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Articles 331 - 360 of 919
Full-Text Articles in Computer Sciences
Multimodal Learning For Hateful Memes Detection, Yi Zhou, Zhenhao Chen, Huiyuan Yang
Multimodal Learning For Hateful Memes Detection, Yi Zhou, Zhenhao Chen, Huiyuan Yang
Computer Science Faculty Research & Creative Works
Memes are used for spreading ideas through social networks. Although most memes are created for humor, some memes become hateful under the combination of pictures and text. Automatically detecting hateful memes can help reduce their harmful social impact. Compared to the conventional multimodal tasks, where the visual and textual information is semantically aligned, hateful memes detection is a more challenging task since the image and text in memes are weakly aligned or even irrelevant. Thus, it requires the model to have a deep understanding of the content and perform reasoning over multiple modalities. This paper focuses on multimodal hateful memes …
Efficient Route Selection For Drone-Based Delivery Under Time-Varying Dynamics, Arindam Khanda, Federico Coro, Francesco Betti Sorbelli, Cristina M. Pinotti, Sajal K. Das
Efficient Route Selection For Drone-Based Delivery Under Time-Varying Dynamics, Arindam Khanda, Federico Coro, Francesco Betti Sorbelli, Cristina M. Pinotti, Sajal K. Das
Computer Science Faculty Research & Creative Works
The use of drones can be a valuable solution for the problem of delivering goods for many reasons. In fact, they can be efficiently employed in time-critical situations when there is a traffic jam on the roads, to serve customers in hard-to-reach places, or simply to expand the business. However, due to limited battery capacities and the fact that drones can serve a single customer at a time, a drone-based delivery system (DBDS) aims to minimize the drones' energy usage for completing a route from the depot to the customer and go back to the depot for new deliveries. In …
Exploring Autoencoder-Based Error-Bounded Compression For Scientific Data, Jinyang Liu, Sheng Di, Kai Zhao, Sian Jin, Dingwen Tao, Xin Liang, Zizhong Chen, Franck Cappello
Exploring Autoencoder-Based Error-Bounded Compression For Scientific Data, Jinyang Liu, Sheng Di, Kai Zhao, Sian Jin, Dingwen Tao, Xin Liang, Zizhong Chen, Franck Cappello
Computer Science Faculty Research & Creative Works
Error-bounded lossy compression is becoming an indispensable technique for the success of today's scientific projects with vast volumes of data produced during the simulations or instrument data acquisitions. Not only can it significantly reduce data size, but it also can control the compression errors based on user-specified error bounds. Autoencoder (AE) models have been widely used in image compression, but few AE-based compression approaches support error-bounding features, which are highly required by scientific applications. To address this issue, we explore using convolutional autoencoders to improve error-bounded lossy compression for scientific data, with the following three key contributions. (1) We provide …
Detection Dns Tunneling Botnets, Bohdan Savenko, Sergii Lysenko, Kira Bobrovnikova, Oleg Savenko, George Markowsky
Detection Dns Tunneling Botnets, Bohdan Savenko, Sergii Lysenko, Kira Bobrovnikova, Oleg Savenko, George Markowsky
Computer Science Faculty Research & Creative Works
Botnets are often used in cyberattacks on network services and individual users, so the ability to detect botnets is very important. Botnets use DNS tunneling to send malicious command-and-control (CC) commands to victims' hosts. Unfortunately, DNS tunneling attacks are very hard to detect. The paper presents a new approach for DNS tunneling botnet detection, which considers all the features and architectural characteristics of botnets. The technique described in this paper is highly efficient at detecting DNS tunneling attacks.
Your 'Attention' Deserves Attention: A Self-Diversified Multi-Channel Attention For Facial Action Analysis, Xiaotian Li, Zhihua Li, Huiyuan Yang, Geran Zhao, Lijun Yin
Your 'Attention' Deserves Attention: A Self-Diversified Multi-Channel Attention For Facial Action Analysis, Xiaotian Li, Zhihua Li, Huiyuan Yang, Geran Zhao, Lijun Yin
Computer Science Faculty Research & Creative Works
Visual attention has been extensively studied for learning fine-grained features in both facial expression recognition (FER) and Action Unit (AU) detection. A broad range of previous research has explored how to use attention modules to localize detailed facial parts (e, g. facial action units), learn discriminative features, and learn inter-class correlation. However, few related works pay attention to the robustness of the attention module itself. Through experiments, we found neural attention maps initialized with different feature maps yield diverse representations when learning to attend the identical Region of Interest (ROI). In other words, similar to general feature learning, the representational …
Improving Lossy Compression For Sz By Exploring The Best-Fit Lossless Compression Techniques, Jinyang Liu, Sihuan Li, Sheng Di, Xin Liang, Kai Zhao, Dingwen Tao, Zizhong Chen, Franck Cappello
Improving Lossy Compression For Sz By Exploring The Best-Fit Lossless Compression Techniques, Jinyang Liu, Sihuan Li, Sheng Di, Xin Liang, Kai Zhao, Dingwen Tao, Zizhong Chen, Franck Cappello
Computer Science Faculty Research & Creative Works
In the past decades, various lossy compressors have been studied broadly due to the ever-increasing volume of data being produced by today's scientific applications. SZ has been one of the best error-bounded lossy compressors ever raised, and it has a flexible framework that includes four adjustable steps: prediction, quantization, variable-length encoding, and lossless compression. In this paper, we improve the lossy compression performances of the SZ compression model by exploring different existing lossless compression techniques using the Squash data compression benchmark. Specifically, we first characterize the bytes outputted by the first three steps in SZ, then we investigate the best …
Free Water In T2 Flair Whitematter Hyperintensity Lesions, Xiaowei Yu, Norman Scheel, Lu Zhang, David C. Zhu, Rong Zhang, Dajiang Zhu
Free Water In T2 Flair Whitematter Hyperintensity Lesions, Xiaowei Yu, Norman Scheel, Lu Zhang, David C. Zhu, Rong Zhang, Dajiang Zhu
Computer Science Faculty Research & Creative Works
Background: White matter (WM) free water (FW) is likely associated with cerebral small vessel disease (CSVD). FWis the fraction of unconstrained water within an image voxel, which can be estimated from diffusion-weighted images. T2-weighted Fluid- Attenuated Inversion Recovery (FLAIR) whitematter hyperintensity (WMH)is awidely used index to assess the damages caused by CSVD. It is critical to characterize howFW content is altered inWMHlesions. In this work, we proposed a data processing framework to assess FW distributions in WMH and normal-appearingWM as well as in differentWMfiber tracts. Method: Single-shell diffusion-weighted image (SS-DWI) and T2 FLAIR image data of 133 cognitively normal (CN) …
Connectionless Edge-Cache Servers For Reducing Cellular Bandwidth Usage In Vehicular Networks, Rui Wang, Jayanthi Rao, Ce Zhou, Subir Biswas
Connectionless Edge-Cache Servers For Reducing Cellular Bandwidth Usage In Vehicular Networks, Rui Wang, Jayanthi Rao, Ce Zhou, Subir Biswas
Computer Science Faculty Research & Creative Works
This paper presents a novel caching mechanism based on Connectionless Edge Cache Servers in vehicular networks. The goal is to intelligently cache content within the vehicles and the edge servers so that majority of the vehiclerequested content can be obtained from those caches, thus minimizing the amount of cellular network usage needed for fetching content from a central server. A notable feature of the cache servers in this work is that they do not have backhaul connectivity. This makes the connectionless servers to be relatively less expensive compared to the usual Roadside Service Units (RSUs), and potentially moveable in response …
Lattice-Based Technique To Visualize And Compare Regional Terrorism Using The Global Terrorism Database, Linda Markowsky, George Markowsky
Lattice-Based Technique To Visualize And Compare Regional Terrorism Using The Global Terrorism Database, Linda Markowsky, George Markowsky
Computer Science Faculty Research & Creative Works
Order-theoretic lattices and their visualizations are proposed as a means of exploring and analyzing databases. The max-complete lattice is formed and the significance of lattice nodes and of the top node are demonstrated. These novel visualizations serve as a useful complement to well-known charting techniques such as bar charts and may extend the knowledge gained from critical databases. The Carver2 dataset is used as an illustrative example, highlighting the formation of the max-complete lattice from the original dataset and the computational advantage provided by compressing the lattice using only its irreducible elements. Regional information from the Global Terrorism Database (GTD), …
Exploiting Semantic Embedding And Visual Feature For Facial Action Unit Detection, Huiyuan Yang, Lijun Yin, Yi Zhou, Jiuxiang Gu
Exploiting Semantic Embedding And Visual Feature For Facial Action Unit Detection, Huiyuan Yang, Lijun Yin, Yi Zhou, Jiuxiang Gu
Computer Science Faculty Research & Creative Works
Recent study on detecting facial action units (AU) has utilized auxiliary information (i.e., facial landmarks, relationship among AUs and expressions, web facial images, etc.), in order to improve the AU detection performance. As of now, no semantic information of AUs has yet been explored for such a task. As a matter of fact, AU semantic descriptions provide much more information than the binary AU labels alone, thus we propose to exploit the Semantic Embedding and Visual feature (SEV-Net) for AU detection. More specifically, AU semantic embeddings are obtained through both Intra-AU and Inter-AU attention modules, where the Intra-AU attention module …
Preface, Zhe Liu, Fan Wu, Sajal K. Das
Preface, Zhe Liu, Fan Wu, Sajal K. Das
Computer Science Faculty Research & Creative Works
No abstract provided.
Optimizing Error-Bounded Lossy Compression For Scientific Data On Gpus, Jiannan Tian, Sheng Di, Xiaodong Yu, Cody Rivera, Kai Zhao, Sian Jin, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello
Optimizing Error-Bounded Lossy Compression For Scientific Data On Gpus, Jiannan Tian, Sheng Di, Xiaodong Yu, Cody Rivera, Kai Zhao, Sian Jin, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello
Computer Science Faculty Research & Creative Works
Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous high-performance computing (HPC) architecture, GPU-accelerated error-bounded compressors (such as CUSZ and cuZFP) have been developed. However, they suffer from either low performance or low compression ratios. To this end, we propose CUSZ+ to target both high compression ratios and throughputs. We identify that data sparsity and data smoothness are key factors for high compression throughputs. Our key contributions in this work are fourfold: (1) We propose an efficient compression workflow to adaptively perform run-length encoding and/or variable-length encoding. (2) We derive Lorenzo reconstruction in …
Swill-Tac: Skill-Oriented Dynamic Task Allocation With Willingness For Complex Job In Crowdsourcing, Riya Samanta, Soumya K. Ghosh, Sajal K. Das
Swill-Tac: Skill-Oriented Dynamic Task Allocation With Willingness For Complex Job In Crowdsourcing, Riya Samanta, Soumya K. Ghosh, Sajal K. Das
Computer Science Faculty Research & Creative Works
Allocating tasks to the best-fit candidates is a classical problem in crowdsourcing (CS). Most of the existing approaches assume that the task and candidate knowledge is known in advance and ignore the effect of enrolled candidates' willingness on the CS system's selection decision. For instance, an unwilling candidate assigned to a task may quit without completing it, thus depreciating the utility of the CS platform. In practice, a task or candidate may arrive or leave the CS system dynamically. Moreover, a complex task may be broken into smaller sub-tasks, each requiring a variety of computations and expertise. To overcome these …
Nodesense2vec: Spatiotemporal Context-Aware Network Embedding For Heterogeneous Urban Mobility Data, Dakshak Keerthi Chandra, Jennifer Leopold, Yanjie Fu
Nodesense2vec: Spatiotemporal Context-Aware Network Embedding For Heterogeneous Urban Mobility Data, Dakshak Keerthi Chandra, Jennifer Leopold, Yanjie Fu
Computer Science Faculty Research & Creative Works
The problem of learning latent representations of heterogeneous networks with spatial and temporal attributes has been gaining traction in recent years, given its myriad of real-world applications. Most systems with applications in the field of transportation, urban economics, medical information, online e-commerce, etc., handle big data that can be structured into Spatiotemporal Heterogeneous Networks (SHNs), thereby making efficient analysis of these networks extremely vital. In this paper, we propose a spatiotemporal context-aware network embedding framework that jointly captures the spatial regularities between objects and the sequential transition patterns of human mobility. First, we model the heterogeneous urban mobility data collected …
Visualization As A Service For Scientific Data, David Pugmire, James Kress, Jieyang Chen, Hank Childs, Jong Choi, Dmitry Ganyushin, Berk Geveci, Mark Kim, Scott Klasky, Xin Liang, For Full List Of Authors, See Publisher's Website.
Visualization As A Service For Scientific Data, David Pugmire, James Kress, Jieyang Chen, Hank Childs, Jong Choi, Dmitry Ganyushin, Berk Geveci, Mark Kim, Scott Klasky, Xin Liang, For Full List Of Authors, See Publisher's Website.
Computer Science Faculty Research & Creative Works
One of the primary challenges facing scientists is extracting understanding from the large amounts of data produced by simulations, experiments, and observational facilities. The use of data across the entire lifetime ranging from real-time to post-hoc analysis is complex and varied, typically requiring a collaborative effort across multiple teams of scientists. Over time, three sets of tools have emerged: One set for analysis, another for visualization, and a final set for orchestrating the tasks. This trifurcated tool set often results in the manual assembly of analysis and visualization workflows, which are one-off solutions that are often fragile and difficult to …
Finding All ∈-Good Arms In Stochastic Bandits, Blake Mason, Lalit Jain, Ardhendu S. Tripathy, Robert Nowak
Finding All ∈-Good Arms In Stochastic Bandits, Blake Mason, Lalit Jain, Ardhendu S. Tripathy, Robert Nowak
Computer Science Faculty Research & Creative Works
The pure-exploration problem in stochastic multi-armed bandits aims to find one or more arms with the largest (or near largest) means. Examples include finding an ∈-good arm, best-arm identification, top-k arm identification, and finding all arms with means above a specified threshold. However, the problem of finding all ∈-good arms has been overlooked in past work, although arguably this may be the most natural objective in many applications. For example, a virologist may conduct preliminary laboratory experiments on a large candidate set of treatments and move all ∈-good treatments into more expensive clinical trials. Since the ultimate clinical efficacy is …
Distributed De Novo Assembler For Large-Scale Long-Read Datasets, Sayan Goswami, Kisung Lee, Seung Jong Park
Distributed De Novo Assembler For Large-Scale Long-Read Datasets, Sayan Goswami, Kisung Lee, Seung Jong Park
Computer Science Faculty Research & Creative Works
Third-generation DNA sequencing technologies such as single-molecule real-time sequencing (SMRT) and nanopore sequencing have the potential to fill the gaps in the existing genome databases since the raw sequences produced by these machines are much longer than those of previous generations and therefore result in more contiguous assemblies. However, these long reads have a high error rate, which makes the assembly process computationally challenging. Moreover, since existing long-read assemblers are designed to run on a single machine, they either take days to complete or run out of memory on even moderate-sized datasets. In this paper, we present a distributed long-read …
Message From The General Chairs, Falko Dressler, Sajal K. Das
Message From The General Chairs, Falko Dressler, Sajal K. Das
Computer Science Faculty Research & Creative Works
No abstract provided.
Special Issue On 6g Wireless Systems, Periklis Chatzimisios, David Soldani, Abbas Jamalipour, Antonio Manzalini, Sajal K. Das
Special Issue On 6g Wireless Systems, Periklis Chatzimisios, David Soldani, Abbas Jamalipour, Antonio Manzalini, Sajal K. Das
Computer Science Faculty Research & Creative Works
No abstract provided.
Set Operation Aided Network For Action Units Detection, Huiyuan Yang, Taoyue Wang, Lijun Yin
Set Operation Aided Network For Action Units Detection, Huiyuan Yang, Taoyue Wang, Lijun Yin
Computer Science Faculty Research & Creative Works
As a large number of parameters exist in deep model-based methods, training such models usually requires many fully AU-annotated facial images. This is true with regard to the number of frames in two widely used datasets: BP4D [31] and DISFA [18], while those frames were captured from a small number of subjects (41, 27 respectively). This is problematic, as subjects produce highly consistent facial muscle movements, adding more frames per subject would only adds more close points in the feature space, and thus the classifier does not benefit from those extra frames. Data augmentation methods can be applied to alleviate …
An Eeg-Based Multi-Modal Emotion Database With Both Posed And Authentic Facial Actions For Emotion Analysis, Xiaotian Li, Xiang Zhang, Huiyuan Yang, Wenna Duan, Weiying Dai, Lijun Yin
An Eeg-Based Multi-Modal Emotion Database With Both Posed And Authentic Facial Actions For Emotion Analysis, Xiaotian Li, Xiang Zhang, Huiyuan Yang, Wenna Duan, Weiying Dai, Lijun Yin
Computer Science Faculty Research & Creative Works
Emotion is an experience associated with a particular pattern of physiological activity along with different physiological, behavioral and cognitive changes. One behavioral change is facial expression, which has been studied extensively over the past few decades. Facial behavior varies with a person's emotion according to differences in terms of culture, personality, age, context, and environment. In recent years, physiological activities have been used to study emotional responses. A typical signal is the electroencephalogram (EEG), which measures brain activity. Most of existing EEG-based emotion analysis has overlooked the role of facial expression changes. There exits little research on the relationship between …
Contextual-Bandit Anomaly Detection For Iot Data In Distributed Hierarchical Edge Computing, Mao V. Ngo, Tie Luo, Hakima Chaouchi, Tony Q.S. Quek
Contextual-Bandit Anomaly Detection For Iot Data In Distributed Hierarchical Edge Computing, Mao V. Ngo, Tie Luo, Hakima Chaouchi, Tony Q.S. Quek
Computer Science Faculty Research & Creative Works
Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity and associate each model with a layer in HEC from bottom to top. Then, we design an adaptive scheme to select one …
Adaptive Multimodal Fusion For Facial Action Units Recognition, Huiyuan Yang, Taoyue Wang, Lijun Yin
Adaptive Multimodal Fusion For Facial Action Units Recognition, Huiyuan Yang, Taoyue Wang, Lijun Yin
Computer Science Faculty Research & Creative Works
Multimodal facial action units (AU) recognition aims to build models that are capable of processing, correlating, and integrating information from multiple modalities (i.e., 2D images from a visual sensor, 3D geometry from 3D imaging, and thermal images from an infrared sensor). Although the multimodal data can provide rich information, there are two challenges that have to be addressed when learning from multimodal data: 1) the model must capture the complex cross-modal interactions in order to utilize the additional and mutual information effectively; 2) the model must be robust enough in the circumstance of unexpected data corruptions during testing, in case …
An Evaluation Of The 6tisch Distributed Resource Management Mode, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
An Evaluation Of The 6tisch Distributed Resource Management Mode, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Computer Science Faculty Research & Creative Works
The IETF is currently defining the 6TiSCH architecture for the Industrial Internet of Things to ensure reliable and timely communication. 6TiSCH relies on the IEEE TSCH MAC protocol and defines different scheduling approaches for managing TSCH cells, including a distributed (neighbor-to-neighbor) scheduling scheme, where cells are allocated by nodes in a cooperative way. Each node leverages a Scheduling Function (SF) to compute the required number of cells, and the 6top (6P) protocol to negotiate them with neighbors. Currently, the Minimal Scheduling Function (MSF) is under consideration for standardization. However, multiple SFs are expected to be used in real deployments, in …
Efficient Column-Oriented Processing For Mutual Subspace Skyline Queries, Tao Jiang, Bin Zhang, Dan Lin, Yunjun Gao, Qing Li
Efficient Column-Oriented Processing For Mutual Subspace Skyline Queries, Tao Jiang, Bin Zhang, Dan Lin, Yunjun Gao, Qing Li
Computer Science Faculty Research & Creative Works
A mutual skyline query will enable some new applications, such as marketing analysis, task allocation, and personalized matching. Algorithms for efficient processing of this query have been recently proposed in the literature. Those approaches use the R-tree indexes and apply a series of pruning criteria toward efficient processing. However, they are characterized by several limitations: (1) they cannot process different interests on attributes for skyline and reverse skyline, (2) they require a multidimensional index, which suffers from performance degradation, especially in high-dimensional space, and (3) they do not support vertically decomposed data that is a natural and intuitive choice for …
Cusz: An Efficient Gpu-Based Error-Bounded Lossy Compression Framework For Scientific Data, Jiannan Tian, Sheng Di, Kai Zhao, Cody Rivera, Megan Hickman Fulp, Robert Underwood, Sian Jin, Xin Liang, For Full List Of Authors, See Publisher's Website.
Cusz: An Efficient Gpu-Based Error-Bounded Lossy Compression Framework For Scientific Data, Jiannan Tian, Sheng Di, Kai Zhao, Cody Rivera, Megan Hickman Fulp, Robert Underwood, Sian Jin, Xin Liang, For Full List Of Authors, See Publisher's Website.
Computer Science Faculty Research & Creative Works
Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain high fidelityfor postanalysis. Because supercomputers and HPC applicationsare becoming heterogeneous using accelerator-based architectures,in particular GPUs, several development teams have recently released GPU versions of their lossy compressors. However, existingstate-of-the-art GPU-based lossy compressors suffer from eitherlow compression and decompression throughput or low compression quality. In this paper, we present an optimized GPU version,cuSZ, for one of the best error-bounded lossy compressors-SZ.To the best of our knowledge, cuSZ is the first error-boundedlossy compressor on GPUs for scientific data. …
Big Data Energy Management, Analytics And Visualization For Residential Areas, Ragini Gupta, A. R. Al-Ali, Imran A. Zualkernan, Sajal K. Das
Big Data Energy Management, Analytics And Visualization For Residential Areas, Ragini Gupta, A. R. Al-Ali, Imran A. Zualkernan, Sajal K. Das
Computer Science Faculty Research & Creative Works
With the rapid development of IoT based home appliances, it has become a possibility that home owners share with Utilities in the management of home appliances energy consumption. Thus, the proposed work empowers home owners to manage their home appliances energy consumption and allow them to compare their consumption with respect to their local community total consumption. This serves as a nudge in consumer's behavior to schedule their home appliances operation according to their local community consumption profile and trend. Utilizing the same common communication infrastructure, it also allows the utilities on different consumption levels (community, state, country) to monitor …
Analysis Of Distributed And Autonomous Scheduling Functions For 6tisch Networks, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Analysis Of Distributed And Autonomous Scheduling Functions For 6tisch Networks, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Computer Science Faculty Research & Creative Works
The 6TiSCH architecture is expected to play a significant role to enable the Internet of Things paradigm also in industrial environments, where reliability and timeliness are of paramount importance to support critical applications. Many research activities have focused on the Scheduling Function (SF) used for managing the allocation of communication resources in order to guarantee the application requirements. Two different approaches have mainly attracted the interest of researchers, namely distributed and autonomous scheduling. Although many different (both distributed and autonomous) SFs have been proposed and analyzed, a direct comparison of these two approaches is still missing. In this work, we …
Hierarchical Syntactic Models For Human Activity Recognition Through Mobility Traces, Enrico Casella, Marco Ortolani, Simone Silvestri, Sajal K. Das
Hierarchical Syntactic Models For Human Activity Recognition Through Mobility Traces, Enrico Casella, Marco Ortolani, Simone Silvestri, Sajal K. Das
Computer Science Faculty Research & Creative Works
Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known as grammatical inference. We also introduce a measure of similarity that accounts for the intrinsic hierarchical …
Motifs Enable Communication Efficiency And Fault-Tolerance In Transcriptional Networks, Satyaki Roy, Preetam Ghosh, Dipak Barua, Sajal K. Das
Motifs Enable Communication Efficiency And Fault-Tolerance In Transcriptional Networks, Satyaki Roy, Preetam Ghosh, Dipak Barua, Sajal K. Das
Computer Science Faculty Research & Creative Works
Analysis of the topology of transcriptional regulatory networks (TRNs) is an effective way to study the regulatory interactions between the transcription factors (TFs) and the target genes. TRNs are characterized by the abundance of motifs such as feed forward loops (FFLs), which contribute to their structural and functional properties. In this paper, we focus on the role of motifs (specifically, FFLs) in signal propagation in TRNs and the organization of the TRN topology with FFLs as building blocks. To this end, we classify nodes participating in FFLs (termed motif central nodes) into three distinct roles (namely, roles A, B …