art SPMLL methods on CUB, a dataset that severely lacks labels.
We introduce a novel method using a new generative model that automatically learns effective representations of the target and background appearance to detect,segment and track each instance in a video *** from curren...
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We introduce a novel method using a new generative model that automatically learns effective representations of the target and background appearance to detect,segment and track each instance in a video *** from current discriminative tracking-by-detection solutions,our proposed hierarchical structural embedding learning can predict more highquality masks with accurate boundary details over spatio-temporal space via the normalizing *** formulate the instance inference procedure as a hierarchical spatio-temporal embedded learning across time and *** the video clip,our method first coarsely locates pixels belonging to a particular instance with Gaussian distribution and then builds a novel mixing distribution to promote the instance boundary by fusing hierarchical appearance embedding information in a coarse-to-fine *** the mixing distribution,we utilize a factorization condition normalized flow fashion to estimate the distribution parameters to improve the segmentation *** qualitative,quantitative,and ablation experiments are performed on three representative video instance segmentation benchmarks(i.e.,YouTube-VIS19,YouTube-VIS21,and OVIS)and the effectiveness of the proposed method is *** impressively,the superior performance of our model on an unsupervised video object segmentation dataset(i.e.,DAVIS19)proves its *** algorithm implementations are publicly available at https://***/zyqin19/HEVis.
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