CHAPTER 2
LITERATURE SURVEY
“Patch-Based Principal Covariance Discriminative Learning for Image Set
Classification”, Hengliang Tan and Ying Gao, Image set classification has attracted
increasing attention with respect to the use of significant amounts of within-set information.
The covariance matrix is a natural and effective descriptor for describing image sets. Nonsingular covariance matrices, known as symmetric positive definite (SPD) matrices, are
regarded as points on a Riemannian manifold. A common method of classifying points on a
manifold is to explicitly map the SPD matrices from a Riemannian manifold to Euclidean
space, such as in the covariance discriminative learning (CDL) method. However, the
disadvantages of the CDL method are as follows: (1) the method models the whole image set
as a covariance matrix, whereas if there are insufficient set samples or merely one set, the
within-class information studied by the discriminative learning may not be utilized well and
(2) when the original sample covariance matrices are of high dimensionality, the
computational cost is non-trivial. To address these problems of CDL, we propose to exploit
the maximal linear patch to cluster image sets into multiple subsets (local patches), which
could provide substantially more within-class information. Moreover, we refine the manifold
formed by the SPD matrices to a lower dimensionality and more discriminative manifold by
collaboratively applying principal component analysis to all training sets. Experiments are
performed on face recognition and objection categorization tasks; extensive comparison
results illustrate the considerable effectiveness of our proposed method.
“A New Approach for Nonlinear Transformation of Means and Covariances in
Direct Statistical Analysis of Nonlinear Systems”, Quancheng Li, Yonghua Fan,
Hongyang Xu, Xuechao Liang, and Jie Yan, Covariance analysis describing function
technique is a conventional method to solve the performance analysis of the nonlinear missile
guidance system. Aiming at the faultiness of covariance analysis describing function
technique and its improved method, this paper proposes a new method to solve the mean and
covariance transfer issue by using the high-degree spherical-radial cubature rule. In terms of
simulation models, the nonlinear missile guidance system contains the saturation link to limit
the lateral acceleration command and the initial heading error is introduced. In addition, the
zero-mean target evasive maneuver with exponentially-decaying autocorrelation function is
chosen as the input of the missile guidance system. Furthermore, compared with the existing
methods, the proposed method has higher computational efficiency, accuracy and better
applicability in high-dimensional nonlinear systems by theoretical derivation and simulation
results from the mean and covariance values of relative lateral separation. Finally, the
presented analysis method can be used as a general method for the design and statistical
analysis of multidimensional nonlinear systems.
“The Algorithm That Mapped Omicron”, Yuan An, Siqiao Meng, and Hao Wu,
When Omicron, the now ubiquitous COVID-19 variant, first emerged in South Africa in
November, scientists were immediately worried. Genetic sequencing showed that Omicron
boasted dozens of mutations in key regions of its genetic code—about four times as many as
previous variants. Still, they did not know how much Omicron differed physically, not just
genetically, from previous variants. That's crucial information in the fight against SARSCoV-2, the virus that causes COVID-19. It's the physical changes—alterations in how a virus
looks and functions—that enable such a pathogen to cause infections by evading the immune
systems of people vaccinated or infected with prior strains. Genetic sequencing gives
researchers early clues about those changes, but only laboratory and clinical testing can
indicate what they mean for the human immune system and current vaccines. To that end,
scientists around the world have been frantically studying Omicron to determine how much
the variant differs physically from the original coronavirus strain and whether new vaccines
are needed—not just for Omicron, but for whatever comes next.
“Recent Advances in Biosensors for Detection of COVID-19 and Other Viruses”,
Shobhit K. Patel (Senior Member, IEEE), Jaymit Surve (Graduate Student Member),
This century has introduced very deadly, dangerous, and infectious diseases to humankind
such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2
commonly known as COVID-19 and have caused epidemics and pandemics across the globe.
For some of these diseases, proper medications, and vaccinations are missing and the early
detection of these viruses will be critical to saving the patients. And even the vaccines are
available for COVID-19; the new variants of COVID-19 such as Delta, and Omicron are
spreading at large. The available virus detection techniques take a long time, are costly, and
complex and some of them generates false negative or false positive that might cost patients
their lives. The biosensor technique is one of the best qualified to address this difficult
challenge. In this systematic review, we have summarized recent advancements in biosensorbased detection of these pandemic viruses including COVID-19. Biosensors are emerging as
efficient and economical analytical diagnostic instruments for early-stage illness detection.
They are highly suitable for applications related to healthcare, wearable electronics, safety,
environment, military, and agriculture. We strongly believe that these insights will aid in the
study and development of a new generation of adaptable virus biosensors for fellow
researchers.
“High-Dimensional Quadratic Discriminant Analysis Under Spiked Covariance
Model”, Houssem Sifaou, Abla Kammoun, and Mohamed-Slim Alouini, Quadratic
discriminant analysis (QDA) is a widely used classification technique that generalizes the
linear discriminant analysis (LDA) classifier to the case of distinct covariance matrices
among classes. For the QDA classifier to yield high classification performance, an accurate
estimation of the covariance matrices is required. Such a task becomes all the more
challenging in high dimensional settings, wherein the number of observations is comparable
with the feature dimension. A popular way to enhance the performance of QDA classifier
under these circumstances is to regularize the covariance matrix, giving the name regularized
QDA (R-QDA) to the corresponding classifier. In this work, we consider the case in which
the population covariance matrix has a spiked covariance structure, a model that is often
assumed in several applications. Building on the classical QDA, we propose a novel
quadratic classification technique, the parameters of which are chosen such that the fisherdiscriminant ratio is maximized. Numerical simulations show that the proposed classifier not
only outperforms the classical R-QDA for both synthetic and real data but also requires lower
computational complexity, making it suitable to high dimensional settings.
“Atlas of currently available human neutralizing antibodies against SARS-CoV-2
and
escape
by
Omicron
sub-variants
BA.1/BA.1.1/BA.2/BA.3”,
Min Huang,
Lili Wu, Anqi Zheng, Yufeng Xie, Qingwen He, SARS-CoV-2 Omicron variant has
presented significant challenges to current antibodies and vaccines. Herein, we systematically
compared the efficacy of 50 human monoclonal antibodies (mAbs), covering the seven
identified epitope classes of the SARS-CoV-2 RBD, against Omicron sub-variants BA.1,
BA.1.1, BA.2, and BA.3. Binding and pseudovirus-based neutralizing assays revealed that 37
of the 50 mAbs lost neutralizing activities, whereas the others displayed variably decreased
activities against the four Omicron sub-variants. BA.2 was found to be more sensitive to
RBD-5 antibodies than the other sub-variants. Furthermore, a quaternary complex structure
of BA.1 RBD with three mAbs showing different neutralizing potencies against Omicron
provided a basis for understanding the immune evasion of Omicron sub-variants and revealed
the lack of G446S mutation accounting for the sensitivity of BA.2 to RBD-5 mAbs. Our
results may guide the application of the available mAbs and facilitate the development of
universal therapeutic antibodies and vaccines against COVID-19.
“The Evolving COVID-19: Omicron”, Md Sadique Hussain, Swati Tyagi, Gurleen
Kaur, Gurusha Bahl, Because of its infectious and vaccine escape mutations, the newest
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant Omicron (B.1.1.529)
has sparked worldwide fear. The SARS-CoV-2 variant's critical infectivity and antibody
resistance are dictated by mutations in the spike (S) protein receptor-binding region. Omicron
is a highly divergent variation with a large number of mutations, including 26-32 mutations
in the spike protein, some of which are linked to humoral immune escape potential and
increased transmissibility. The Omicron variation is made up of four lineages: B.1.1.529,
BA.1, BA.2, and BA.3. Based on the information now available, the total risk associated with
Omicron continues quite significant. Omicron has a positive growth benefit over Delta,
resulting in fast community expansion and greater levels of incidence than previously
reported in this pandemic. Despite a lower risk of severe disease and death after infection
than previous SARSCoV-2 variants, the very high levels of transmission have resulted in
increases in hospitalization, continue to place overwhelming demands on healthcare systems
in most countries, and may result in significant morbidity, particularly in vulnerable
populations.
“Potent cross-reactive antibodies following Omicron breakthrough in vaccines”,
Rungtiwa Nutalai, Daming Zhou, Aekkachai Tuekprakhon, Piyada Supasa,
Highly
transmissible Omicron variants of SARS-CoV-2 currently dominate globally. Here, we
compare neutralization of Omicron BA.1, BA.1.1, and BA.2. BA.2 RBD has slightly
higher ACE2 affinity than BA.1 and slightly reduced neutralization by vaccine serum,
possibly associated with its increased transmissibility. Neutralization differences between
sub-lineages for mAbs (including therapeutics) mostly arise from variation in residues
bordering the ACE2 binding site; however, more distant mutations S371F (BA.2) and R346K
(BA.1.1) markedly reduce neutralization by therapeutic antibody Vir-S309. In-depth
structure-and-function analyses of 27 potent RBD-binding mAbs isolated from vaccinated
volunteers following breakthrough Omicron-BA.1 infection reveals that they are focused in
two main clusters within the RBD, with potent right-shoulder antibodies showing increased
prevalence. Selection and somatic maturation have optimized antibody potency in lessmutated epitopes and recovered potency in highly mutated epitopes. All 27 mAbs potently
neutralize early pandemic strains, and many show broad reactivity with variants of concern.
“Neutralizing immunity in vaccine breakthrough infections from the SARS-CoV-2
Omicron and Delta variants”, Venice Servellita, Abdullah M. Syed, Mary Kate Morris,
and Noah Brazer, Virus-like particle (VLP) and live virus assays were used to investigate
neutralizing immunity against Delta and Omicron SARS-CoV-2 variants in 259 samples from
128 vaccinated individuals. Following Delta breakthrough infection, titers against WT rose
57-fold and 3.1-fold compared with uninfected boosted and unboosted individuals,
respectively, versus only a 5.8-fold increase and 3.1-fold decrease for Omicron breakthrough
infection. Among immune-competent, unboosted patients, Delta breakthrough infections
induced 10.8-fold higher titers against WT compared with Omicron (p = 0.037).
Decreased antibody responses in Omicron breakthrough infections relative to Delta were
potentially related to a higher proportion of asymptomatic or mild breakthrough infections
(55.0% versus 28.6%, respectively), which exhibited 12.3-fold lower titers against WT
compared with moderate to severe infections (p = 0.020). Following either Delta or Omicron
breakthrough infection, limited variant-specific cross-neutralizing immunity was observed.
These results suggest that Omicron breakthrough infections are less immunogenic than Delta,
thus providing reduced protection against reinfection or infection from future variants.
Analyzing from the various above sources many contributions have been done towards
developing this idea. The Authors have used various algorithms and concepts to make out to
bring the outcome effectively. Algorithms like MLP algorithm, Gaussian sum Estimation
Algorithm, K Means Clustering, Classifier Decision Rule, Random Forest, Linear
Regression, Decision Tree and many other algorithmic approaches in bringing up solution for
this problem. There are many complexity’s made in some cases mentioned above. In our
project we will be using five algorithms to develop this. The five algorithms mainly used are
Logistic Regression, Linear Regression, Multinomial NB Classifier, Random Forest and
Decision tree. This type of method is not exactly proposed in any of the cases above so we
will be taking up this path to do our project and get effective and considerable output from
this.