Miracle-dz/UniFAR-FTUs huggingface dataset:
Representation of Chromosome Conformations Using a Shape Alphabet Across Modeling Methods

Despite enormous structural variability exhibited in 3D chromosomal conformations at a global scale, there is a significant commonality of structures visible at smaller, local levels. We hypothesize that chromosomal conformations are representable as concatenations of a handful of prototypical shapelets, termed shape letters. This is akin to expressing complicated sentences in a language using only a small set of letters. Our goal is to organize the vast variability of 3D chromosomal conformation by constructing a set of predominant shape letters, termed a shape alphabet, using statistical shape analysis of curvelets taken from training conformations. This paper utilizes conformations generated from Integrative Genome Modeling to develop a shape alphabet as follows: it first segments 3D conformations into curvelets according to their Topologically Associated Domains. It then clusters these segments, estimates mean shapes, and refines and reorders these shapes into a Chromosome Shape Alphabet. The paper demonstrates effectiveness of this construction by successfully representing independent test conformations taken from IGM and other methods such as SIMBA3D, both symbolically and structurally, using the constructed alphabet.
Empirical analysis of the residual information needed for image classification

We study the limits of classification models applied to an abstract representation of data.Previous research has dealt with the classification of images generated from data, which are abstract pictures that greatly differ from standard real-world images. This approach might be robust to the curse of dimensionality and we wish to apply it to multi-dimensional data. Current image classification methods have been proven to be effective when applied to real-world images. We want to know how these same methods perform on abstract images, i.e. if they can work and if so, what are the necessary characteristics that the images must have for an optimized and efficient learning? We propose different empirical experiments in order to define the residual information rate required for image classification. This implies knowing the amount of information required for a classification model to perform well on abstract images.
Initial Evaluation of a Multilevel Inverter with Unfolding Stage for BESS Applications

Today, the integration of renewable energy sources into power grids has increased significantly. This necessitates the development of more battery energy storage systems. A crucial component in such systems is the power electronic converters, which ensure the proper connection of the batteries to the grid. These bidirectional converters should be able not only to discharge batteries to the grid, but also to charge the batteries when it is needed. Various power converters can be identified for these systems. In particular, multilevel topologies offer several advantages, such as lower harmonic distortion of the output waveforms and lower switching losses. Among all multilevel converter topologies, the multilevel converter with independent sources provides one more benefit - battery balancing is achieved naturally by swapping the module switching order. This paper evaluates the performance of a modular multilevel converter with independent sources and unfolding stage.
Transformer-based Multi-target Regression on Electronic Health Records for Primordial Prevention of Cardiovascular Disease

Machine learning algorithms have been widely used to capture the static and temporal patterns within electronic health records (EHRs). While many studies focus on the (primary) prevention of diseases, primordial prevention (preventing the factors that are known to increase the risk of a disease occurring) is still widely under-investigated. In this study, we propose a multi-target regression model leveraging transformers to learn the bidirectional representations of EHR data and predict the future values of 11 major modifiable risk factors of cardiovascular disease (CVD). Inspired by the proven results of pre-training in natural language processing studies, we apply the same principles on EHR data, dividing the training of our model into two phases: pre-training and fine-tuning. We u se t he fine-tuned transformer model in a “multi-target regression” theme. Following this theme, we combine the 11 disjoint prediction tasks by adding shared and target-specific l ayers t o t he m odel and jointly train the entire model. We evaluate the performance of our proposed method on a large publicly available EHR dataset. Through various experiments, we demonstrate that the proposed method obtains a significant improvement (12.6% MAE on average across all 11 different outputs) over the baselines.
PCAT: PLC Command Analysis Tool for automatic incidence response in Water Treatment Plants

A cyber-physical attack against critical infrastructures, such as water treatment and distribution plants, could lead to process anomalies. Several design and data centric approaches were developed to detect such anomalies when the physical processes of the underlying plant move from a normal to a malicious state. Although these approaches are necessary for the continued and reliable plant operation, they might not be sufficient to prevent service disruption or damage to components. This is because the impact of anomalies is already realized before the attack is detected. In this paper, we propose PCAT – PLC Command Analysis Tool that validates the control commands issued by the programmable logic controller (PLC). PCAT stops rogue attack commands before they reach the target actuators. In a case study, PCAT was deployed and validated on the operational water treatment plant named SWaT. The experimental results attest the performance of PCAT in ensuring the security, safety, and service status of the plant before it enters an anomalous state.
Presentation of a novel and laboratory proved method to determine efficiently the minimum deionization time in hybrid circuit breaker

The application of hybrid circuit breakers (HCB) is possible in all voltage levels of DC-grids. For high voltage direct current transmission applications, HCBs are often equipped with commutation power electronics (PE). In low voltage grids, the use of commutation PEs is mostly avoided. Thus, a longer arcing process for commutation results from the mechanical circuit breaker to the PEs. As a result, a higher energy entry of the gas within the mechanical circuit breaker occurs. However, the components and the expenditure for the control unit is significantly reduced which is highly beneficial. This contribution presents a new method to optimize the deionization time of HCBs without commutation PEs for LVDC and MVDC-grids. They are applied to different test grids to show the opportunities of this technology. To ensure a relative fast switching time, a method to determine the minimum deionization time (DI) is developed and presented in order to avoid arc reignitions. This contribution shows the functionality of this method and discusses the efficiency varying different parameters.
FREIDA - Fracture Risk Evaluation using Highly Efficient Information Retrieval and Analysis of Large Healthcare Datasets

This paper presents an approach based on optimized data search and data analysis of high dimensional clinical health records, for the purpose of identifying the patients with significant risk of fracture to be introduced to the Fracture Liaison Service (FLS) for further investigations and treatment. The main objective of the work is to speed up and automate the process of identifying the patients suitable for FLS and provide a real decision support for the clinical team by fast information retrieval and structuring. The performed analysis and the current results are promising and support our research aim for the optimization of the process in the real life scenario.
Combining Geometric and Topological Information for Boundary Estimation

We propose a method which jointly incorporates geometric and topological information to simultaneously estimate boundaries for objects in images with more complex topologies. We use a topological clustering-based method to assist the initialization of the Bayesian active contour model. When applied separately, the topological clustering is not robust to background noise, while active contour methods are known to be extremely sensitive to algorithm initialization. Our proposed topologically guided method provides an interpretable, principled initialization in these settings, which avoids potential pitfalls associated with these types of objects. We provide a simulation study comparing our initialization to boundary estimates obtained from standard segmentation algorithms on simulated images, and then demonstrate our method successfully on real-world applications with skin lesions and neural cellular images, for which multiple topological features can be identified automatically.
DFL-PiDA: Prediction of Piwi-interacting RNA-Disease Associations based on Deep Feature Learning

Piwi-interacting RNAs (piRNAs) fulfill the necessary requirements of epigenetic mechanisms, working to regulate gene expression in diseases and homeostasis in a coordinated manner. Hence, predicting new piRNAs that are associated with diseases conduces to understanding the pathogenicity mechanisms. In this study, we presented a deep feature learning model (DFLPiDA) to predict potential piRNA-disease associations based on the multi-model similarity features of piRNAs and diseases and the convolutional denoising auto-encoder. In particular, we firstly calculated four types of similarity features of piRNAs and diseases. Then, the convolutional denoising auto-encoder was utilized to perform deep learning on the fused similarity features. Finally, the extreme learning machine was employed as the training model as well as to predict unknown associations. The empirical results of five-fold cross-validation experiments show that the DFL-PiDA is efficient for predicting potential piRNA-disease associations. Furthermore, we proved the effectiveness of convolutional denoising auto-encoder neural network in piRNA and disease association prediction. Case studies also demonstrate the practical application of DFL-PiDA to discover potential associations.
DeepCOVIDNet: Deep Convolutional Neural Network for COVID-19 Detection from Chest Radiographic Images

The novel Coronavirus Disease 2019 (COVID-19) is a global pandemic that has infected millions of people causing millions of deaths around the world. Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the standard screening method for COVID-19 detection but it requires specific molecular-biology training. Moreover, the general workflow is difficult e.g. sample collection, processing time, and analysis expertise, etc. Chest radiographic image analysis can be a good alternative screening method that is faster, more efficient, and requires minimal clinical or molecular biology trained laboratory personnel. Early studies have shown that abnormalities on the chest radiographic images are likely to be the consequence of COVID-19 infection. In this study, we propose DeepCOVIDNet, a deep learning based COVID-19 detection model. Our proposed deep-learning model is a multiclass classifier that can distinguish COVID-19, viral pneumonia, bacterial pneumonia, and healthy chest X-ray images. Our proposed model classifies radiographic images into four distinct classes and achieves the accuracy of 89.47% along with a high degree of precision, recall and F1 score. On a different dataset setting (COVID-19, bacterial pneumonia, viral pneumonia) our model achieves the maximum accuracy of 98.25%. We demonstrate generalizability of our proposed method using 5-fold cross validation for COVID-19 vs pneumonia and COVID-19 vs healthy classification that also manifests promising results.
A Low Voltage High Power Bidirectional Multi-Port DC-DC Converter for DC Microgrids

Multi-port DC-DC converters (MPCs) are gaining attention in recent times as it can integrate more than one energy sources and energy storage devices into a single converter block. In this paper, a MPC topology is proposed for battery and supercapacitor-based hybrid energy storage system which is suitable for low voltage high power application in a DC Microgrid. The converter utilizes a three-phase current fed Dual Active Bridge (DAB) structure which provides galvanic isolation between the battery bank and the load. A low voltage high power supercapacitor bank is integrated at the load side by means of two stage conversion to achieve high voltage gain. The wide range zero voltage switching (ZVS) of the three-phase current fed DAB and bidirectional operation between any two ports are the key features of the proposed MPC. In addition, the input current ripple of both the storage devices are greatly reduced due to the interleaved structure. The operation of the proposed MPC and closed loop control strategy are discussed. A 20kW system is designed and simulated in the MATLAB Simulink environment to verify the proposed converter topology.
Time-Series Analysis of Gene Correlation Networks based on Single-Cell Transcriptome Data

Inflammation is an anatomical and physiological response underlying a variety of diseases, and the detection of tissue lesions without subjective symptoms is considered important for preventing of chronic inflammation. For this purpose, it is expected to be useful that recently developed technologies for obtaining single-cell transcriptome data and analytical techniques such as MAGIC and Monocle. In this study, we propose a gene correlation network based on single-cell transcriptome data as a novel analytical approach. Specifically, wep ropose a method for constructing a gene correlation network using MAGIC, and visualization and ranking methods for analyzing its time-series changes. In order to confirm the usefulness of the proposed methods, we conducted experiments using the time-sequence data of single-cell transcriptome obtained from mice induced pulmonary lung fibrosis bye xposure to silica particles. We observed significant changes in the gene correlation network consisting of “negative edges” with the progression of the disease state, as well as that characteristic gene groups with large fluctuations in ranking play important roles in the early stage of the disease.
An Interpretable Multi-Level Enhanced Graph Attention Network for Disease Diagnosis with Gene Expression Data

Clinical omics, especially gene expression data, have been widely studied and successfully applied for disease diagnosis using machine learning techniques. As genes often work interactively rather than individually, investigating co-functional gene modules can improve our understanding of disease mechanisms and facilitate disease state prediction. To this end, we in this paper propose a novel Multi-Level Enhanced Graph ATtention (MLE-GAT) network to explore the gene modules and intergene relational information contained in the omics data. In specific, we first format the omics data of each patient into co-expression graphs using weighted correlation network analysis (WGCNA) and then feed them to a well-designed multi-level graph feature fully fusion (MGFFF) module for disease diagnosis. For model interpretation, we develop a novel full-gradient graph saliency (FGS) mechanism to identify the disease-relevant genes. Comprehensive experiments show that our proposed MLE-GAT achieves state-of-the-art performance on transcriptomics data from TCGA-LGG/TCGA-GBM and proteomics data from COVID-19/non-COVID-19 patient sera.
Efficient Last-Mile Link Adaptation in Next-Gen Wireless Heterogeneous Networks

The forthcoming generations of wireless networks have a high demand for reliable and enhanced data rate transmissions which are crucial for emerging smart cities. Thus, there is a need to develop efficient Link Adaptation (LA) for the last hop between end-users and the base stations in order to mitigate the severe interference resulting from the dense heterogeneous networks. We develop a Machine Learning (ML) based last-mile link adaptation method for a 5G wireless communication network. Dynamic selection of MCS for Resource Block (RB) allocation is efficient in terms of better network throughput and reduced BER which is verified through simulations for 5G New Radio (NR). We assume perfect channel estimation in our analysis. We have used a Deep Neural Network (DNN) model that dynamically selects the appropriate Modulation and Coding Scheme (MCS) ensuring 10 percent Bit Error Rate (BER) and maximizes the system spectral efficiency. Further, we evaluate the Signal to Interference and Noise Ratio (SINR) corresponding to varied channel states for different frequencies of operation and the DNN model selects the Channel Quality Indicator (CQI) corresponding to the optimal MCS available at the corresponding base stations for the end -users. This results in seamless connectivity for mobile users adapting to the last-mile link efficiently and achieving a higher downlink network throughput.
Overhead Projection Approach For Multi-Camera Vessel Activity Recognition

On-board video sensors on large ships capture video data at high rates and provide real-time object tracking for maritime applications such as piracy and illegal fishing. High volumes of collected video and imagery data require advanced technology to analyze the video feeds, reduce data smog, and alert the crew on ships or coastal guard when unusual activities are detected. We present an integrated end-to-end system that analyzes multi-camera ship video feed; localizes maritime vessels in the video feed; identifies the maritime vessel over multiple cameras; maps the vessel track onto an overhead plane; and identifies anomalous vessel movement around the ship. In this paper, we focus on a specific activity detection approach in maritime vessel overhead tracks and on synthetic data generation to realistically model maritime boat movements around onboard ship cameras using real-world examples. We propose and compare three novel modes of trajectory analysis and activity classification, using Computing with Words (CWW), a Markov trajectory feature classifier (MTFC), and Naïve Bayes Radial Classifier (NBRC) to detect the activity of vessel approaching the ship, vessel chasing another vessel, and vessel circling around the ship.
Sprinkler system automation control development

The article devoted to an important issue in higher engineering education. The article discusses the development of a prototype of an automated fire extinguishing system, the selection of the major components and the calculation of the main parameters of the system. Planning, evaluating, and improving the applied methods analyzed from the point of view of its endless nature.
Deriving Spatial Policies for Overtaking Maneuvers with Autonomous Vehicles

Planning an accurate and safe trajectory is a crucial element in autonomous driving. To execute complex driving maneuvers like overtaking, motion planning requires an enhanced decision-making algorithm that decides the when, where and how of the overtaking maneuver. This paper proposes an algorithm that increases the likelihood of a safe overtaking maneuver by learning spatial information. Here, spatial information refers to the track portion/curve and the position of the ego vehicle with reference to that. The technique is applied to an autonomous racing setup where vehicles have to detect and operate at the limits of dynamic handling. To learn the spatial information, offline experiments of a 2-player race are conducted to generate probability distributions of overtaking maneuvers conditioned on speed and relative-position of the ego vehicle with respect to the opponent. Furthermore, a Switched Model Predictive Contouring Controller (SMPCC) is proposed for incorporating the policy learning algorithm into the path planning and control setup. Extensive simulations show that the proposed algorithm is able to achieve an increased number of overtakes at different track portions on known and unknown race tracks.
Generating Physically-Realistic Tertiary Protein Structures with Deep Latent Variable Models Learning Over Experimentally-available Structures

Sophisticated deep neural networks have significantly advanced our ability to predict a native structure of a protein amino-acid sequence. However, going beyond a single-structure view remains challenging. While rapid advances are being made, fundamental questions on the ability of generative deep modeling to learn to generate physically-realistic tertiary structures remain. This paper makes two key contributions. It first extends deep convolutional variable autoencoder networks to be able to learn from experimentally-available tertiary structures of proteins of variable lengths. The presented models learn over distance matrix representations of tertiary structures. A systematic and detailed analysis demonstrates that the design of the training data is of primary importance to the ability of the proposed models to learn key characteristics of tertiary structures. The second contribution this paper makes is a careful analysis along several metrics that measure the physical realism of generated tertiary structures. The presented results are promising and show that once seeded with sufficient, physically-realistic structures, variational autoencoders are efficient models for generating physically-realistic tertiary structures.
IDEA: Integrating Divisive and Ensemble-Agglomerate hierarchical clustering framework for arbitrary shape data

Hierarchical clustering, a traditional clustering method, has been getting attention again. Among several reasons, a credit goes to a recent paper by Dasgupta in 2016 that proposed a cost function that quantitatively evaluates hierarchical clustering trees. An important question is how to combine this recent advance with existing successful clustering methods. In this paper, we propose a hierarchical clustering method to minimize the cost function of clustering tree by incorporating existing clustering techniques. First, we developed an ensemble tree-search method that finds an integrated tree with reduced cost by integrating multiple existing hierarchical clustering methods. Second, to operate on large and arbitrary shape data, we designed an efficient hierarchical clustering framework, called integrating divisive and ensemble-agglomerate (IDEA) by combining it with advanced clustering techniques such as nearest neighbor graph construction, divisive-agglomerate hybridization, and dynamic cut tree. The IDEA clustering method showed better performance in minimizing Dasgupta's cost and improving accuracy (adjusted rand index) over existing cost-minimization-based, and density-based hierarchical clustering methods in experiments using arbitrary shape datasets and complex biology-domain datasets.
A Convolutional Autoencoder Based Method with SMOTE for Cyber Intrusion Detection

The advancement of internet applications over the last decade makes network security a priority, in which Intrusion Detection System (IDS) is responsible for detecting threat in network throughput. Nevertheless, current IDS encounter challenges because (i) the training dataset is seriously class-imbalance, (ii) conventional method requires a lot of hand-crafted feature selection. To address these problems, we propose a novel method that combines data augmentation method as well as convolutional autoencoder to build up an IDS. Our method uses SMOTE to balance the distribution of each attack in the training dataset, then uses a convolutional autoencoder to detect the intrusion, which is free of hand-crafted feature design. Experimentally, we show that our proposed method outperforms conventional method both in binary and multi-class classification in terms of accuracy. We also have an ablation study on how the ratio of minority class in dataset effect the f1-score of the model.