On the hardness of learning sparse parities

This work investigates the hardness of computing sparse solutions to systems of linear equations over F_2. Consider the k-EvenSet problem: given a homogeneous system of linear equations over F_2 on n variables, decide if there exists a nonzero solution of Hamming weight at most k (i.e. a k-sparse solution). While there is a simple O(n^{k/2})-time algorithm for it, establishing fixed parameter intractability for k-EvenSet has been a notorious open problem. Towards this goal, we show that unless k-Clique can be solved in n^{o(k)} time, k-EvenSet has no poly(n)2^{o(sqrt{k})} time algorithm and no polynomial time algorithm when k = (log n)^{2+eta} for any eta > 0. Our work also shows that the non-homogeneous generalization of the problem -- which we call k-VectorSum -- is W[1]-hard on instances where the number of equations is O(k log n), improving on previous reductions which produced Omega(n) equations. We also show that for any constant eps > 0, given a system of O(exp(O(k))log n) linear equations, it is W[1]-hard to decide if there is a k-sparse linear form satisfying all the equations or if every function on at most k-variables (k-junta) satisfies at most (1/2 + eps)-fraction of the equations. In the setting of computational learning, this shows hardness of approximate non-proper learning of k-parities. In a similar vein, we use the hardness of k-EvenSet to show that that for any constant d, unless k-Clique can be solved in n^{o(k)} time there is no poly(m, n)2^{o(sqrt{k}) time algorithm to decide whether a given set of m points in F_2^n satisfies: (i) there exists a non-trivial k-sparse homogeneous linear form evaluating to 0 on all the points, or (ii) any non-trivial degree d polynomial P supported on at most k variables evaluates to zero on approx. Pr_{F_2^n}[P(z) = 0] fraction of the points i.e., P is fooled by the set of points

Functional Exploration of Antisense Long Non-Coding RNAs Containing Transposable Elements: A Bioinformatics Approach

Long non-coding RNA (lncRNAs) show a wide range of regulatory functions at the transcriptional and post-transcriptional levels both in the nucleus and cytoplasm. Recently, antisense lncRNAs (ASlncRNAs) were reported to up-regulate protein synthesis post-transcriptionally through a mechanism depending on an embedded inverted SINE B2 and 5' overlap to the target mRNAs. Such ASlncRNAs are also referred as SINEUPs. Synthetic SINEUPs with identical modular organization were also demonstrated to exert the same activity suggesting a functional relationship between SINE repetitive elements and ASlncRNAs. In order to gain a broader insight on the contribution of transposable elements (TEs) in the sequence composition of ASlncRNAs, I have developed a bioinformatic pipeline that can identify and characterize transcripts containing TEs and analyze TEs coverage for different classes of coding/non-coding sense/antisense (S/AS) pairs. I aimed at identifying if the functional activity of SINEUPs could be a widespread phenomenon across multiple similar natural ASlnRNAs in the transcriptomes of the extensively studied model organisms that have a well annotated catalog of IncNRAs. From my initial analysis I identified human and mouse are the two species that showed a significant coverage enrichment of SINE repeats among ASlncRNAs. I further performed several functional enrichment analysis for the sense coding genes overlapping to ASlncRNAs taking into consideration of different characteristics of the 5' binding domain and the 3' embedded SINE repetitive elements. This permitted me to identify the effect of these modular features over the functional associations of sense coding genes. The results of the analysis showed that the products of coding genes associated to ASlncRNAs containing SINEs are significantly enriched for mitochondrial localization. Further, to determine if these ASlncRNAs could exert SINEUP-like activity during stress, I analyzed the data from a published custom microarray experiment study, that were associated to the polysome fractions of MRC5 cell lysates in control and oxidative stress condition. The results revealed that the ASlncRNA carrying inverted or direct SINE repeats and their corresponding sense coding genes do not show any significant differential polysome loading in stress with respect to normal conditions, which is not a desired characteristic of a potential SINEUP. However, ASlncRNAs with inverted and direct SINE repeats corresponding to high translating polysome fractions showed a significantly higher ratio of means for RNA levels in stress over control, in contrast to noASlncRNA. This suggests that the ASlncRNA containing SINE elements are the key RNA molecules that are active during stress, although to determine if they are also involved in the increased polysome loading of their respective sense coding mRNAs, there is a need of further experimentation and exploration. Altogether, the work presented in this thesis provides a novel bioinformatics approach to study transcriptome-wide ASlncRNAs containing TEs and their functional association over the sense coding genes, and discover new significant functional features of ASlncRNA to be biologically validated

Cor triatriatum sinister with situs inversus totalis in an infant.

Cor triatriatum sinister is a rare congenital cardiac malformation characterized by a membrane in the left atrium which separates the left atrium into the proximal and distal chambers.Association of cor triatriatum is extremely rare with situs inversus totalis. This article reports a rare case of cor triatriatum sinister with situs inversus totalis in a 5 month old female infantpeer-reviewe

AN EXPERIMENTAL STUDY TOWARDS UNDERWATER PROPULSION SYSTEM USING STRUCTURE BORNE TRAVELING WAVES

The method of generating steady-state structure-borne traveling waves underwater in an infinite media creates abundant opportunities in the field of propulsive applications, and they are gaining attention from several researchers. This experimental study provides a framework for harnessing traveling waves in a 1D beam immersed under quiescent water using two force input methods and providing a motion to an object floating on the surface of the water. In this study, underwater traveling waves are tailored using structural vibrations at five different frequencies in the range of 10Hz to 300Hz. The resulting fluid motion provides a propulsive thrust that moves a 3D-printed bob floating on the surface of the water. The undulatory motion of the floating bob is determined using an image processing approach. In this approach, videos are recorded for image processing to determine the effects of each traveling wave frequency on the object’s motion. Through image processing, observations are drawn regarding the velocity and the distance traveled by the bob for each SBTW frequency. As this is developing research, there is a limited understanding to the relationship between the amplitude of force input, the traveling wave frequency, and the velocity attained by the object. So, with the help of image processing, a general observation about the effects of varied force input on the motion of the object at each frequency is drawn

Design & Development of Three Roller Sheet Bending Machine

Metal forming can be defined as a process in which the desired size and shapes are obtained through plastic deformation of a material without any significance loss of material. Bending is a metal forming process in which straight length is transformed into a curved length. Roller forming is a continuous bending operation in which a long strip of metal is passed through consecutive sets of rollers, until the desired cross sectional profile is obtained. The roller bending process usually produces larger parts of cylindrical or conical cross sections in large quantity. Normal practice of the roller bending still heavily depends upon the experience and skill of the operator. Trial and Error is a common practice in the industry. Rolling process always began with crucial operation of pre bending both ends of the work piece. This operation eliminates flat spot when rolling a full cylindrical shape and ensures better closure. DOI: 10.17762/ijritcc2321-8169.15081

Accuracy of pulse oximetry screening for detecting critical congenital heart disease in the newborns in rural hospital of Central India

Congenital cardiovascular malformations are the most common category of birth defects and responsible for mortality in the first twelve months of life. Critical congenital heart disease (CCHD) will be present in approximately one quarter of these children, which requires catheter or surgery intervention in the first year of life. The aim is to determine the accuracy of pulse oximetry for detecting clinically unrecognized CCHD in the newborns. This article reports the following methods : Pulse oximetry was performed on clinically normal newborns within first 4 hours of life. If screening oxygen saturation (SpO2) was below 90%, echocardiography was then performed. Inclusion criteria: All newborns who were admitted in postnatal ward & NICU. Exclusion criteria: Out born babies and babies with a prenatal diagnosis of duct dependent circulation.peer-reviewe

Brain Tumor Detection by using Fine-tuned MobileNetV2 Deep Learning Model

Most of the deaths in the world happen due to Cancer. It is a disease in which the cells of our body organs or tissues grow in an undisciplined way which in turn can harm our normal cells and tissues in our body. These cells very smartly trick the immune system so that the cancerous cells are kept alive and are not destroyed. In the human body, tumors can be classified into three types: cancerous, non-cancerous, and pre-cancerous. Timely identification of the cancer can be helpful in many ways. As it improves a patient’s chances of survival. The most valuable, uncomplicated technique used is MRI scans for predicting tumor is a tough task and have chances of human error. So to be more accurate with our predictions we have moved on to use computerized techniques to ease the work. The focus of this research is the development of an automated brain tumor classification system using magnetic resonance imaging (MRI) scans, leveraging a deep learning model. The proposed model employs a convolutional neural network (CNN) architecture known as MobileNetV2, which is trained on a pre-processed MRI image dataset to classify brain tumors into one of two categories: tumor tissues and normal brain tissue. To mitigate overfitting and expand the dataset, data augmentation techniques are employed. The trained model achieves high accuracy, sensitivity, and specificity in classifying brain tumors. Proposed CNN model outperformed other deep learning models, including VGG16, Xception, and ResNet50, which were used for comparison

Improving Data Security in Public Cloud Storage with the Implementation of Data Obfuscation and Steganography Techniques

Cloud computing is a widely used distribution paradigm for delivering secure information services over the internet. The advantages of cloud computing include the capacity to remotely access one's data from any location, eliminating concerns over data backups, as well as the establishment of disaster recovery and business continuity facilities. Nevertheless, cloud computing gives rise to apprehensions over the appropriate management of information and interactions by cloud service providers, user organisations, and governments. Cloud computing has become an increasingly popular choice for both big organisations and individuals seeking cost-effective access to a wide range of network services. Typically, individuals' information is kept on a public Cloud, which is accessible to everybody. This basic gives rise to several concerns that are contrary to the adaptable services offered by cloud providers, such as Confidentiality, Integrity, Availability, Authorization, and others. Currently, there are several choices available for safeguarding data, with encryption being the most favoured one. Encryption alone is insufficient for adequately safeguarding the sensitive information of many users. Additionally, the encryption and decryption procedure for each every query requires a greater amount of time. Furthermore, it is not advisable to just prioritise user-centric thinking, since users relinquish direct control over their data once it is uploaded to Cloud premises. Given this reality, it is important to contemplate the security of users' vital information on the Cloud server. This may be achieved by the use of the crucial method known as obfuscation. In order to alleviate the load on the Cloud server and provide sufficient security for user data, we suggest an approach that combines both strategies, namely... The thesis explores the concepts of obfuscation and encryption. If the files or documents need security, the user data may be encrypted. The Cloud's DaaS service is protected utilising obfuscation methods. By using a dual-pronged strategy, the suggested technique provides enough protection for anonymous access and ensures the preservation of privacy, even while dealing with information stored on Cloud servers. The objective is to provide a robust integrity checking method, an enhanced access control mechanism, and a group sharing mechanism. These improvements seek to reduce the workload and foster a higher degree of confidence between clients and service providers

Tuberculosis Disease Detection through CXR Images based on Deep Neural Network Approach

Tuberculosis (TB) is a disease that, if left untreated for an extended period of time, can ultimately be fatal. Early TB detection can be aided by using a deep learning ensemble. In previous work, ensemble classifiers were only trained on images that shared similar characteristics. It is necessary for an ensemble to produce a diverse set of errors in order for it to be useful; this can be accomplished by making use of a number of different classifiers and/or features. In light of this, a brand-new framework has been constructed in this study for the purpose of segmenting and identifying TB in human Chest X-ray. It was determined that searching traditional web databases for chest X-ray was necessary. At this point, we pass the photos that we have collected over to Swin ResUnet3 so that they may be segmented. After the segmented chest X-ray have been provided to it, the Multi-scale Attention-based Densenet with Extreme Learning Machine (MAD-ELM) model will be applied in the detection stage in order to effectively diagnose tuberculosis from human chest X-ray. This will be done in order to maximize efficiency. Because it increased the variety of errors made by the basic classifiers, the supplied variation of the approach that was proposed was able to detect tuberculosis more effectively. The proposed ensemble method produced results with an accuracy of 94.2 percent, which are comparable to those obtained by past efforts