GA-Novo: De Novo Peptide Sequencing via Tandem Mass Spectrometry using Genetic Algorithm

Proteomics is the large-scale analysis of the proteins. The common method for identifying proteins and characterising their amino acid sequences is to digest the proteins into peptides, analyse the peptides using mass spectrometry and assign the resulting tandem mass spectra (MS/MS) to peptides using database search tools. However, database search algorithms are highly dependent on a reference protein database and they cannot identify peptides and proteins not included in the database. Therefore, de novo sequencing algorithms are developed to overcome the problem by directly reconstructing the peptide sequence of an MS/MS spectrum without using any protein database. Current de novo sequencing algorithms often fail to construct the completely matched sequences, and produce partial matches. In this study, we propose a genetic algorithm based method, GA-Novo, to solve the complex optimisation task of de novo peptide sequencing, aiming at constructing full length sequences. Given an MS/MS spectrum, GA-Novo optimises the amino acid sequences to best fit the input spectrum. On the testing dataset, GA-Novo outperforms PEAKS, the most commonly used software for this task, by constructing 8% higher number of fully matched peptide sequences, and 4% higher recall at partially matched sequences

Towards training-free refinement for semantic indexing of visual media

Indexing of visual media based on content analysis has now moved beyond using individual concept detectors and there is now a fo- cus on combining concepts or post-processing the outputs of individual concept detection. Due to the limitations and availability of training cor- pora which are usually sparsely and imprecisely labeled, training-based refinement methods for semantic indexing of visual media suffer in cor- rectly capturing relationships between concepts, including co-occurrence and ontological relationships. In contrast to training-dependent methods which dominate this field, this paper presents a training-free refinement (TFR) algorithm for enhancing semantic indexing of visual media based purely on concept detection results, making the refinement of initial con- cept detections based on semantic enhancement, practical and flexible. This is achieved using global and temporal neighbourhood information inferred from the original concept detections in terms of weighted non- negative matrix factorization and neighbourhood-based graph propaga- tion, respectively. Any available ontological concept relationships can also be integrated into this model as an additional source of external a priori knowledge. Experiments on two datasets demonstrate the efficacy of the proposed TFR solution

Improving the classification of quantified self activities and behaviour using a Fisher kernel

Visual recording of everyday human activities and behaviour over the long term is now feasible and with the widespread use of wearable devices embedded with cameras this offers the potential to gain real insights into wearers’ activities and behaviour. To date we have concentrated on automatically detecting semantic concepts from within visual lifelogs yet identifying human activities from such lifelogged images or videos is still a major challenge if we are to use lifelogs to maximum benefit. In this paper, we propose an activity classification method from visual lifelogs based on Fisher kernels, which extract discriminative embeddings from Hidden Markov Models (HMMs) of occurrences of semantic concepts. By using the gradients as features, the resulting classifiers can better distinguish different activities and from that we can make inferences about human behaviour. Experiments show the effectiveness of this method in improving classification accuracy, especially when the semantic concepts are initially detected with low degrees of accuracy

Scalable Multiuser Immersive Communications with Multi-numerology and Mini-slot

This paper studies multiuser immersive communications networks in which different user equipment may demand various extended reality (XR) services. In such heterogeneous networks, time-frequency resource allocation needs to be more adaptive since XR services are usually multi-modal and latency-sensitive. To this end, we develop a scalable time-frequency resource allocation method based on multi-numerology and mini-slot. To appropriately determining the discrete parameters of multi-numerology and mini-slot for multiuser immersive communications, the proposed method first presents a novel flexible time-frequency resource block configuration, then it leverages the deep reinforcement learning to maximize the total quality-of-experience (QoE) under different users' QoE constraints. The results confirm the efficiency and scalability of the proposed time-frequency resource allocation method