LEARNet Dynamic Imaging Network for Micro Expression Recognition

Unlike prevalent facial expressions, micro expressions have subtle, involuntary muscle movements which are short-lived in nature. These minute muscle movements reflect true emotions of a person. Due to the short duration and low intensity, these micro-expressions are very difficult to perceive and interpret correctly. In this paper, we propose the dynamic representation of micro-expressions to preserve facial movement information of a video in a single frame. We also propose a Lateral Accretive Hybrid Network (LEARNet) to capture micro-level features of an expression in the facial region. The LEARNet refines the salient expression features in accretive manner by incorporating accretion layers (AL) in the network. The response of the AL holds the hybrid feature maps generated by prior laterally connected convolution layers. Moreover, LEARNet architecture incorporates the cross decoupled relationship between convolution layers which helps in preserving the tiny but influential facial muscle change information. The visual responses of the proposed LEARNet depict the effectiveness of the system by preserving both high- and micro-level edge features of facial expression. The effectiveness of the proposed LEARNet is evaluated on four benchmark datasets: CASME-I, CASME-II, CAS(ME)^2 and SMIC. The experimental results after investigation show a significant improvement of 4.03%, 1.90%, 1.79% and 2.82% as compared with ResNet on CASME-I, CASME-II, CAS(ME)^2 and SMIC datasets respectively.Comment: Dynamic imaging, accretion, lateral, micro expression recognitio

Efficient Neural Architecture Search for Emotion Recognition

Automated human emotion recognition from facial expressions is a well-studied problem and still remains a very challenging task. Some efficient or accurate deep learning models have been presented in the literature. However, it is quite difficult to design a model that is both efficient and accurate at the same time. Moreover, identifying the minute feature variations in facial regions for both macro and micro-expressions requires expertise in network design. In this paper, we proposed to search for a highly efficient and robust neural architecture for both macro and micro-level facial expression recognition. To the best of our knowledge, this is the first attempt to design a NAS-based solution for both macro and micro-expression recognition. We produce lightweight models with a gradient-based architecture search algorithm. To maintain consistency between macro and micro-expressions, we utilize dynamic imaging and convert microexpression sequences into a single frame, preserving the spatiotemporal features in the facial regions. The EmoNAS has evaluated over 13 datasets (7 macro expression datasets: CK+, DISFA, MUG, ISED, OULU-VIS CASIA, FER2013, RAF-DB, and 6 micro-expression datasets: CASME-I, CASME-II, CAS(ME)2, SAMM, SMIC, MEGC2019 challenge). The proposed models outperform the existing state-of-the-art methods and perform very well in terms of speed and space complexity

Simultaneous capturing of mixed contaminants from wastewater using novel one-pot chitosan functionalized with EDTA and graphene oxide adsorbent

The emergence of inorganic and organic contaminants has raised great concerns owing to their adverse impact on human health and ecological security. Herein, first time one-pot process was applied for chitosan (CS) functionalization using graphene oxide (GO) and ethylenediaminetetraacetic acid (EDTA) for simultaneous capturing of toxic inorganic (lead (Pb2+) and cadmium (Cd2+)) and organic (ciprofloxacin (CIP) and sildenafil (SDF)) contaminants from wastewater. In this approach, we believe that CS would work as a backbone, GO would capture both inorganic and organic contaminants via electrostatic interactions, while EDTA would make complexation with heavy metals. Various parameters including pH, reaction time, concentration, reusability etc. were evaluated to achieve the best experimental result in monocomponent system. The prepared adsorbent displayed an excellent monolayer adsorption capacity of 351.20 and 264.10 mg g−1 for Pb2+ and Cd2+, respectively, while a heterogeneous sorption capacity of 75.40 and 40.90 mg g−1 for CIP and SDF, respectively. The kinetics data fitted well to Pseudo-second order (PSO) kinetics model for both types of contaminants and gave faster interaction towards metal ions (higher k2) than organic contaminants. Experimental results showed excellent adsorption efficiencies at environmental levels in the capturing of both inorganic and organic contaminants at the same time from polluted water. The capturing mechanism of both types of contaminants was explained by elemental mapping, EDS, and FT−IR spectra. Overall, easy synthesis, excellent capturing capacity, and reusability imply that the prepared adsorbent has a sufficient potential for the treatment of co-existing toxic contaminants in water

Multimodal Spatiotemporal Deep Learning Framework to Predict Response of Breast Cancer to Neoadjuvant Systemic Therapy

Current approaches to breast cancer therapy include neoadjuvant systemic therapy (NST). The efficacy of NST is measured by pathologic complete response (pCR). A patient who attains pCR has significantly enhanced disease-free survival progress. The accurate prediction of pCR in response to a given treatment regimen could increase the likelihood of achieving pCR and prevent toxicities caused by treatments that are not effective. Th early prediction of response to NST can increase the likelihood of survival and help with decisions regarding breast-conserving surgery. An automated NST prediction framework that is able to precisely predict which patient undergoing NST will achieve a pathological complete response (pCR) at an early stage of treatment is needed. Here, we propose an end-to-end efficient multimodal spatiotemporal deep learning framework (deep-NST) framework to predict the outcome of NST prior or at an early stage of treatment. The deep-NST model incorporates imaging data captured at different timestamps of NST regimens, a tumor’s molecular data, and a patient’s demographic data. The efficacy of the proposed work is validated on the publicly available ISPY-1 dataset, in terms of accuracy, area under the curve (AUC), and computational complexity. In addition, seven ablation experiments were carried out to evaluate the impact of each design module in the proposed work. The experimental results show that the proposed framework performs significantly better than other recent methods

Role and Recent Advancements of Ionic Liquids in Drug Delivery Systems

Advancements in the fields of ionic liquids (ILs) broaden its applications not only in traditional use but also in different pharmaceutical and biomedical fields. Ionic liquids “Solutions for Your Success” have received a lot of interest from scientists due to a myriad of applications in the pharmaceutical industry for drug delivery systems as well as targeting different diseases. Solubility is a critical physicochemical property that determines the drug’s fate at the target site. Many promising drug candidates fail in various phases of drug research due to poor solubility. In this context, ionic liquids are regarded as effective drug delivery systems for poorly soluble medicines. ILs are also able to combine different anions/cations with other cations/anions to produce salts that satisfy the concept behind the ILs. The important characteristics of ionic liquids are the modularity of their physicochemical properties depending on the application. The review highlights the recent advancement and further applications of ionic liquids to deliver drugs in the pharmaceutical and biomedical fields