A dynamic emotion recognition system based on convolutional feature extraction and recurrent neural network

Over the past three decades, there has been sustained research activity in emotion recognition from faces, powered by the popularity of smart devices and the development of improved machine learning, resulting in the creation of recognition systems with high accuracy. While research has commonly focused on single images, recent research has also made use of dynamic video data. This paper presents CNN-RNN (Convolutional Neural Network - Recurrent Neural Network) based emotion recognition using videos from the ADFES database, and we present the results in the arousal-valence space, rather than assigning a discrete emotion. As well as traditional performance metrics, we also design a new performance metric, PN accuracy, to distinguish between positive and negative emotions. We demonstrate improved performance with a smaller RNN than the initial pre-trained model, and report a peak accuracy of 0.58, with peak PN accuracy of 0.76, which shows our approach is very capable distinguishing between positive and negative emotions. We also present a detailed analysis of system performance, using new valence-arousal domain temporal visualisations to show transitions in recognition over time, demonstrating the importance of context based information in emotion recognition

Initializing Models with Larger Ones

Weight initialization plays an important role in neural network training. Widely used initialization methods are proposed and evaluated for networks that are trained from scratch. However, the growing number of pretrained models now offers new opportunities for tackling this classical problem of weight initialization. In this work, we introduce weight selection, a method for initializing smaller models by selecting a subset of weights from a pretrained larger model. This enables the transfer of knowledge from pretrained weights to smaller models. Our experiments demonstrate that weight selection can significantly enhance the performance of small models and reduce their training time. Notably, it can also be used together with knowledge distillation. Weight selection offers a new approach to leverage the power of pretrained models in resource-constrained settings, and we hope it can be a useful tool for training small models in the large-model era. Code is available at https://github.com/OscarXZQ/weight-selection

Impact of Immunization Technology and Assay Application on Antibody Performance – A Systematic Comparative Evaluation

Antibodies are quintessential affinity reagents for the investigation and determination of a protein's expression patterns, localization, quantitation, modifications, purification, and functional understanding. Antibodies are typically used in techniques such as Western blot, immunohistochemistry (IHC), and enzyme-linked immunosorbent assays (ELISA), among others. The methods employed to generate antibodies can have a profound impact on their success in any of these applications. We raised antibodies against 10 serum proteins using 3 immunization methods: peptide antigens (3 per protein), DNA prime/protein fragment-boost (“DNA immunization”; 3 per protein), and full length protein. Antibodies thus generated were systematically evaluated using several different assay technologies (ELISA, IHC, and Western blot). Antibodies raised against peptides worked predominantly in applications where the target protein was denatured (57% success in Western blot, 66% success in immunohistochemistry), although 37% of the antibodies thus generated did not work in any of these applications. In contrast, antibodies produced by DNA immunization performed well against both denatured and native targets with a high level of success: 93% success in Western blots, 100% success in immunohistochemistry, and 79% success in ELISA. Importantly, success in one assay method was not predictive of success in another. Immunization with full length protein consistently yielded the best results; however, this method is not typically available for new targets, due to the difficulty of generating full length protein. We conclude that DNA immunization strategies which are not encumbered by the limitations of efficacy (peptides) or requirements for full length proteins can be quite successful, particularly when multiple constructs for each protein are used

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Knowledge-Graph Augmented Word Representations for Named Entity Recognition

By modeling the context information, ELMo and BERT have successfully improved the state-of-the-art of word representation, and demonstrated their effectiveness on the Named Entity Recognition task. In this paper, in addition to such context modeling, we propose to encode the prior knowledge of entities from an external knowledge base into the representation, and introduce a Knowledge-Graph Augmented Word Representation or KAWR for named entity recognition. Basically, KAWR provides a kind of knowledge-aware representation for words by 1) encoding entity information from a pre-trained KG embedding model with a new recurrent unit (GERU), and 2) strengthening context modeling from knowledge wise by providing a relation attention scheme based on the entity relations defined in KG. We demonstrate that KAWR, as an augmented version of the existing linguistic word representations, promotes F1 scores on 5 datasets in various domains by +0.46∼+2.07. Better generalization is also observed for KAWR on new entities that cannot be found in the training sets

Secrecy Performance Analysis of Wireless Powered Sensor Networks Under Saturation Nonlinear Energy Harvesting and Activation Threshold

In this paper, we investigate the impact of saturation nonlinear energy harvesting (EH) and activation threshold on the multiuser wireless powered sensor networks (WPSNs) from the physical layer security (PLS) perspective. In particular, for improving the secrecy performance, the generalized multiuser scheduling (GMS) scheme is exploited, in which the Kth strongest sensor is chosen based on the legitimate link. For evaluating the impact of various key parameters on the security of system, we obtain the exact closed-form expressions for secrecy outage probability (SOP) under linear EH (LEH), saturation nonlinear EH (SNEH) and saturation nonlinear EH with activation threshold (SNAT), respectively, and solve the maximization problem of secure energy efficiency (SEE). Simulation results demonstrate that: (1) the number of source sensors, the EH efficiency and the transmit power of power beacon (PB) all have positive impact on SOP, and the smaller generalized selection coefficient is advantageous for secrecy performance; (2) LEH is an ideal situation for SNEH when the saturation threshold is large enough and SNEH is a special situation for SNAT when the activation threshold is low enough; (3) the time-switching factor and the activation threshold both have an important impact on the secrecy performance, which are worth considering carefully

Secure Green-Oriented Multiuser Scheduling for Wireless-Powered Internet of Things

In this paper, we consider the secure green-oriented multiuser scheduling for the wireless-powered Internet of Things (IoT) scenario, in which multiple source sensors communicate with a controller assisted by an intermediate sensor with the existence of a passive tapping device. Due to the limited energy, all sensors must acquire energy from external power beacons (PBs). Specifically, for the security improvement, we introduce two multiuser scheduling schemes possessing the optimal PB chosen by the relay, i.e., the best source sensor is scheduled in a random way (BSR), while the best source sensor is decided by the best PB (BSBP). Furthermore, for every scheme, we derive the analytical expressions for the secrecy outage probability (SOP) and investigate the secure energy efficiency (SEE) optimization problem with constricted transmission power in PBs. Simulation results reveal that the BSBP scheme provides better secrecy performance, and elevating the PBs quantity or reducing both the ratio of distance from PBs to source users and the total communication distance to some extent is helpful for improving SEE. In addition, the time-switching factor shows an important effect upon secrecy performance of the considered system

The Application Potential and Advance of Mesenchymal Stem Cell-Derived Exosomes in Myocardial Infarction

Myocardial infarction (MI) is a devastating disease with high morbidity and mortality caused by the irreversible loss of functional cardiomyocytes and heart failure (HF) due to the restricted blood supply. Mesenchymal stem cells (MSCs) have been emerging as lead candidates to treat MI and subsequent HF mainly through secreting multitudinous factors of which exosomes act as the most effective constituent to boost the repair of heart function through carrying noncoding RNAs and proteins. Given the advantages of higher stability in the circulation, lower toxicity, and controllable transplantation dosage, exosomes have been described as a wonderful and promising cell-free treatment method in cardiovascular disease. Nowadays, MSC-derived exosomes have been proposed as a promising therapeutic approach to improve cardiac function and reverse heart remodeling. However, exosomes’ lack of modification cannot result in desired therapeutic effect. Hence, optimized exosomes can be developed via various engineering methods such as pharmacological compound preconditioned MSCs, genetically modified MSCs, or miRNA-loaded exosomes and peptide tagged exosomes to improve the targeting and therapeutic effects of exosomes. The biological characteristics, therapeutic potential, and optimizing strategy of exosomes will be described in our review

RTANet: Recommendation Target-Aware Network Embedding

Network embedding is a process of encoding nodes into latent vectors by preserving network structure and content information. It is used in various applications, especially in recommender systems. In a social network setting, when recommending new friends to a user, the similarity between the user's embedding and the target friend will be examined. Traditional methods generate user node embedding without considering the recommendation target. No matter which target is to be recommended, the same embedding vector is generated for that particular user. This approach has its limitations. For example, a user can be both a computer scientist and a musician. When recommending music friends with potentially the same taste to him, we are interested in getting his representation that is useful in recommending music friends rather than computer scientists. His corresponding embedding should consider the user's musical features rather than those associated with computer science with the awareness that the recommendation targets are music friends. In order to address this issue, we propose a new framework which we name it as Recommendation Target-Aware Network embedding method (RTANet). Herein, the embedding of each user is no longer fixed to a constant vector, but it can vary according to their specific recommendation target. Concretely, RTANet assigns different attention weights to each neighbour node, allowing us to obtain the user's context information aggregated from its neighbours before transforming this context into its embedding. Different from other graph attention approaches, the attention weights in our work measure the similarity between each user's neighbour node and the target node, which in return generates the target-aware embedding. To demonstrate the effectiveness of our method, we compared RTANet with several state-of-the-art network embedding methods on four real-world datasets and showed that RTANet outperforms other comparative methods in the recommendation tasks