Common Vector Research Articles

Robust Multimodal Sentiment Analysis via Tag Encoding of Uncertain Missing Modalities

Multimodal sentiment analysis aims to extract emotions with multiple data sources, usually under the assumption that all modalities are available. In practice, such a strong assumption does not always hold, and most of multimodal sentiment analysis methods may fail when partial modalities are missing. Some existing works have started to address the missing modality problem; but only considered the single modality missing case, while ignoring the practically more general cases of multiple modalities missing. To this end, in this paper, we propose a Tag-Assisted Transformer Encoder (TATE) network to handle the problem of missing uncertain modalities. Specifically, we design a tag encoding module to cover both the single modality and multiple modalities missing cases, so as to guide the network's attention to those missing modalities. Besides, a new space projection pattern is adopted to align common vectors, taking into account the different importance of each modality. Afterwards, a Transformer encoder-decoder network is utilized to learn the missing modality features, and the outputs of the Transformer encoder are extracted for the final sentiment classification. Extensive experiments and analyses are conducted on CMU-MOSI, IEMOCAP, and MELD datasets, which show that the proposed method can achieve significant improvements compared with several baselines.

BRADYCARDIA-AN ALARMING SIGN IN DENGUE FEVER

Dengue Fever is one of the most common vector borne disease which is caused by bite of Aedes aegypti in Tropical parts of world. Bleeding manifestations are most common signs due to Thrombocytopenia. Relative Bradycardia which is becoming one of the cause of mortality in Dengue Fever. This study aims to observe, changes in Heart rate in Dengue Fever. Materials and methods: This was an observational study done at Narayana medical college and hospital, Nellore, from June 2021 to October 2022 on 150 patients satisfying inclusion and exclusion criteria, demographic data and clinical features and laboratory evaluation was done for patients in the study vitals signs (pulse/heart rate, Blood pressure, respiratory rate, temperature) was noted for all patients. Results: Out of 150 patients, 43.3% have relative bradycardia and 30% patients have absolute bradycardia with ECG showing sinus bradycardia in 28% of patients. Conclusion: Relative bradycardia in dengue fever patients is present in association with thrombocytopenia and is important contributing factor to mortality in patients with dengue fever.

Recent Trends of Dengue Virus Infection in Dhaka, Bangladesh

The disease caused by the Dengue virus (DENV), belongs to the Flaviviridae family, which is the most common vector for transmission of the virus between humans and arthropods Aedes aegypti mosquitoes. In recent years, the number of dengue cases has increased drastically throughout the world, according to a survey around 390 million dengue infection cases are recorded annually, among which one-fourth of cases show clinical manifestations. In Bangladesh, dengue outbreaks are on the increasing trend despite the efforts to control this disease A Hospital-based study of dengue patients in Dhaka city was conducted to check the recent trends of dengue infection among the inhabitants of Dhaka, Bangladesh. 89 blood samples from different patients with appropriate symptoms were tested using the Dengue NS1 Quanticard kit. Among samples, 60 (68%) samples were tested as positive whereas 22 (25%) showed negative results and 6 (7%) showed invalid results in tests. Despite the testing of a low number of samples, it can be clearly stated that a large number of positive results was observed with specific symptoms commonly found in Dengue infection. Among all the positive results some (7%) of the patients did not show any signs and symptoms, but after testing the results were positive against the viral antigen. Attempt to increase awareness about dengue fever through effective management measures from the government and NGOs are very much needed. Stamford Journal of Microbiology, Vol.12 (1) 2022: 43-46

Impact of different mosquito collection methods on indicators of Anopheles malaria vectors in Uganda

BackgroundMethods used to sample mosquitoes are important to consider when estimating entomologic metrics. Human landing catches (HLCs) are considered the gold standard for collecting malaria vectors. However, HLCs are labour intensive, can expose collectors to transmission risk, and are difficult to implement at scale. This study compared alternative methods to HLCs for collecting Anopheles mosquitoes in eastern Uganda.MethodsBetween June and November 2021, mosquitoes were collected from randomly selected households in three parishes in Tororo and Busia districts. Mosquitoes were collected indoors and outdoors using HLCs in 16 households every 4 weeks. Additional collections were done indoors with prokopack aspirators, and outdoors with pit traps, in these 16 households every 2 weeks. CDC light trap collections were done indoors in 80 households every 4 weeks. Female Anopheles mosquitoes were identified morphologically and Anopheles gambiae sensu lato were speciated using PCR. Plasmodium falciparum sporozoite testing was done with ELISA.ResultsOverall, 4,891 female Anopheles were collected, including 3,318 indoors and 1,573 outdoors. Compared to indoor HLCs, vector density (mosquitoes per unit collection) was lower using CDC light traps (4.24 vs 2.96, density ratio [DR] 0.70, 95% CIs 0.63–0.77, p < 0.001) and prokopacks (4.24 vs 1.82, DR 0.43, 95% CIs 0.37–0.49, p < 0.001). Sporozoite rates were similar between indoor methods, although precision was limited. Compared to outdoor HLCs, vector density was higher using pit trap collections (3.53 vs 6.43, DR 1.82, 95% CIs 1.61–2.05, p < 0.001), while the sporozoite rate was lower (0.018 vs 0.004, rate ratio [RR] 0.23, 95% CIs 0.07–0.75, p = 0.008). Prokopacks collected a higher proportion of Anopheles funestus (75.0%) than indoor HLCs (25.8%), while pit traps collected a higher proportion of Anopheles arabiensis (84.3%) than outdoor HLCs (36.9%).ConclusionIn this setting, the density and species of mosquitoes collected with alternative methods varied, reflecting the feeding and resting characteristics of the common vectors and the different collection approaches. These differences could impact on the accuracy of entomological indicators and estimates of malaria transmission, when using the alternative methods for sampling mosquitos, as compared to HLCs.

Characterization of a recombinant tomato leaf curl New Delhi Virus (ToLCNDV) in a perennial medicinal climber host (Ipomoea cairica (L.) Sweet).

During the year 2020-2021, a disease syndrome very commonly observed in railway creepers (Ipomoea cairica(L.) Sweet) was taken into consideration from Gorakhpur Province (UP East region). Whitefly, a common vector for plant-related viral diseases was observed for wide transmission of disease. DNA from 17 infected leaf samples was isolated and analyzed through PCR using specific primers designed for the detection of Begomoviruses. Further amplification of isolated DNA fragments supporting a begomovirus infection with an estimated size of (2.7kb). RCA of the isolated DNA sample was carried out using ϕ29 DNA polymerase by digesting it through a set of restriction endonucleases (such as BamHI, Kpn1, HindIII, EcoRI) obtaining the best result through KpnI. The amplified segment was cloned into pUC 18vectors. The obtained sequences were matched using the NCBI BLAST database showing the highest sequence similarity of 98.7% with ToLCNDV of snake gourd (Accession no.KY780199), isolates of CP genes sequence in Varanasi. ToLCNDV, a begomovirus of bipartite nature was first reported to be from Tomato (Solanaceae), later affecting certain members of the Cucurbitaceae family in India and adjacent countries. The obtained DNA sequence was submitted at NCBI with the name ToLCNDV-IP (GenBank Accession no. OM777194). The phylogenetic analysis clubbed the current isolate ToLCNDV-IP with Asian isolates. All European isolates were clubbed in separate clades indicating two distinct groups of ToLCNDV isolates. ToLCNDV-IP isolate was found to be an intralineage recombinant between two Asian isolates viz ToLCNDV isolate from Papaya (India) and ToLCNDV isolate from Tomato (Pakistan). This study shows the association of recombinant ToLCNDV infection in a new hostIpomoea cairicafor the first time in India.

FEATURE ENGINEERING WITH SENTENCE SIMILARITY USING THE LONGEST COMMON SUBSEQUENCE FOR EMAIL CLASSIFICATION

Feature selection plays a prominent role in email classification since selecting the most relevant features enhances the accuracy and performance of the learning classifier. Due to the exponential increase rate in the usage of emails, the classification of such emails posed a fitting problem. Therefore, there is a requirement for a proper classification system. Such an email classification system requires an efficient feature selection method for the accurate classification of the most relevant features. This paper proposes a novel feature selection method for sentence similarity using the longest common subsequence for email classification. The proposed feature selection method works in two main phases: First, it builds the longest common subsequence vector of features by comparing each email with all other emails in the dataset. Later, a template is constructed for each class using the closest features of emails of a particular class. Further, email classification is tested for unseen emails using these templates. The performance of the proposed method is compared with traditional feature selection methods such as TF-IDF, Information Gain, Chi-square, and semantic approach. The experimental results showed that the proposed method performed well with 96.61% accuracy.

Imaging-guided gene delivery: seeing is delivering

Imaging-guided gene delivery: seeing is delivering

Multimodal Analogy-Based Image Retrieval by Improving Semantic Embeddings

In this work, we study the application of multimodal analogical reasoning to image retrieval. Multimodal analogy questions are given in a form of tuples of words and images, e.g., “cat”:“dog”::[an image of a cat sitting on a bench]:?, to search for an image of a dog sitting on a bench. Retrieving desired images given these tuples can be seen as a task of finding images whose relation between the query image is close to that of query words. One way to achieve the task is building a common vector space that exhibits analogical regularities. To learn such an embedding, we propose a quadruple neural network called multimodal siamese network. The network consists of recurrent neural networks and convolutional neural networks based on the siamese architecture. We also introduce an effective procedure to generate analogy examples from an image-caption dataset for training of our network. In our experiments, we test our model on analogy-based image retrieval tasks. The results show that our method outperforms the previous work in qualitative evaluation.

Impact of aerial humidity on seasonal malaria: an ecological study in Zambia

BackgroundSeasonal patterns of malaria cases in many parts of Africa are generally associated with rainfall, yet in the dry seasons, malaria transmission declines but does not always cease. It is important to understand what conditions support these periodic cases. Aerial moisture is thought to be important for mosquito survival and ability to forage, but its role during the dry seasons has not been well studied. During the dry season aerial moisture is minimal, but intermittent periods may arise from the transpiration of peri-domestic trees or from some other sources in the environment. These periods may provide conditions to sustain pockets of mosquitoes that become active and forage, thereby transmitting malaria. In this work, humidity along with other ecological variables that may impact malaria transmission have been examined.MethodsNegative binomial regression models were used to explore the association between peri-domestic tree humidity and local malaria incidence. This was done using sensitive temperature and humidity loggers in the rural Southern Province of Zambia over three consecutive years. Additional variables including rainfall, temperature and elevation were also explored.ResultsA negative binomial model with no lag was found to best fit the malaria cases for the full year in the evaluated sites of the Southern Province of Zambia. Local tree and granary night-time humidity and temperature were found to be associated with local health centre-reported incidence of malaria, while rainfall and elevation did not significantly contribute to this model. A no lag and one week lag model for the dry season alone also showed a significant effect of humidity, but not temperature, elevation, or rainfall.ConclusionThe study has shown that throughout the dry season, periodic conditions of sustained humidity occur that may permit foraging by resting mosquitoes, and these periods are associated with increased incidence of malaria cases. These results shed a light on conditions that impact the survival of the common malaria vector species, Anopheles arabiensis, in arid seasons and suggests how they emerge to forage when conditions permit.

A Transfer Learning Method for Forecasting Masked-Load With Behind-the-Meter Distributed Energy Resources

With increasing installation of behind-the-meter distributed energy resources (DERs) at the household level, the power load profile has been significantly masked. As a result, original load forecasting models are becoming not suitable for the continuously masked-load. Besides, present masked-load may not have sufficient samples to train an accurate forecasting model. This letter proposed a transfer learning-based method to solve this problem by capturing different but related relationships between the unmasked-load and masked-load datasets. The common feature vectors from the two datasets are extracted by adversarial training, then the feature vectors from masked-load could be compatible with a predictor which is based on feature vectors from unmasked-load, thus masked-load could be accurately predicted. Simulation results show that the proposed method has higher accuracy compared to the benchmark models.

Development of Multi-Image Compression Technique Based on Common Code Vector

Development of Multi-Image Compression Technique Based on Common Code Vector

Diabetes and climate change.

It is widely accepted that climate change is the biggest threat to human health. The pandemic of diabetes is also a major threat to human health, especially in rapidly developing nations. Climate change and diabetes appear to have common global vectors, including increased urbanisation, increased use of transportation, and production and ingestion of ultra-processed foods. People with diabetes appear to be at higher risk of threats to health from climate change, including effects from extreme heat or extreme cold, and natural disasters. Solutions to climate change offer some benefits for the prevention of diabetes and diabetes-related complications. Moving towards lower carbon economies is likely to help reduce reliance on intensive agriculture, reduce physical inactivity, reduce air pollution and enhance quality of life. It may enable a reduction in the prevalence of diabetes and reduced morbidity from the condition.

Epidemiology and patterns of nasal bone fracture in elderly patients in comparison to other age groups: an 8-year single-center retrospective analysis.

Nasal bone fractures are the most common type of facial bone fracture, but are under-studied in adults above 65 years of age. Therefore, we investigated the epidemiology and patterns of nasal bone fractures among older adults in comparison to different age groups. This retrospective study included 2,321 nasal bone fracture patients who underwent surgery at our hospital from January 2010 to December 2017. The patients were classified by age as preschoolers, school-age children, young and middle-aged adults, and the elderly. We performed pairwise comparisons between elderly patients and each other age group in terms of sex, cause of injury, and fracture type. The 2,321 nasal bone fracture patients included 76 elderly patients (50 men [65.8%] and 26 women [34.2%]). In these patients, the two most common injury causes were falling or slipping down (n = 39; 51.3%) and road traffic accidents (n = 19; 25.0%). According to the Stranc and Robertson classification, the most common force vector was lateral, and plane 2 fractures with lateral forces predominated. The elderly showed similar patterns of nasal bone fractures to those observed in young and middle-aged adults, but significant differences from preschoolers (in the injury vector and plane of fracture) and from school-age children (in the sex ratio and plane of fracture). However, elderly patients presented significantly different epidemiological characteristics compared to the other three groups. Therefore, it is necessary to improve the quality of life of the elderly and prepare for the upcoming super-aged society by taking steps to reduce the incidence and severity of fractures. Possible options for doing so include strengthening individual-level safety factors and expanding the social safety net for the elderly.

A novel therapeutic concern: Antibiotic resistance genes in common chronic diseases.

Infections caused by multidrug-resistant bacteria carrying antibiotic resistance genes pose a severe threat to global public health and human health. In clinical practice, it has been found that human gut microbiota act as a "reservoir" of antibiotic resistance genes (ARGs) since gut microbiota contain a wide variety of ARGs, and that the structure of the gut microbiome is influenced by the profile of the drug resistance genes present. In addition, ARGs can spread within and between species of the gut microbiome in multiple ways. To better understand gut microbiota ARGs and their effects on patients with chronic diseases, this article reviews the generation of ARGs, common vectors that transmit ARGs, the characteristics of gut microbiota ARGs in common chronic diseases, their impact on prognosis, the current state of treatment for ARGs, and what should be addressed in future research.

On two correlated linear models with common and different parameters

Abstract A parametric regression model may comprise several correlated individual regression equations, and these equations may have common and different unknown parameters. In such a situation, the common unknown parameter vectors in these equations can be estimated individually or simultaneously according to various available statistical inference methods. The purpose of this paper is to provide an integral account of two classic objects in regression theory: the best linear unbiased predictors (BLUPs) and the best linear unbiased estimators (BLUEs) on common and different unknown parameters in two correlated linear models with common and different parameters. We first introduce some reduced models associated with the two correlated linear models. We then define and characterize predictability and estimability of all unknown parameter vectors in the two correlated models and their reduced models, and derive analytical formulas for calculating the BLUPs and BLUEs of all unknown parameter vectors in these models by means of a constrained quadratic matrix optimization method. We also discuss a variety of theoretical properties of the predictors and estimators.

A Simple, interpretable method to identify surprising topic shifts in scientific fields.

This paper proposes a text-mining framework to systematically identify vanishing or newly formed topics in highly interdisciplinary and diverse fields like cognitive science. We apply topic modeling via non-negative matrix factorization to cognitive science publications before and after 2012; this allows us to study how the field has changed since the revival of neural networks in the neighboring field of AI/ML. Our proposed method represents the two distinct sets of topics in an interpretable, common vector space, and uses an entropy-based measure to quantify topical shifts. Case studies on vanishing (e.g., connectionist/symbolic AI debate) and newly emerged (e.g., art and technology) topics are presented. Our framework can be applied to any field or any historical event considered to mark a major shift in thought. Such findings can help lead to more efficient and impactful scientific discoveries.

Step by Step Design of Popular and Common Vectors in Genetic Manipulation Using CRISPR/Cas9 System

Step by Step Design of Popular and Common Vectors in Genetic Manipulation Using CRISPR/Cas9 System

Joint Optimization of Sum and Difference Patterns with a Common Weight Vector Using the Genetic Algorithm

A monopulse searching and tracking radar antenna array with a large number of radiating elements requires a simple and efficient design of the feeding network. In this paper, an effective and versatile method for jointly optimizing the sum and difference patterns using the genetic algorithm is proposed. Moreover, the array feeding network is simplified by attaching a single common weight to each of its elements. The optimal sum pattern with the desired constraints is first generated by independently optimizing amplitude weights of the array elements. The suboptimal difference pattern is then obtained by introducing a phase displacement π to half of the array elements under the condition of sharing some sided elements weights of the sum mode. The sharing percentage is controlled by the designer, such that the best performance can be met. The remaining uncommon weights of the difference mode represent the number of degrees of freedom which create a compromise difference pattern. Simulation results demonstrate the effectiveness of the proposed method in generating the optimal sum and suboptimal difference patterns characterized by independently, partially, and even fully common weight vectors.

In search for the most preferred solution in value efficiency analysis

Choosing the Most Preferred Solution (MPS), namely a real or artificial Decision Making Unit (DMU) reflecting the decision maker’s preferences over the desirable structure of inputs and outputs, is of particular importance in Value Efficiency Analysis (VEA). In this paper, we review various MPS choices used in the VEA literature and propose some new, which rely respectively on the relative position of frontier DMUs, the Most Productive Scale Size (MPSS), the Average Production Unit (APU), and common vectors of weights. The suggested MPS choices reflect overall organizational goals such as the pursuit of scale economies and the maximization of structural efficiency, or the need to assess DMUs against common standards because of limited control over the resources allocated to them or autonomy in setting their own priorities. The potential implications of using different MPSs in VEA are illustrated by providing comparative empirical results using a dataset of 526 Greek cotton farms.

Vegetation detection using vegetation indices algorithm supported by statistical machine learning.

In precision agriculture (PA), the usage of image processing, artificial intelligence, data analysis, and internet of things provides an increase in efficiency, energy, and time saving. In image processing-based applications, vegetation detection, in other words, segmentation that allows monitoring of plant growth and health as well as identification of weeds has a great importance. Vegetation indices (VIs) are widely used algorithms for segmentation. Their advantages include low computational cost and easy implementation and handling compared to the other algorithms. Nevertheless, they require a manual threshold detection that customizes the process and prevents generalization. In this study, a novel automatic segmentation method, which does not require a manual threshold detection by combining VIs with a classification algorithm, is proposed. It deals with the segmentation process as a two class classification problem (vegetation and background). As the classification algorithm, Discriminative Common Vector Approach (DCVA) that has a high discrimination power is used. Each image pixel is represented with a 3 × 1 dimensional vector whose elements correspond to Excess Green (ExG), Green minus Blue (GB), and Color Index of Vegetation (CIVE); VI values are obtained. Then, on the sample space accepting this pixel vector as a sample, DCVA is applied and a discriminative common vector for each class which is unique and describes that class in the best way possible is obtained and it is used for classification. Proposed segmentation method's performance is compared with Convolutional Neural Networks (CNN) and Random Forest (RF) algorithm. The proposed segmentation algorithm outperformed both CNN's and RF's performance.