Mixed Information Flow for Cross-Domain Sequential Recommendations

Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains. Previous studies have investigated the flow of behavioral information by exploring the connection between items from different domains. The flow of knowledge (i.e., the connection between knowledge from different domains) has so far been neglected. In this article, we propose a mixed information flow network for cross-domain sequential recommendation to consider both the flow of behavioral information and the flow of knowledge by incorporating a behavior transfer unit and a knowledge transfer unit . The proposed mixed information flow network is able to decide when cross-domain information should be used and, if so, which cross-domain information should be used to enrich the sequence representation according to users’ current preferences. Extensive experiments conducted on four e-commerce datasets demonstrate that the proposed mixed information flow network is able to improve recommendation performance in different domains by modeling mixed information flow. In this article, we focus on the application of mixed information flow network s to a scenario with two domains, but the method can easily be extended to multiple domains.

Persian Fake News Detection: Neural Representation and Classification at Word and Text Levels

Nowadays, broadcasting news on social media and websites has grown at a swifter pace, which has had negative impacts on both the general public and governments; hence, this has urged us to build a fake news detection system. Contextualized word embeddings have achieved great success in recent years due to their power to embed both syntactic and semantic features of textual contents. In this article, we aim to address the problem of the lack of fake news datasets in Persian by introducing a new dataset crawled from different news agencies, and propose two deep models based on the Bidirectional Encoder Representations from Transformers model (BERT), which is a deep contextualized pre-trained model for extracting valuable features. In our proposed models, we benefit from two different settings of BERT, namely pool-based representation, which provides a representation for the whole document, and sequence representation, which provides a representation for each token of the document. In the former one, we connect a Single Layer Perceptron (SLP) to the BERT to use the embedding directly for detecting fake news. The latter one uses Convolutional Neural Network (CNN) after the BERT’s embedding layer to extract extra features based on the collocation of words in a corpus. Furthermore, we present the TAJ dataset, which is a new Persian fake news dataset crawled from news agencies’ websites. We evaluate our proposed models on the newly provided TAJ dataset as well as the two different Persian rumor datasets as baselines. The results indicate the effectiveness of using deep contextualized embedding approaches for the fake news detection task. We also show that both BERT-SLP and BERT-CNN models achieve superior performance to the previous baselines and traditional machine learning models, with 15.58% and 17.1% improvement compared to the reported results by Zamani et al. [ 30 ], and 11.29% and 11.18% improvement compared to the reported results by Jahanbakhsh-Nagadeh et al. [ 9 ].

Sequential Recommendation through Graph Neural Networks and Transformer Encoder with Degree Encoding

Predicting users’ next behavior through learning users’ preferences according to the users’ historical behaviors is known as sequential recommendation. In this task, learning sequence representation by modeling the pairwise relationship between items in the sequence to capture their long-range dependencies is crucial. In this paper, we propose a novel deep neural network named graph convolutional network transformer recommender (GCNTRec). GCNTRec is capable of learning effective item representation in a user’s historical behaviors sequence, which involves extracting the correlation between the target node and multi-layer neighbor nodes on the graphs constructed under the heterogeneous information networks in an end-to-end fashion through a graph convolutional network (GCN) with degree encoding, while the capturing long-range dependencies of items in a sequence through the transformer encoder model. Using this multi-dimensional vector representation, items related to the a user historical behavior sequence can be easily predicted. We empirically evaluated GCNTRec on multiple public datasets. The experimental results show that our approach can effectively predict subsequent relevant items and outperforms previous techniques.

Draft genome assemblies for tree pathogens Phytophthora pseudosyringae and Phytophthora boehmeriae

Species of Phytophthora, plant pathogenic eukaryotic microbes, can cause disease on many tree species. Genome sequencing of species from this genus has helped to determine components of their pathogenicity arsenal. Here we sequenced genomes for two widely distributed species, P. pseudosyringae and P. boehmeriae, yielding genome assemblies of 49 Mb and 40 Mb, respectively. We identified more than 280 candidate disease promoting RXLR effector coding genes for each species, and hundreds of genes encoding candidate plant cell wall degrading carbohydrate active enzymes (CAZymes). These data boost genome sequence representation across the Phytophthora genus, and form resources for further study of Phytophthora pathogenesis.

Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation

Recurrent neural networks (RNNs) have been widely used to model sequential neural dynamics (“neural sequences”) of cortical circuits in cognitive and motor tasks. Efforts to incorporate biological constraints and Dale's principle will help elucidate the neural representations and mechanisms of underlying circuits. We trained an excitatory-inhibitory RNN to learn neural sequences in a supervised manner and studied the representations and dynamic attractors of the trained network. The trained RNN was robust to trigger the sequence in response to various input signals and interpolated a time-warped input for sequence representation. Interestingly, a learned sequence can repeat periodically when the RNN evolved beyond the duration of a single sequence. The eigenspectrum of the learned recurrent connectivity matrix with growing or damping modes, together with the RNN's nonlinearity, were adequate to generate a limit cycle attractor. We further examined the stability of dynamic attractors while training the RNN to learn two sequences. Together, our results provide a general framework for understanding neural sequence representation in the excitatory-inhibitory RNN.

Early and ongoing importations of SARS-CoV-2 in Canada

Tracking the emergence and spread of SARS–CoV–2 is critical to inform public health interventions. Phylodynamic analyses have quantified SARS–CoV–2 migration on global and local scales, yet they have not been applied to determine transmission dynamics in Canada. We quantified SARS-CoV-2 migration into, within, and out of Canada in the context of COVID-19 travel restrictions. To minimize sampling bias, global sequences were subsampled with probabilities corrected for their countries' monthly contribution to global new diagnoses. A time–scaled maximum likelihood tree was used to estimate most likely ancestral geographic locations (country or Canadian province), enabling identification of sublineages, defined as introduction events into Canada resulting in domestic transmission. Of 402 Canadian sublineages identified, the majority likely originated from the USA (54%), followed by Russia (7%), India (6%), Italy (6%), and the UK (5%). International introductions were mostly into Ontario (39%) and Quebec (38%). Among Pango lineages, B.1 was imported at least 191 separate times from 11 different countries. Introduction rates peaked in late March then diminished but were not eliminated following national interventions including restrictions on non–essential travel. We further identified 1,380 singleton importations, international importations that did not result in further sampled transmission, whereby representation of lineages and location were comparable to sublineages. Although proportion of international transmission decreased over time, this coincided with exponential growth of within–province transmission – in fact, total number of sampled transmission events from international or interprovincial sources increased from winter 2020 into spring 2020 in many provinces. Ontario, Quebec, and British Columbia acted as sources of transmission more than recipients, within the caveat of higher sequence representation. We present strong evidence that international introductions and interprovincial transmission of SARS–CoV–2 contributed to the Canadian COVID–19 burden throughout 2020, despite initial reductions mediated by travel restrictions in 2020. More stringent border controls and quarantine measures may have curtailed introductions of SARS–CoV–2 into Canada and may still be warranted.