De novo peptide sequencing of tandem mass spec-trometry (MS/MS) has emerged as an important technology for peptide sequencing in proteomics. To increase the accuracy and practicality of de novo sequencing, some previous algorithms used multiple spectra to identify the peptide sequence. In this approach, we focus on de novo sequencing of multiple SILAC-based tandem mass spectra. SILAC technology uses medium containing different kinds of isotope-labeled essential amino acids, usually Arginine(R) and Lysine(K), to label newly synthesized proteins with stable isotopes during cell growth. Multiple MS/MS spectra for the same peptide sequence are produced by spectrometry after the SILAC samples are processed by LC-MS/MS shotgun proteomics. Based on the factors such as the type of isotope labeling, precursor ion mass, etc., multiple spectra with different type of SILAC PTMs for the same peptide can be used to identify the peptide sequence. In this paper, we present two de novo sequencing algorithms to compute the peptide sequence which are based on total number of SILAC modifications and based on the numbers of SILAC Arginine(R) and Lysine(K).
The application of millimeter-wave(mmWave) radar in human activity recognition has attracted significant attention because of its insensitivity to ambient lighting and privacy concerns. Millimeter-wave radar will be transformed into a point cloud as an input. This paper proposes a system that dynamically adjusts DBSCAN to handle point cloud noise. It consists of four main components: random forest to determine minPts, KNN to compute Eps, Cluster Merging, and Noise Re-judgment to optimize the point cloud clustering problem in human activity recognition. The proposed new method makes the dynamic selection of the two parameters minPts and Eps of DBSCAN, as well as the design of a new clustering method for human point cloud clustering based on the property of local sparsity of point cloud of the human body taken by radar. We build a dataset based on a millimeter-wave radar of 10 volunteers. Based on this dataset using the Rand Index and Purity assessment, our proposed method has higher accuracy than other methods, reaching average accuracy of 88.52% and 84.86%, respectively, almost 18% higher than other methods.
The capability to infer emotional insights from emojis found in social media has projected emoji analysis into the spotlight of current emoji-based research. Previous studies mainly used text-surrounding emojis to estimate sentimentality scores. However, trying to conclude the same score based solely on emojis is challenging. In this paper this challenge was welcomed, and with it we created a new concept. This revolutionary scoring method, named the EmojiSets Sentiment Score Rank, proposes using sets of emojis taken from tweets along with information from previous studies [1] to find a sentiment score. This bottom-up scoring approach gives each emoji a sentiment score. It then calculates the context-level sentiment score of a tweet solely dependent on the emojis found within it. To the best of the authors' knowledge, no such approach has been researched in the Emojis Sentiment Analysis area. We tested our model against over 1.2 million tweets concerning Covid-19 and compared it to the VADER model [7] to validate our assumption. Our model corrected around 72% of the tweets that the other model scored as neutral. To succor these findings, 32 human annotators were given the task of annotating 8040 randomly chosen tweets. When calculating similarity using the Jaccard Index, their results were consistent with our approach in over 70% of cases
Thanks to creative rendering and display techniques, 360-degree images can provide a more immersive and interactive experience for streaming users. However, such features make the perceptual characteristics of 360-degree images more complex than those of fixed-view images, making it impossible to achieve a comprehensive and accurate image quality assessment (IQA) task using a simple stack of pre-processing, post-processing, compression, and rendering tasks. In order to thoroughly learn global and local features in 360-degree images, reduce the complexity of multichannel neural network models and simplify the training process, this paper proposes a user-aware joint architecture and an efficient converter dedicated to 360-degree no-reference (NR) IQA. The input of the proposed method is a 360-degree cubic mapping projection (CMP) image. In addition, the proposed 360-degree NRIQA method includes a non-overlapping self-attentive selection module based on a dominant map and a feature extraction module based on a U-shaped transformer (U-former) to address perceptual region significance and projection distortion. The transformer-based architecture and the weighted averaging technique are jointly used to predict local perceptual quality. Experimental results obtained on widely used databases show that the proposed model outperforms other state-of-the-art methods in the case of NR 360-degree image quality assessment. In addition, cross-database evaluation and ablation studies demonstrate the intrinsic robustness and generalization of the proposed model.
It is widely believed that progress towards advanced artificial intelligence (AI) systems will only occur when brain-inspired information processing systems are available to guide autonomous behaviour. The development of such autonomous systems (AS) requires cognitive entities capable of acquiring information, learning, planning, and reasoning that eventually adapt to environmental uncertainties. Cognitive dynamic systems (CDS) provide an engineering tool to design AS. Although the literature addresses various aspects of cognitive dynamic systems, it needs a holistic methodology to analyze alternative techniques concerning the driving module that internally guides the system toward a goal. Since uncertain events far from expectation play a significant role in triggering information-seeking behaviours, contextual surprise can be considered an intrinsic motivator in CDS. This presentation highlights connections and similarities among several definitions of contextual surprise. For demonstration purposes, it reviews the design of a linear Gaussian CDS as a motivating example. It uses a contextual surprise minimization scheme to express information utility and guide CDS’ state estimation and control.
