Block-wise Approach Research Articles

Does Family Functioning Matter? Understanding the Relationship Between Family Interactions and Depressive Symptoms for Caregivers of Cancer Patients.

Caregivers of cancer patients are at increased risk of depression and other health challenges. There is limited understanding of the role of the caregiver's own family members in promoting or discouraging mental wellbeing. Family functioning conceptualizes how family members interact to promote a positive family environment and has the potential to impact caregiver mental health. The purpose of this study is to assess the association between family interactions and depressive symptoms among family caregivers of cancer patients. Secondary analysis of baseline data from an NIH-funded randomized control trial of family caregivers of cancer patients recruited from academic palliative care clinics at three sites (2 Midwest, 1 East). We tested for an association between caregiver responses to the Family Quality of Life in Dementia-Family Interactions Subscale and Patient-Reported Outcomes Measurement Information System Depression Short Form 8A using a block-wise approach to linear modeling. A total of 246 caregivers were included in analysis; caregivers were mostly White (82%), not Hispanic or Latina/o (96%), and female (65%), with an average age of 55years. Overall, participants had high family interactions (mean 57.7, sd 11.7) and an average depressive symptom burden (t-score 52.4, SD 8.57). Family interactions was significantly negatively associated with depressive symptoms (b = -.163, se = .057) when accounting for relevant covariates. Family caregivers with more positive family interactions are likely to have fewer depressive symptoms, suggesting family functioning may play a key role in bolstering family caregiver mental health and wellbeing.

Family Caregiver Communication and Perceptions of Involvement in Hospice Care.

Background: The burden of caregiving for family members is significant and becomes particularly challenging at end of life, with negative effects on mental health, including anxiety and depression. Research has shown caregivers need better communication with their health care team. Objectives: To evaluate the relationship between hospice team communication with caregivers and caregiver involvement in care. Methods: The purpose of this secondary analysis of data collected from a U.S.-based cluster crossover randomized trial was to evaluate whether caregiver-centered communication (Caregiver-Centered Communication Questionnaire) is associated with a caregiver's perceptions of involvement in care (Perceived Involved in Care Scale). A block-wise approach was used to estimate linear models, which were created using total scores and subscale scores. Results: Caregiver-centered communication was positively associated with perceptions of involvement in care. Conclusion: Skilled communication between hospice clinicians and family caregivers is critical in helping family members perception they are involved in the care of their loved one. There could be similar benefit in caregiver-centered communication during cancer treatment as well.

Correction to: A paced multi-stage block-wise approach for object detection in thermal images

Correction to: A paced multi-stage block-wise approach for object detection in thermal images

A paced multi-stage block-wise approach for object detection in thermal images.

The growing advocacy of thermal imagery in applications, such as autonomous vehicles, surveillance, and COVID-19 detection, necessitates accurate object detection frameworks for the thermal domain. Conventional methods could fall short, especially in situations with poor lighting, for instance, detection during night-time. In this paper, we propose a paced multi-stage block-wise framework for effectively detecting objects from thermal images. Our approach utilizes the pre-existing knowledge of deep neural network-based object detectors trained on large-scale natural image data to enhance performance in the thermal domain constructively. The employed, multi-stage approach drives our model to achieve higher accuracies. And the introduction of the pace parameter during domain adaption enables efficient training. Our experimental results demonstrate that the framework outperforms previous benchmarks on the FLIR ADAS dataset on the person, bicycle, and car categories. We have also illustrated further analysis of the framework, such as the effect of its components on accuracy and training efficiency, its generalizability to other thermal datasets, and its superior performance on night-time images in contrast to state-of-the-art RGB object detectors.

Engineering In-place (Shared-memory) Sorting Algorithms

We present new sequential and parallel sorting algorithms that now represent the fastest known techniques for a wide range of input sizes, input distributions, data types, and machines. Somewhat surprisingly, part of the speed advantage is due to the additional feature of the algorithms to work in-place, i.e., they do not need a significant amount of space beyond the input array. Previously, the in-place feature often implied performance penalties. Our main algorithmic contribution is a blockwise approach to in-place data distribution that is provably cache-efficient. We also parallelize this approach taking dynamic load balancing and memory locality into account. Our new comparison-based algorithm In-place Parallel Super Scalar Samplesort ( IPS 4 o ) , combines this technique with branchless decision trees. By taking cases with many equal elements into account and by adapting the distribution degree dynamically, we obtain a highly robust algorithm that outperforms the best previous in-place parallel comparison-based sorting algorithms by almost a factor of three. That algorithm also outperforms the best comparison-based competitors regardless of whether we consider in-place or not in-place, parallel or sequential settings. Another surprising result is that IPS 4 o even outperforms the best (in-place or not in-place) integer sorting algorithms in a wide range of situations. In many of the remaining cases (often involving near-uniform input distributions, small keys, or a sequential setting), our new In-place Parallel Super Scalar Radix Sort ( IPS 2 Ra ) turns out to be the best algorithm. Claims to have the – in some sense – “best” sorting algorithm can be found in many papers which cannot all be true. Therefore, we base our conclusions on an extensive experimental study involving a large part of the cross product of 21 state-of-the-art sorting codes, 6 data types, 10 input distributions, 4 machines, 4 memory allocation strategies, and input sizes varying over 7 orders of magnitude. This confirms the claims made about the robust performance of our algorithms while revealing major performance problems in many competitors outside the concrete set of measurements reported in the associated publications. This is particularly true for integer sorting algorithms giving one reason to prefer comparison-based algorithms for robust general-purpose sorting.

Pliable lasso for the support vector machine

In this article, we study the support vector machine with interaction effects. The pliable lasso penalty, which allows for estimating the main effects of the covariates X and the interaction effects between the covariates and a set modifiers Z is implemented to handle the interaction effect. Interaction variables are included in a hierarchical manner by first considering whether their corresponding main effect variables have been included in the model to avoid over-fitting. The loss function employed is the squared hinge loss, with the pliable lasso penalty and then, the block-wise coordinate descent approach is employed. The results from the simulation and real data show the effectiveness of the pliable lasso in building support vector machine models in situations where interaction effects are involved.

A new approach to breast cancer analysis through histopathological images using MI, MD binary, and eight class classifying techniques

Globally, cancer is the second deadliest disease in the world. The most familiar type of cancers affecting human beings is lung, colorectal, breast, cervical, stomach, thyroid, and stomach cancer. An individual affected by cancer suffers from physical, emotional, and financial restrain. The proper maintenance of the health system with early detection reduces the cancer death rate. Breast cancer is the most serious crisis that affects a major percentage of women. The superfluous intensification of cells in the breast is the main reason for breast cancer. The histopathological images are very useful in the diagnosis of breast cancer. However, it is a time-intensive and tedious task requiring long years of pathologist training. Today’s digital advancements can be exploited for automating this task. This paper presents a new learning approach for automating breast cancer Classifying is histopathological images into magnification independent (MI) and magnification dependent (MD) eight-class and binary classifying. For learning, we use the deep neural network-ResNet-18 and this is provided with Image Net training. The proposed learning technique mainly depends on the accurate tuning of the block-wise approach in which the deep network model's two terminal residual blocks can be educated. Additionally, we reinforced the proposed approach’s adaptability with Global Contrast Normalization and a three-fold rise in details concerning training data. The evaluation is carried out on Break His dataset. The results protrude our proposed technique to be effective in classifying breast cancer histopathological images into MI and MD eight-class and binary classes. It outperformed 11 renowned existing techniques.

Blockwise Euclidean likelihood for spatio-temporal covariance models

A spatio-temporal blockwise Euclidean likelihood method for the estimation of covariance models when dealing with large spatio-temporal Gaussian data is proposed. The method uses moment conditions coming from the score of the pairwise composite likelihood. The blockwise approach guarantees considerable computational improvements over the standard pairwise composite likelihood method. In order to further speed up computation, a general purpose graphics processing unit implementation using OpenCL is implemented. The asymptotic properties of the proposed estimator are derived and the finite sample properties of this methodology by means of a simulation study highlighting the computational gains of the OpenCL graphics processing unit implementation. Finally, there is an application of the estimation method to a wind component data set.

Convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product

Modeling the performance and energy consumption of the sparse matrix-vector product (SpMV) is essential to perform off-line analysis and, for example, choose a target computer architecture that delivers the best performance-energy consumption ratio. However, this task is especially complex given the memory-bounded nature and irregular memory accesses of the SpMV, mainly dictated by the input sparse matrix. In this paper, we propose a Machine Learning (ML)-driven approach that leverages Convolutional Neural Networks (CNNs) to provide accurate estimations of the performance and energy consumption of the SpMV kernel. The proposed CNN-based models use a blockwise approach to make the CNN architecture independent of the matrix size. These models are trained to estimate execution time as well as total, package, and DRAM energy consumption at different processor frequencies. The experimental results reveal that the overall relative error ranges between 0.5% and 14%, while at matrix level is not superior to 10%. To demonstrate the applicability and accuracy of the SpMV CNN-based models, this study is complemented with an ad-hoc time-energy model for the PageRank algorithm, a popular algorithm for web information retrieval used by search engines, which internally realizes the SpMV kernel.

Pliable lasso for the multinomial logistic regression

In this paper, we study the multinomial logistic regression with interactive effects. Our approach involves the implementation of the pliable lasso penalty which allows for estimating the main effects of the covariates X and an interaction effects between the covariates and a set modifiers Z. The hierarchical penalty helps to avoid over-fitting by excluding the interaction effects when the corresponding main effects are zero. The original log-likelihood model is transformed into an iteratively reweighted least square problem with the pliable lasso penalty and then, the block-wise coordinate descent approach is employed. Our results show that the pliable lasso for multinomial logistic regression has some good qualities and can perform well in multi-classification problems which involve interactive variables.

Fingerprint pores extraction by using automatic scale selection

Extraction of fingerprint sweat pores is a critical step in those applications which are based on highly secured features. Pores are varying in scale (size) and evenly distributed along the ridges. It is the main challenge to design a technique which determines the pores of different sizes in the fingerprint image. In this paper, pore extraction algorithm is proposed for high-resolution fingerprint images which utilised multiscale γ-normalised Laplacian of Gaussian (LoG) filter. A block-wise approach is implemented in which each region is filtered at multiple scale values. Scale space theory is applied and candidate pixels of high negative response are identified through local maxima approach. The efficacy of the proposed algorithm is tested by measuring average true detection rate (TDR) and average false detection rate (FDR). Results of the proposed algorithm achieve average TDR and average FDR values as 82.89% and 21.2% respectively which are better in comparison to the state-of-art techniques.

Fingerprint pores extraction by using automatic scale selection

Extraction of fingerprint sweat pores is a critical step in those applications which are based on highly secured features. Pores are varying in scale (size) and evenly distributed along the ridges. It is the main challenge to design a technique which determines the pores of different sizes in the fingerprint image. In this paper, pore extraction algorithm is proposed for high-resolution fingerprint images which utilised multiscale γ-normalised Laplacian of Gaussian (LoG) filter. A block-wise approach is implemented in which each region is filtered at multiple scale values. Scale space theory is applied and candidate pixels of high negative response are identified through local maxima approach. The efficacy of the proposed algorithm is tested by measuring average true detection rate (TDR) and average false detection rate (FDR). Results of the proposed algorithm achieve average TDR and average FDR values as 82.89% and 21.2% respectively which are better in comparison to the state-of-art techniques.

Secure Secret Image Sharing using Histogram Localization Based Block wise Methodology

Various types of encryption techniques have been used for data security. Complex algorithms are followed for this purpose in most of the methods. Confidentiality is provided by a technique called visual cryptography excluding of any intricate computations and algorithms. In this paper, we put forward an innovative method called Histogram Localization based Blockwise (HLB) approach to solve pixel expansion problem at the same time it will increase the security and visual quality for highly confidential secret image which is in color. According to this method, the secret shares are generated based on the histogram and also the number of black pixels in the corresponding block. The image size of resultant shares is same as in the original secret image. These shares are distributed to participants, and human vision system is used for decryption purpose.

Statistical Inconsistency of Maximum Parsimony for k-Tuple-Site Data.

One of the main aims of phylogenetics is to reconstruct the "Tree of Life." In this respect, different methods and criteria are used to analyze DNA sequences of different species and to compare them in order to derive the evolutionary relationships of these species. Maximum parsimony is one such criterion for tree reconstruction, and it is the one which we will use in this paper. However, it is well known that tree reconstruction methods can lead to wrong relationship estimates. One typical problem of maximum parsimony is long branch attraction, which can lead to statistical inconsistency. In this work, we will consider a blockwise approach to alignment analysis, namely the so-called k-tuple analyses. For four taxa, it has already been shown that k-tuple-based analyses are statistically inconsistent if and only if the standard character-based (site-based) analyses are statistically inconsistent. So, in the four-taxon case, going from individual sites to k-tuples does not lead to any improvement. However, real biological analyses often consider more than only four taxa. Therefore, we analyze the case of five taxa for 2- and 3-tuple-site data and consider alphabets with two and four elements. We show that the equivalence of single-site data and k-tuple-site data then no longer holds. Even so, we can show that maximum parsimony is statistically inconsistent for k-tuple-site data and five taxa.

Identification of person or data using modified square blockwise approach

Identification of person or data using modified square blockwise approach

Identification of person or data using modified square blockwise approach

Nowadays authentication is important to identify the originality of a person or document. Visual secret sharing scheme is one of the best methods to provide authentication without any complex computations. In this paper, we proposed novel method called modified square blockwise approach to generate shares. Here the personage photo or any biometric data can be used as an authenticated image. This approach will generate two shares, one share is printed on identity card and the other one is stored on database. To verify the originality of a document or a person, first receive the identity card and scan or take a photo of the share. Now this share is compared with the stored one, if it is reveal the authenticated image then it is accepted, otherwise simply rejected. This approach is used to authenticate any confidential data such as medical document, bank details and administration details also. Using this approach, we can restrict the use of confidential documents without knowing the originality of the document.

Scalable non-negative matrix tri-factorization

BackgroundMatrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining. Matrix factorization learns a latent data model that takes a data matrix and transforms it into a latent feature space enabling generalization, noise removal and feature discovery. However, factorization algorithms are numerically intensive, and hence there is a pressing challenge to scale current algorithms to work with large datasets. Our focus in this paper is matrix tri-factorization, a popular method that is not limited by the assumption of standard matrix factorization about data residing in one latent space. Matrix tri-factorization solves this by inferring a separate latent space for each dimension in a data matrix, and a latent mapping of interactions between the inferred spaces, making the approach particularly suitable for biomedical data mining.ResultsWe developed a block-wise approach for latent factor learning in matrix tri-factorization. The approach partitions a data matrix into disjoint submatrices that are treated independently and fed into a parallel factorization system. An appealing property of the proposed approach is its mathematical equivalence with serial matrix tri-factorization. In a study on large biomedical datasets we show that our approach scales well on multi-processor and multi-GPU architectures. On a four-GPU system we demonstrate that our approach can be more than 100-times faster than its single-processor counterpart.ConclusionsA general approach for scaling non-negative matrix tri-factorization is proposed. The approach is especially useful parallel matrix factorization implemented in a multi-GPU environment. We expect the new approach will be useful in emerging procedures for latent factor analysis, notably for data integration, where many large data matrices need to be collectively factorized.

Function-spacer-lipid constructs of Lewis and chimeric Lewis/ABH glycans. Synthesis and use in serological studies

Seven lipophilic constructs containing Lewis (Lea, Leb, Ley) or chimeric Lewis/ABH (ALeb, BLeb, ALey, BLey) glycans were obtained starting from corresponding oligosaccharides in form of 3-aminopropyl glycosides. ALeb and BLeb pentasaccharides were synthesized via [3 + 1] blockwise approach. The constructs (neoglycolipids, or FSLs) were inserted in erythrocyte membrane, and obtained “kodecytes” were used to map the immunochemical specificity of historical and contemporary monoclonal and polyclonal blood group system Lewis reagents.

Sparsity-aware target localization using TDOA/AOA measurements in distributed MIMO radars

Abstract In this paper, a sparsity-aware hybrid target localization method in multiple-input-multiple-output (MIMO) radars from time difference of arrival (TDOA) and angle of arrival (AOA) measurements is proposed. This method provides a maximum likelihood estimate of target position by employing compressive sensing techniques. A blockwise approach is addressed in order to achieve better accuracy for a constant computational complexity. The mismatch problem due to grid discretization is also tackled by a dictionary learning technique. The Cramer–Rao lower bound for this model is derived as a benchmark. Numerical simulations are included to corroborate the theoretical developments.

Long-Term Stationary Object Detection Based on Spatio-Temporal Change Detection

We present a block-wise approach to detect stationary objects based on spatio-temporal change detection. First, block candidates are extracted by filtering out consecutive blocks containing moving objects. Then, an online clustering approach groups similar blocks at each spatial location over time via statistical variation of pixel ratios. The stability changes are identified by analyzing the relationships between the most repeated clusters at regular sampling instants. Finally, stationary objects are detected as those stability changes that exceed an alarm time and have not been visualized before. Unlike previous approaches making use of Background Subtraction, the proposed approach does not require foreground segmentation and provides robustness to illumination changes, crowds and intermittent object motion. The experiments over an heterogeneous dataset demonstrate the ability of the proposed approach for short- and long-term operation while overcoming challenging issues.