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Gabidulin, E. M., N. I. Pilipchuk, and M. Bossert. "Decoding of random network codes." Problems of Information Transmission 46, no. 4 (2010): 300–320. http://dx.doi.org/10.1134/s0032946010040034.
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Chen, Chao, Hongmei Xie, and Baoming Bai. "Layered subspace codes for random network coding." Transactions on Emerging Telecommunications Technologies 26, no. 3 (2013): 461–68. http://dx.doi.org/10.1002/ett.2648.
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Riemensberger, Maximilian, Yalin Sagduyu, Michael Honig, and Wolfgang Utschick. "Training overhead for decoding random linear network codes in wireless networks." IEEE Journal on Selected Areas in Communications 27, no. 5 (2009): 729–37. http://dx.doi.org/10.1109/jsac.2009.090613.
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Li, Ye, Jun Zhu, and Zhihua Bao. "Sparse Random Linear Network Coding With Precoded Band Codes." IEEE Communications Letters 21, no. 3 (2017): 480–83. http://dx.doi.org/10.1109/lcomm.2016.2632731.
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Wang, Shiheng, Quan Zhou, Siyuan Yang, Chaoyuan Bai, and Heng Liu. "Wireless Communication Strategy with BATS Codes for Butterfly Network." Journal of Physics: Conference Series 2218, no. 1 (2022): 012003. http://dx.doi.org/10.1088/1742-6596/2218/1/012003.
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Abstract In this paper, two transmission schemes for batched sparse(BATS) codes based on a multicast protocol in a butterfly network are studied. For both of the schemes, a source file is first segmented into several packets at the source node, which are then coded with outer coder of the BATS codes to generate potentially unlimited batches. Rather than the linear network, in which the packets are directly transmitted to the next node, the packets will be forwarded to multiple nodes in the butterfly network. All the intermediate nodes which receive the packets recode them with random linear network coding (RLNC). At the destination node receivers, the sink node decodes the packets transmitted from different links. The Scheme I requires the intermediate nodes to recode the packets until all of them are received. On the contrary, the intermidate nodes of Scheme II just recode the packets as soon as receiving them and forward the recoded packets to the next node immediately. The Belief Propagation(BP) decoders of these two schemes are studied and applied to Scheme I and Scheme II respectively. The simulation results show that Scheme I consumed fewer batches than Scheme II, which indicates Scheme I outperforms Scheme II.
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Chen, Siguang, Meng Wu, and Weifeng Lu. "Compressed error and erasure correcting codes via rank-metric codes in random network coding." International Journal of Communication Systems 25, no. 11 (2011): 1398–414. http://dx.doi.org/10.1002/dac.1316.
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Nazer, Bobak, and Michael Gastpar. "The case for structured random codes in network capacity theorems." European Transactions on Telecommunications 19, no. 4 (2008): 455–74. http://dx.doi.org/10.1002/ett.1284.
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LARASATI, SOLICHAH, and KHOIRUN NI’AMAH. "Sub-Optimal Degree Distribution untuk Prioritas Komunikasi Manusia menggunakan Proyeksi EXIT Chart pada Jaringan Masa Depan." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 7, no. 3 (2019): 442. http://dx.doi.org/10.26760/elkomika.v7i3.442.
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ABSTRAKPada jaringan masa depan melibatkan komunikasi antara mesin dan manusia. Penelitian ini mengusulkan konsep coding dalam jaringan menggunakan Maximum Distance Separable (MDS) codes. Desain sub-optimal degree distribution untuk memprioritaskan manusia menggunakan proyeksi EXIT Chart. Pada penelitian ini dasar dari skema multiple akses untuk jaringan super-padat menggunakan Coded Random Access (CRA). Usulan model jaringan menggunakan Binary Erasure Channel (BEC). Evaluasi performansi untuk grup manusia dan mesin diukur berdasarkan throughput dan packet-loss-rate dan hasilnya juga dibuktikan menggunakan frequency-flat Rayleigh fading. Sub-optimal degree distribusi yang diusulkan ℎ𝑥8,2,1 dan Λ 𝑥3,2,0.2,4,2,0.8 dengan hasil throughput sebelum fading Th 0.35 paket/slot dan Tm 0.32 paket/slot, sedangkan setelah fading Th 0.34 paket/slot dan Tm 0.22 paket/slot.Kata kunci : MDS codes, CRA, human, machines, EXIT chart ABSTRACTFuture wireless network involving machines and human communications.This research proposed new concept of network coding based on Maximum Distance Separable (MDS) codes. Designed optimally sub-optimal degree distribution for prioritizing human using projected EXIT chart. This research fundamental multiple access scheme for wireless super-dense network using Coded Random Access (CRA). In this research, proposed scheme under Binary Erasure Channel (BEC) to model a network. We evaluate the performance for human and machines group in terms of throughput and packet-loss-rate, and the result are then verified using frequency-flat Rayleigh fading. We have proposed sub-optimal degree distributions are Λ ℎ𝑥8,2,1 and Λ 𝑥3,2,0.2,4,2,0.8, the resulting throughput Th 0.35 packet/slot and Tm 0.32 packet/slot under fading and without fading Th 0.34 packet/slot and Tm 0.22 packet/slot.Keywords: MDS codes, CRA, human, machines, EXIT chart
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Wachter-Zeh, Antonia, Markus Stinner, and Vladimir Sidorenko. "Convolutional Codes in Rank Metric With Application to Random Network Coding." IEEE Transactions on Information Theory 61, no. 6 (2015): 3199–213. http://dx.doi.org/10.1109/tit.2015.2424930.
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Xiao, Ming, Muriel Medard, and Tor Aulin. "Cross-Layer Design of Rateless Random Network Codes for Delay Optimization." IEEE Transactions on Communications 59, no. 12 (2011): 3311–22. http://dx.doi.org/10.1109/tcomm.2011.112311.100366.
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Li, Ye, Wai-Yip Chan, and Steven D. Blostein. "On Design and Efficient Decoding of Sparse Random Linear Network Codes." IEEE Access 5 (2017): 17031–44. http://dx.doi.org/10.1109/access.2017.2741972.
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Mao, Licheng, Shenghao Yang, Xuan Huang, and Yanyan Dong. "Design and Analysis of Systematic Batched Network Codes." Entropy 25, no. 7 (2023): 1055. http://dx.doi.org/10.3390/e25071055.
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Systematic codes are of important practical interest for communications. Network coding, however, seems to conflict with systematic codes: although the source node can transmit message packets, network coding at the intermediate network nodes may significantly reduce the number of message packets received by the destination node. Is it possible to obtain the benefit of network coding while preserving some properties of the systematic codes? In this paper, we study the systematic design of batched network coding, which is a general network coding framework that includes random linear network coding as a special case. A batched network code has an outer code and an inner code, where the latter is formed by linear network coding. A systematic batched network code must take both the outer code and the inner code into consideration. Based on the outer code of a BATS code, which is a matrix-generalized fountain code, we propose a general systematic outer code construction that achieves a low encoding/decoding computation cost. To further reduce the number of random trials required to search a code with a close-to-optimal coding overhead, a triangular embedding approach is proposed for the construction of the systematic batches. We introduce new inner codes that provide protection for the systematic batches during transmission and show that it is possible to significantly increase the expected number of message packets in a received batch at the destination node, without harm to the expected rank of the batch transfer matrix generated by network coding.
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Si, Jing Jing. "A Hierarchical Multicast Scheme for Heterogeneous Receivers." Advanced Materials Research 108-111 (May 2010): 57–62. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.57.
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We investigate inter-session network coding for networks with heterogeneous receivers in this paper. Based on layered source coding, we define the hierarchical inter-layer random network codes, and propose a hierarchical multicast scheme. Moreover, we compare our hierarchical multicast scheme with the layered multicast schemes in theory and with simulations. Simulation results show that our hierarchical multicast scheme can achieve the optimal aggregate throughput for some networks where the layered multicast schemes are suboptimal.
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Emran, A., M. Elsabrouty, O. Muta, and H. Furukawa. "ACK based partial random selection encoding for uplink distributed LDPC network codes." Electronics Letters 51, no. 17 (2015): 1328–29. http://dx.doi.org/10.1049/el.2015.2132.
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Chen, Siguang, Meng Wu, and Weifeng Lu. "Secret Error Control Codes Against Malicious Attacks in Random Multisource Network Coding." Wireless Personal Communications 69, no. 4 (2012): 1847–64. http://dx.doi.org/10.1007/s11277-012-0666-7.
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Do-Duy, Tan, and Maria Ángeles Vázquez-Castro. "Finite-length performance comparison of network codes using random vs Pascal matrices." AEU - International Journal of Electronics and Communications 114 (February 2020): 153012. http://dx.doi.org/10.1016/j.aeue.2019.153012.
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Nazir, Sajid, Vladimir Stanković, Ivan Andonović, and Dejan Vukobratović. "Application Layer Systematic Network Coding for Sliced H.264/AVC Video Streaming." Advances in Multimedia 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/916715.
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Application Layer Forward Error Correction (AL-FEC) with rateless codes can be applied to protect the video data over lossy channels. Expanding Window Random Linear Codes (EW RLCs) are a flexible unequal error protection fountain coding scheme which can provide prioritized data transmission. In this paper, we propose a system that exploits systematic EW RLC for H.264/Advanced Video Coding (AVC) slice-partitioned data. The system prioritizes slices based on their PSNR contribution to reconstruction as well as temporal significance. Simulation results demonstrate usefulness of using relative slice priority with systematic codes for multimedia broadcast applications.
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Bioglio, Valerio, Marco Grangetto, Rossano Gaeta, and Matteo Sereno. "A practical Random Network Coding scheme for data distribution on peer-to-peer networks using rateless codes." Performance Evaluation 70, no. 1 (2013): 1–13. http://dx.doi.org/10.1016/j.peva.2012.09.001.
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Gallego, Antonio-Javier, Antonio Pertusa, and Jorge Calvo-Zaragoza. "Improving Convolutional Neural Networks’ Accuracy in Noisy Environments Using k-Nearest Neighbors." Applied Sciences 8, no. 11 (2018): 2086. http://dx.doi.org/10.3390/app8112086.
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We present a hybrid approach to improve the accuracy of Convolutional Neural Networks (CNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (kNN) algorithm for inference. Although this is a common technique in transfer learning, we apply it to the same domain for which the network was trained. Previous works show that neural codes (neuron activations of the last hidden layers) can benefit from the inclusion of classifiers such as support vector machines or random forests. In this work, our proposed hybrid CNN + kNN architecture is evaluated using several image datasets, network topologies and label noise levels. The results show significant accuracy improvements in the inference stage with respect to the standard CNN with noisy labels, especially with relatively large datasets such as CIFAR100. We also verify that applying the ℓ 2 norm on neural codes is statistically beneficial for this approach.
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Wang, Ming, Yong Li, Rui Liu, Huihui Wu, Youqiang Hu, and Francis C. M. Lau. "Decoding Quadratic Residue Codes Using Deep Neural Networks." Electronics 11, no. 17 (2022): 2717. http://dx.doi.org/10.3390/electronics11172717.
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In this paper, a low-complexity decoder based on a neural network is proposed to decode binary quadratic residue (QR) codes. The proposed decoder is based on the neural min-sum algorithm and the modified random redundant decoder (mRRD) algorithm. This new method has the same asymptotic time complexity as the min-sum algorithm, which is much lower than the difference on syndromes (DS) algorithm. Simulation results show that the proposed algorithm achieves a gain of more than 0.4 dB when compared to the DS algorithm. Furthermore, a simplified approach based on trapping sets is applied to reduce the complexity of the mRRD. This simplification leads to a slight loss in error performance and a reduction in implementation complexity.
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Sundaram, Varun, Rosita Zakeri, Klaus K. Witte, and Jennifer kathleen Quint. "Development of algorithms for determining heart failure with reduced and preserved ejection fraction using nationwide electronic healthcare records in the UK." Open Heart 9, no. 2 (2022): e002142. http://dx.doi.org/10.1136/openhrt-2022-002142.
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BackgroundDetermining heart failure (HF) phenotypes in routine electronic health records (EHR) is challenging. We aimed to develop and validate EHR algorithms for identification of specific HF phenotypes, using Read codes in combination with selected patient characteristics.MethodsWe used The Healthcare Improvement Network (THIN). The study population included a random sample of individuals with HF diagnostic codes (HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF) and non-specific HF) selected from all participants registered in the THIN database between 1 January 2015 and 30 September 2017. Confirmed diagnoses were determined in a randomly selected subgroup of 500 patients via GP questionnaires including a review of all available cardiovascular investigations. Confirmed diagnoses of HFrEF and HFpEF were based on four criteria. Based on these data, we calculated a positive predictive value (PPV) of predefined algorithms which consisted of a combination of Read codes and additional information such as echocardiogram results and HF medication records.ResultsThe final cohort from which we drew the 500 patient random sample consisted of 10 275 patients. Response rate to the questionnaire was 77.2%. A small proportion (18%) of the overall HF patient population were coded with specific HF phenotype Read codes. For HFrEF, algorithms achieving over 80% PPV included definite, possible or non-specific HF HFrEF codes when combined with at least two of the drugs used to treat HFrEF. Only in non-specific HF coding did the use of three drugs (rather than two) contribute to an improvement of the PPV for HFrEF. HFpEF was only accurately defined with specific codes. In the absence of specific coding for HFpEF, the PPV was consistently below 50%.ConclusionsPrescription for HF medication can reliably be used to find HFrEF patients in the UK, even in the absence of a specific Read code for HFrEF. Algorithms using non-specific coding could not reliably find HFpEF patients.
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Zhang, Wenyue, and Min Zhu. "Weighted BATS Codes with LDPC Precoding." Entropy 25, no. 4 (2023): 686. http://dx.doi.org/10.3390/e25040686.
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Batched Sparse (BATS) codes are a type of network coding scheme that use a combination of random linear network coding (RLNC) and fountain coding to enhance the reliability and efficiency of data transmission. In order to achieve unequal error protection for different data, researchers have proposed unequal error protection BATS (UEP-BATS) codes. However, current UEP-BATS codes suffer from high error floors in their decoding performance, which restricts their practical applications. To address this issue, we propose a novel UEP-BATS code scheme that employs a precoding stage prior to the weighted BATS code. The proposed precoding stage utilizes a partially regular low-density parity-check (PR-LDPC) code, which helps to mitigate the high error floors in the weighted BATS code We derive the asymptotic performance of the proposed scheme based on density evolution (DE). Additionally, we propose a searching algorithm to optimize precoding degree distribution within the complexity range of the precoding stage. Simulation results show that compared to the conventional weighted BATS codes, our proposed scheme offers superior UEP performance and lower error floor, which verifies the effectiveness of our scheme.
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Sun, Yu Qiang, Xiao Kang Wang, Yu Wan Gu, and Qiang Zhu. "Study of RFID Anti-Collision Based on Token Ring Network Model." Applied Mechanics and Materials 182-183 (June 2012): 1013–16. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1013.
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Models of Computer Network Anti-collision can’t be used in the area of RFID Anti-collision Algorithm, because the limit of tag’s structure. The paper discusses the anti-collision model of computer network----Token Ring Network Model, which lets the orthogonal codes act as token, proposed a strategy for RFID Anti-collision. Currently, the popular RFID Anti-collision Algorithms are based on the idea of random access, when collision happens, the reader tells the collision tags to send ID, waiting for any time. When the number of tag is large, two or more tags will be waiting for the same random time, then collision will be happen again, the contents of this paper can effectively solve these problems.
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Rinke, Michael L., Dominique Jan, Janelle Nassim, Jaeun Choi, and Steven J. Choi. "Surgical Site Infections Following Pediatric Ambulatory Surgery: An Epidemiologic Analysis." Infection Control & Hospital Epidemiology 37, no. 8 (2016): 931–38. http://dx.doi.org/10.1017/ice.2016.98.
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OBJECTIVETo identify surgical site infection (SSI) rates following pediatric ambulatory surgery, SSI outcomes and risk factors, and sensitivity and specificity of SSI administrative billing codes.DESIGNRetrospective chart review of pediatric ambulatory surgeries with International Classification of Disease, Ninth Revision (ICD-9) codes for SSI, and a systematic random sampling of 5% of surgeries without SSI ICD-9 codes, all adjudicated for SSI on the basis of an ambulatory-adapted National Healthcare Safety Network definition.SETTINGUrban pediatric tertiary care center April 1, 2009-March 31, 2014.METHODSSSI rates and sensitivity and specificity of ICD-9 codes were estimated using sampling design, and risk factors were analyzed in case–rest of cohort, and case-control, designs.RESULTSIn 15,448 pediatric ambulatory surgeries, 34 patients had ICD-9 codes for SSI and 25 met the adapted National Healthcare Safety Network criteria. One additional SSI was identified with systematic random sampling. The SSI rate following pediatric ambulatory surgery was 2.9 per 1,000 surgeries (95% CI, 1.2–6.9). Otolaryngology surgeries demonstrated significantly lower SSI rates compared with endocrine (P=.001), integumentary (P=.001), male genital (P<.0001), and respiratory (P=.01) surgeries. Almost half of patients with an SSI were admitted, 88% received antibiotics, and 15% returned to the operating room. No risk factors were associated with SSI. The sensitivity of ICD-9 codes for SSI following ambulatory surgery was 55.31% (95% CI, 12.69%–91.33%) and specificity was 99.94% (99.89%–99.97%).CONCLUSIONSSSI following pediatric ambulatory surgery occurs at an appreciable rate and conveys morbidity on children.Infect Control Hosp Epidemiol 2016;37:931–938
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Didier, Gilles, Christine Brun, and Anaïs Baudot. "Identifying communities from multiplex biological networks." PeerJ 3 (December 22, 2015): e1525. http://dx.doi.org/10.7717/peerj.1525.
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Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression). However, this diversity is classically not considered and the different interaction categories are usually aggregated in a single network. The multiplex framework, where biological relationships are represented by different network layers reflecting the various nature of interactions, is expected to retain more information. Here we assessed aggregation, consensus and multiplex-modularity approaches to detect communities from multiple network sources. By simulating random networks, we demonstrated that the multiplex-modularity method outperforms the aggregation and consensus approaches when network layers are incomplete or heterogeneous in density. Application to a multiplex biological network containing 4 layers of physical or functional interactions allowed recovering communities more accurately annotated than their aggregated counterparts. Overall, taking into account the multiplexity of biological networks leads to better-defined functional modules. A user-friendly graphical software to detect communities from multiplex networks, and corresponding C source codes, are available at GitHub (https://github.com/gilles-didier/MolTi).
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Gadouleau, Maximilien, and Zhiyuan Yan. "Packing and Covering Properties of Subspace Codes for Error Control in Random Linear Network Coding." IEEE Transactions on Information Theory 56, no. 5 (2010): 2097–108. http://dx.doi.org/10.1109/tit.2010.2043780.
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Yang, Ximing, Yuan Wu, Kaiyi Zhang, and Cheng Jin. "CPCGAN: A Controllable 3D Point Cloud Generative Adversarial Network with Semantic Label Generating." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3154–62. http://dx.doi.org/10.1609/aaai.v35i4.16425.
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Generative Adversarial Networks (GAN) are good at generating variant samples of complex data distributions. Generating a sample with certain properties is one of the major tasks in the real-world application of GANs. In this paper, we propose a novel generative adversarial network to generate 3D point clouds from random latent codes, named Controllable Point Cloud Generative Adversarial Network(CPCGAN). A two-stage GAN framework is utilized in CPCGAN and a sparse point cloud containing major structural information is extracted as the middle-level information between the two stages. With their help, CPCGAN has the ability to control the generated structure and generate 3D point clouds with semantic labels for points. Experimental results demonstrate that the proposed CPCGAN outperforms state-of-the-art point cloud GANs.
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Khalil, Amaad, Nasruminallah, Irfan Ahmed, and Salman Ilahi Siddiqui. "Design of Robust Video Transmission System by Using Efficient Forward Error Correction Scheme." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 58, no. 4 (2022): 27–34. http://dx.doi.org/10.53560/ppasa(58-4)767.
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The advent of modern digital technologies has made multimedia communication systems one of the most demanding technologies of the time. The use of available bandwidth for efficient and errorless multimedia communication is the key challenge for the wireless communication research community. However, a wireless network has the disadvantage of being prone to random channel noise and data contamination. This paper proposes a robust video transmission framework by using an efficient forward error correction technique. In this work, the experimental performance of widely used forward error correction codes i.e., Convolution codes, LDPC codes, Turbo codes, and Concatenated codes, are compared based on their capability to compensate the channel noise and distortion. An efficient encoding scheme is devised for the transmission of YUV encoded frames by using the selected FEC codes in a noisy channel environment. The retrieved video is analysed by using the Peak Signal-to-Noise ratio and bit error rate as performance metrics. The results and cross comparison shows that concatenated codes have a handsome improvement in avoiding channel contamination and distortion.
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Long, Yahui, Min Wu, Chee Keong Kwoh, Jiawei Luo, and Xiaoli Li. "Predicting human microbe–drug associations via graph convolutional network with conditional random field." Bioinformatics 36, no. 19 (2020): 4918–27. http://dx.doi.org/10.1093/bioinformatics/btaa598.
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Abstract Motivation Human microbes play critical roles in drug development and precision medicine. How to systematically understand the complex interaction mechanism between human microbes and drugs remains a challenge nowadays. Identifying microbe–drug associations can not only provide great insights into understanding the mechanism, but also boost the development of drug discovery and repurposing. Considering the high cost and risk of biological experiments, the computational approach is an alternative choice. However, at present, few computational approaches have been developed to tackle this task. Results In this work, we leveraged rich biological information to construct a heterogeneous network for drugs and microbes, including a microbe similarity network, a drug similarity network and a microbe–drug interaction network. We then proposed a novel graph convolutional network (GCN)-based framework for predicting human Microbe–Drug Associations, named GCNMDA. In the hidden layer of GCN, we further exploited the Conditional Random Field (CRF), which can ensure that similar nodes (i.e. microbes or drugs) have similar representations. To more accurately aggregate representations of neighborhoods, an attention mechanism was designed in the CRF layer. Moreover, we performed a random walk with restart-based scheme on both drug and microbe similarity networks to learn valuable features for drugs and microbes, respectively. Experimental results on three different datasets showed that our GCNMDA model consistently achieved better performance than seven state-of-the-art methods. Case studies for three microbes including SARS-CoV-2 and two antimicrobial drugs (i.e. Ciprofloxacin and Moxifloxacin) further confirmed the effectiveness of GCNMDA in identifying potential microbe–drug associations. Availability and implementation Python codes and dataset are available at: https://github.com/longyahui/GCNMDA. Supplementary information Supplementary data are available at Bioinformatics online.
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Panyapanuwat, Petcharat, Suwatchai Kamonsantiroj, and Luepol Pipanmaekaporn. "Similarity-preserving hash for content-based audio retrieval using unsupervised deep neural networks." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 879. http://dx.doi.org/10.11591/ijece.v11i1.pp879-891.
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Due to its efficiency in storage and search speed, binary hashing has become an attractive approach for a large audio database search. However, most existing hashing-based methods focus on data-independent scheme where random linear projections or some arithmetic expression are used to construct hash functions. Hence, the binary codes do not preserve the similarity and may degrade the search performance. In this paper, an unsupervised similarity-preserving hashing method for content-based audio retrieval is proposed. Different from data-independent hashing methods, we develop a deep network to learn compact binary codes from multiple hierarchical layers of nonlinear and linear transformations such that the similarity between samples is preserved. The independence and balance properties are included and optimized in the objective function to improve the codes. Experimental results on the Extended Ballroom dataset with 8 genres of 3,000 musical excerpts show that our proposed method significantly outperforms state-of-the-art data-independent method in both effectiveness and efficiency.
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Kamal, Md Sarwar, and Mohammad Ibrahim Khan. "Chapman–Kolmogorov equations for global PPIs with Discriminant-EM." International Journal of Biomathematics 07, no. 05 (2014): 1450053. http://dx.doi.org/10.1142/s1793524514500533.
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Ongoing improvements in Computational Biology research have generated massive amounts of Protein–Protein Interactions (PPIs) dataset. In this regard, the availability of PPI data for several organisms provoke the discovery of computational methods for measurements, analysis, modeling, comparisons, clustering and alignments of biological data networks. Nevertheless, fixed network comparison is computationally stubborn and as a result several methods have been used instead. We illustrate a probabilistic approach among proteins nodes that are part of various networks by using Chapman–Kolmogorov (CK) formula. We have compared CK formula with semi-Markov random method, SMETANA. We significantly noticed that CK outperforms the SMETANA in all respects such as efficiency, speed, space and complexity. We have modified the SMETANA source codes available in MATLAB in the light of CK formula. Discriminant-Expectation Maximization (D-EM) accesses the parameters of a protein network datasets and determines a linear transformation to simplify the assumption of probabilistic format of data distributions and find good features dynamically. Our implementation finds that D-EM has a satisfactory performance in protein network alignment applications.
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Ngeth, Rithea, Brian Kurkoski, Yuto Lim, and Yasuo Tan. "A Design of Overlapped Chunked Code over Compute-and-Forward for Multi-Source Multi-Relay Networks." Sensors 18, no. 10 (2018): 3225. http://dx.doi.org/10.3390/s18103225.
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This paper investigates the design of overlapped chunked codes (OCC) for multi-source multi-relay networks where a physical-layer network coding approach, compute-and-forward (CF) based on nested lattice codes (NLC), is applied for the simultaneous transmissions from the sources to the relays. This code is called OCC/CF. In this paper, OCC is applied before NLC before transmitting for each source. Random linear network coding is applied within each chunk. A decodability condition to design OCC/CF is provided. In addition, an OCC with a contiguously overlapping, but non-rounded-end fashion is employed for the design, which is done by using the probability distributions of the number of innovative codeword combinations and the probability distribution of the participation factor of each source to the codeword combinations received for a chunk transmission. An estimation is done to select an allocation, i.e., the number of innovative blocks per chunk and the number of blocks taken from the previous chunk for all sources, that is expected to provide the desired performance. From the numerical results, the design overhead of OCC/CF is low when the probability distribution of the participation factor of each source is dense at the chunk size for each source.
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Gicev, Spiro, Lloyd C. L. Hollenberg, and Muhammad Usman. "A scalable and fast artificial neural network syndrome decoder for surface codes." Quantum 7 (July 12, 2023): 1058. http://dx.doi.org/10.22331/q-2023-07-12-1058.
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Surface code error correction offers a highly promising pathway to achieve scalable fault-tolerant quantum computing. When operated as stabilizer codes, surface code computations consist of a syndrome decoding step where measured stabilizer operators are used to determine appropriate corrections for errors in physical qubits. Decoding algorithms have undergone substantial development, with recent work incorporating machine learning (ML) techniques. Despite promising initial results, the ML-based syndrome decoders are still limited to small scale demonstrations with low latency and are incapable of handling surface codes with boundary conditions and various shapes needed for lattice surgery and braiding. Here, we report the development of an artificial neural network (ANN) based scalable and fast syndrome decoder capable of decoding surface codes of arbitrary shape and size with data qubits suffering from the depolarizing error model. Based on rigorous training over 50 million random quantum error instances, our ANN decoder is shown to work with code distances exceeding 1000 (more than 4 million physical qubits), which is the largest ML-based decoder demonstration to-date. The established ANN decoder demonstrates an execution time in principle independent of code distance, implying that its implementation on dedicated hardware could potentially offer surface code decoding times of O(&#x03BC;sec), commensurate with the experimentally realisable qubit coherence times. With the anticipated scale-up of quantum processors within the next decade, their augmentation with a fast and scalable syndrome decoder such as developed in our work is expected to play a decisive role towards experimental implementation of fault-tolerant quantum information processing.
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Allahgholi, Milad, Hossein Rahmani, Delaram Javdani, et al. "DDREL: From drug-drug relationships to drug repurposing." Intelligent Data Analysis 26, no. 1 (2022): 221–37. http://dx.doi.org/10.3233/ida-215745.
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Analyzing the relationships among various drugs is an essential issue in the field of computational biology. Different kinds of informative knowledge, such as drug repurposing, can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease, and drug-disease relationships. In this paper, we propose DDREL as a general-purpose method that applies deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperforms the existing human drug network method and a random network respected to average similarities of drugs’ anatomical therapeutic chemical (ATC) codes. DDREL is able to shed light on the existing deficiency of the ATC codes in various drug groups. From the DDREL graph, the history of drug discovery became visible. In addition, drugs that had repurposing score 1 (diflunisal, pargyline, fenofibrate, guanfacine, chlorzoxazone, doxazosin, oxymetholone, azathioprine, drotaverine, demecarium, omifensine, yohimbine) were already used in additional indication. The proposed DDREL method justifies the predictive power of textual data in PubMed abstracts. DDREL shows that such data can be used to 1- Predict repurposing drugs with high accuracy, and 2- Reveal existing deficiencies of the ATC codes in various drug groups.
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Kim, Junhong, Hyungseok Kim, Jaesun Park, Kyounghyun Mo, and Pilsung Kang. "Bin2Vec: A Better Wafer Bin Map Coloring Scheme for Comprehensible Visualization and Effective Bad Wafer Classification." Applied Sciences 9, no. 3 (2019): 597. http://dx.doi.org/10.3390/app9030597.
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A wafer bin map (WBM), which is the result of an electrical die-sorting test, provides information on which bins failed what tests, and plays an important role in finding defective wafer patterns in semiconductor manufacturing. Current wafer inspection based on WBM has two problems: good/bad WBM classification is performed by engineers and the bin code coloring scheme does not reflect the relationship between bin codes. To solve these problems, we propose a neural network-based bin coloring method called Bin2Vec to make similar bin codes are represented by similar colors. We also build a convolutional neural network-based WBM classification model to reduce the variations in the decisions made by engineers with different expertise by learning the company-wide historical WBM classification results. Based on a real dataset with a total of 27,701 WBMs, our WBM classification model significantly outperformed benchmarked machine learning models. In addition, the visualization results of the proposed Bin2Vec method makes it easier to discover meaningful WBM patterns compared with the random RGB coloring scheme. We expect the proposed framework to improve both efficiencies by automating the bad wafer classification process and effectiveness by assigning similar bin codes and their corresponding colors on the WBM.
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Tran, Steven, Luke Rasmussen, Jennifer Pacheco, et al. "802 An electronic health record-based approach to identify and characterize patients with immune checkpoint inhibitor-associated arthritis." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (2021): A838—A839. http://dx.doi.org/10.1136/jitc-2021-sitc2021.802.
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BackgroundImmune checkpoint inhibitors (ICIs) are a pillar of cancer therapy with demonstrated efficacy in a variety of malignancies. However, they are associated with immune-related adverse events (irAEs) that affect many organ systems with varying severity, inhibiting patient quality of life and in some cases the ability to continue immunotherapy. Research into irAEs is nascent, and identifying patients with adverse events poses a critical challenge for future research efforts and patient care. This study's objective was to develop an electronic health record (EHR)-based model to identify and characterize patients with ICI-associated arthritis (checkpoint arthritis).MethodsForty-two patients with checkpoint arthritis were chart abstracted from a cohort of all patients who received checkpoint therapy for cancer (n=2,612) in a single-center retrospective study. All EHR clinical codes (N=32,198) were extracted including International Classification of Diseases (ICD)-9 and ICD-10, Logical Observation Identifiers Names and Codes (LOINC), RxNorm, and Current Procedural Terminology (CPT). Logistic regression, random forest, gradient boosting, support vector machine, K-nearest neighbors, and neural network machine learning models were trained to identify checkpoint arthritis patients using these clinical codes. Models were evaluated using receiver operating characteristic area under the curve (ROC-AUC), and the most important variables were determined from the logistic regression model. Models were retrained on smaller fractions of the important variables to determine the minimum variable set necessary to achieve accurate identification of checkpoint arthritis.ResultsLogistic regression and random forest were the highest performing models on the full variable set of 32,198 clinical codes (AUCs: 0.911, 0.894, respectively) (table 1). Retraining the models on smaller fractions of the most important variables demonstrated peak performance using the top 31 clinical codes, or 0.1% of the total variables (figure 1). The most important features included presence of ESR, CRP, rheumatoid factor lab, prednisone, joint pain, creatine kinase lab, thyroid labs, and immunization, all positively associated with checkpoint arthritis (figure 2).ConclusionsOur study demonstrates that a data-driven, EHR based approach can robustly identify checkpoint arthritis patients. The high performance of the models using only the 0.1% most important variables suggests that only a small number of clinical attributes are needed to identify these patients. The variables most important for identifying checkpoint arthritis included several unexpected clinical features, such as thyroid labs and immunization, indicating potential underlying irAE associations that warrant further exploration. Finally, the flexibility of this approach and its demonstrated effectiveness could be applied to identify and characterize other irAEs.Ethics ApprovalThis study was approved by the Northwestern University Institutional Review Board, ID STU00210502, with a granted waiver of consentAbstract 802 Table 1Model performance metricsAUC was calculated from the ROC curve. Sensitivity, specificity, PPV, and NPV were determined at the threshold maximizing the F1-score. AUC = area under the curve, ROC = receiver operating characteristic, PPV = positive predictive value, NPV = negative predictive valueAbstract 802 Figure 1Model AUC trained on decreasing fractions of the most important variables, determined by the random forest model. 100% = 32,198 clinical codes. LReg = logistic regression, RF = random forest, GB = gradient boosting, NN = neural network, KNN = K-nearest neighbor, SVM = support vector machine, SVMAnom = SVM anomaly detectionAbstract 802 Figure 2The 31 most important variables determined by the logistic regression (A, coefficients) and random forest (B, relative importance) models
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Partala, Juha. "Semantically Secure Symmetric Encryption with Error Correction for Distributed Storage." Security and Communication Networks 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/4321296.
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A distributed storage system (DSS) is a fundamental building block in many distributed applications. It applies linear network coding to achieve an optimal tradeoff between storage and repair bandwidth when node failures occur. Additively homomorphic encryption is compatible with linear network coding. The homomorphic property ensures that a linear combination of ciphertext messages decrypts to the same linear combination of the corresponding plaintext messages. In this paper, we construct a linearly homomorphic symmetric encryption scheme that is designed for a DSS. Our proposal provides simultaneous encryption and error correction by applying linear error correcting codes. We show its IND-CPA security for a limited number of messages based on binary Goppa codes and the following assumption: when dividing a scrambled generator matrix G^ into two parts G1^ and G2^, it is infeasible to distinguish G2^ from random and to find a statistical connection between G1^ and G2^. Our infeasibility assumptions are closely related to those underlying the McEliece public key cryptosystem but are considerably weaker. We believe that the proposed problem has independent cryptographic interest.
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Shahapur, Salma S., Rajashri Khanai, and Dattaprasad A. Torse. "Performance Analysis of Error Control Codes for Underwater Wireless Acoustic Communication." Trends in Sciences 19, no. 3 (2022): 2164. http://dx.doi.org/10.48048/tis.2022.2164.
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In underwater acoustic communication, the information transmitted from 1 sensor node to another is corrupted due to errors persuaded by the noisy channel and other issues. To reduce the bit error rate, it is essential to propose suitable error regulator structure. In this paper, we simulate the performance analysis of Orthogonal Frequency Division Multiplexing Interleaver Division Multiple Access Multiple Input Multiple Output scheme with different channel codes to improve bit error rate performance. Bit error rate and consumed power are measured by communicating arbitrarily generated information through AWGN network. From the simulation results and assessment of the 2 divergent channel coding, 2 interleavers and 3 modulation techniques. We conclude that turbo codes with random interleaver and binary phase shift keying are best suitable to improve reliability performance for underwater wireless acoustic communication. To reduce the burst error in underwater acostic communication we propose an hybrid approach IDMA OFDM MIMO. BER performance is improved upto 10−6.
 HIGHLIGHTS
 
 In underwater acoustic communication to reduce bit error rate, we simulate the performance analysis of Orthogonal Frequency Division Multiplexing Interleaver Division Multiple Access Multiple Input Multiple Output scheme
 We propose a hybrid approach with 2 divergent channel coding, 2 interleavers and 3 modulation techniques
 Finally, we observe from simulation results that turbo code with binary phase shift keying and random interleaving improves bit error rate performance
 
 GRAPHICAL ABSTRACT
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Gao, Shang, Michael T. Young, John X. Qiu, et al. "Hierarchical attention networks for information extraction from cancer pathology reports." Journal of the American Medical Informatics Association 25, no. 3 (2017): 321–30. http://dx.doi.org/10.1093/jamia/ocx131.
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Abstract Objective We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents. Materials and Methods Data for our analyses were obtained from 942 deidentified pathology reports collected by the National Cancer Institute Surveillance, Epidemiology, and End Results program. The HAN was implemented for 2 information extraction tasks: (1) primary site, matched to 12 International Classification of Diseases for Oncology topography codes (7 breast, 5 lung primary sites), and (2) histological grade classification, matched to G1–G4. Model performance metrics were compared to conventional machine learning (ML) approaches including naive Bayes, logistic regression, support vector machine, random forest, and extreme gradient boosting, and other DL models, including a recurrent neural network (RNN), a recurrent neural network with attention (RNN w/A), and a convolutional neural network. Results Our results demonstrate that for both information tasks, HAN performed significantly better compared to the conventional ML and DL techniques. In particular, across the 2 tasks, the mean micro and macroF-scores for the HAN with pretraining were (0.852,0.708), compared to naive Bayes (0.518, 0.213), logistic regression (0.682, 0.453), support vector machine (0.634, 0.434), random forest (0.698, 0.508), extreme gradient boosting (0.696, 0.522), RNN (0.505, 0.301), RNN w/A (0.637, 0.471), and convolutional neural network (0.714, 0.460). Conclusions HAN-based DL models show promise in information abstraction tasks within unstructured clinical pathology reports.
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Liu, Zhenpeng, Xianwei Yang, Shichen Zhang, Yi Liu, Yonggang Zhao, and Weihua Zheng. "Automatic Generation of Test Cases Based on Genetic Algorithm and RBF Neural Network." Mobile Information Systems 2022 (May 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/1489063.
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Software testing plays an important role in improving the quality of software, but the design of test cases requires a lot of manpower, material resources, and time, and designers tend to be subjective when designing test cases. To solve this problem and make the test cases have objectivity and greater coverage, a branch coverage test case automatic generation method based on genetic algorithm and RBF neural network algorithm (GAR) is proposed. In terms of test case generation, based on the genetic algorithm optimized in this paper, a certain number of test case samples are randomly selected to train the RBF neural network to simulate the fitness function and to calculate the individual fitness value. The experiment uses 7 C language codes to automatically generate test cases and compares the experimental data generated by the branch coverage test case generation method based on adaptive genetic algorithm (PDGA), traditional genetic algorithm (SGA), and random test generation method (random) to evaluate the proposed algorithm. The experimental results show that the method is feasible and effective, the branch coverage is increased in the generation of test cases, and the number of iterations of the population is less.
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Chung, Moo K., Hyekyoung Lee, Alex DiChristofano, Hernando Ombao, and Victor Solo. "Exact topological inference of the resting-state brain networks in twins." Network Neuroscience 3, no. 3 (2019): 674–94. http://dx.doi.org/10.1162/netn_a_00091.
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A cycle in a brain network is a subset of a connected component with redundant additional connections. If there are many cycles in a connected component, the connected component is more densely connected. Whereas the number of connected components represents the integration of the brain network, the number of cycles represents how strong the integration is. However, it is unclear how to perform statistical inference on the number of cycles in the brain network. In this study, we present a new statistical inference framework for determining the significance of the number of cycles through the Kolmogorov-Smirnov (KS) distance, which was recently introduced to measure the similarity between networks across different filtration values by using the zeroth Betti number. In this paper, we show how to extend the method to the first Betti number, which measures the number of cycles. The performance analysis was conducted using the random network simulations with ground truths. By using a twin imaging study, which provides biological ground truth, the methods are applied in determining if the number of cycles is a statistically significant heritable network feature in the resting-state functional connectivity in 217 twins obtained from the Human Connectome Project. The MATLAB codes as well as the connectivity matrices used in generating results are provided at http://www.stat.wisc.edu/∼mchung/TDA .
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Debreceny, Roger S., Tawei Wang, and Mi (Jamie) Zhou. "Research in Social Media: Data Sources and Methodologies." Journal of Information Systems 33, no. 1 (2017): 1–28. http://dx.doi.org/10.2308/isys-51984.
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ABSTRACT This paper examines both the opportunities and limitations in the use of social media for accounting research. Given the dynamic nature of social media and the richness of the context, there are opportunities for researchers to directly observe communication and information exchanges, typically within the context of an observable social network. The paper provides an overview of the characteristics of four commonly used social network sites (SNSs): Facebook, Twitter, LinkedIn, and StockTwits. The data collection details, opportunities, and limitations are set out. The paper also provides illustrative examples of codes that a researcher might employ to extract information from the SNSs. To provide a comparison of accounting-relevant interactions, the paper measures the extent of posts on StockTwits, Twitter, and Facebook for a random sample of corporate announcements.
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Kim, So Yeon, Eun Kyung Choe, Manu Shivakumar, Dokyoon Kim, and Kyung-Ah Sohn. "Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer." Bioinformatics 37, no. 16 (2021): 2405–13. http://dx.doi.org/10.1093/bioinformatics/btab086.
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Abstract Motivation To better understand the molecular features of cancers, a comprehensive analysis using multi-omics data has been conducted. In addition, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene–gene graph using pathway information by assigning interactions between genes in multiple layers of networks. Results As a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene–gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets. Availability and implementation iDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW. Supplementary information Supplementary data are available at Bioinformatics online.
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Quenet, Brigitte, and David Horn. "The Dynamic Neural Filter: A Binary Model of Spatiotemporal Coding." Neural Computation 15, no. 2 (2003): 309–29. http://dx.doi.org/10.1162/089976603762552933.
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We describe and discuss the properties of a binary neural network that can serve as a dynamic neural filter (DNF), which maps regions of input space into spatiotemporal sequences of neuronal activity. Both deterministic and stochastic dynamics are studied, allowing the investigation of the stability of spatiotemporal sequences under noisy conditions. We define a measure of the coding capacity of a DNF and develop an algorithm for constructing a DNF that can serve as a source of given codes. On the basis of this algorithm, we suggest using a minimal DNF capable of generating observed sequences as a measure of complexity of spatiotemporal data. This measure is applied to experimental observations in the locust olfactory system, whose reverberating local field potential provides a natural temporal scale allowing the use of a binary DNF. For random synaptic matrices, a DNF can generate very large cycles, thus becoming an efficient tool for producing spatiotemporal codes. The latter can be stabilized by applying to the parameters of the DNF a learning algorithm with suitable margins.
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Wenderfer, Scott E., Joyce C. Chang, Amy Goodwin Davies, et al. "Using a Multi-Institutional Pediatric Learning Health System to Identify Systemic Lupus Erythematosus and Lupus Nephritis." Clinical Journal of the American Society of Nephrology 17, no. 1 (2021): 65–74. http://dx.doi.org/10.2215/cjn.07810621.
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Background and objectivesPerforming adequately powered clinical trials in pediatric diseases, such as SLE, is challenging. Improved recruitment strategies are needed for identifying patients.Design, setting, participants, & measurementsElectronic health record algorithms were developed and tested to identify children with SLE both with and without lupus nephritis. We used single-center electronic health record data to develop computable phenotypes composed of diagnosis, medication, procedure, and utilization codes. These were evaluated iteratively against a manually assembled database of patients with SLE. The highest-performing phenotypes were then evaluated across institutions in PEDSnet, a national health care systems network of >6.7 million children. Reviewers blinded to case status used standardized forms to review random samples of cases (n=350) and noncases (n=350).ResultsFinal algorithms consisted of both utilization and diagnostic criteria. For both, utilization criteria included two or more in-person visits with nephrology or rheumatology and ≥60 days follow-up. SLE diagnostic criteria included absence of neonatal lupus, one or more hydroxychloroquine exposures, and either three or more qualifying diagnosis codes separated by ≥30 days or one or more diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 100% (95% confidence interval [95% CI], 99 to 100), specificity was 92% (95% CI, 88 to 94), positive predictive value was 91% (95% CI, 87 to 94), and negative predictive value was 100% (95% CI, 99 to 100). Lupus nephritis diagnostic criteria included either three or more qualifying lupus nephritis diagnosis codes (or SLE codes on the same day as glomerular/kidney codes) separated by ≥30 days or one or more SLE diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 90% (95% CI, 85 to 94), specificity was 93% (95% CI, 89 to 97), positive predictive value was 94% (95% CI, 89 to 97), and negative predictive value was 90% (95% CI, 84 to 94). Algorithms identified 1508 children with SLE at PEDSnet institutions (537 with lupus nephritis), 809 of whom were seen in the past 12 months.ConclusionsElectronic health record–based algorithms for SLE and lupus nephritis demonstrated excellent classification accuracy across PEDSnet institutions.
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Zhang, Yuan Yuan, Shi Song Yang, and Peng Dong. "The Construction of Index System and Comprehensive Evaluation Model Based on Improved Genetic Algorithm and Fuzzy Neural Network." Applied Mechanics and Materials 20-23 (January 2010): 1229–35. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.1229.
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Artificial neural network(ANN) and genetic algorithm (GA) have both prevalent uses in large area. Along with the development of technology a method based on the combination of Artificial neural network (ANN) and genetic algorithm (GA) aroused. In such a case, the paper uses the combination of Artificial neural network(ANN) and genetic algorithm (GA) to solve the problems of costructing index system and comprehensive evaluation. Firstly establishing feedforward neural network model and make sure about the input and output variables. Secondly improved genetic algorithm is used to solve the problem of network weight and threshold value which is constitute by three steps real codes, random selection and Genetic Manipulation of Chromosome. Moreover as it know to all, error back propagation(BP) algorithm is effective in local searching so adding error back propagation(BP) algorithm to genetic algorithm is a good way to get the satisfying result. Thirdly the paper gets the output of index effectiveness. Thirdly according to the entropy theory that the summation of effective value which could be involved in the index system should be larger than a certain critical value, the paper screened out the final index. Fourthly it uses the fuzzy neural network method to establishing the comprehensive evaluation model. Finally take the evaluation for teaching quality for example to authenticate the feasibility of the method.
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Denburg, Michelle R., Hanieh Razzaghi, L. Charles Bailey, et al. "Using Electronic Health Record Data to Rapidly Identify Children with Glomerular Disease for Clinical Research." Journal of the American Society of Nephrology 30, no. 12 (2019): 2427–35. http://dx.doi.org/10.1681/asn.2019040365.
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BackgroundThe rarity of pediatric glomerular disease makes it difficult to identify sufficient numbers of participants for clinical trials. This leaves limited data to guide improvements in care for these patients.MethodsThe authors developed and tested an electronic health record (EHR) algorithm to identify children with glomerular disease. We used EHR data from 231 patients with glomerular disorders at a single center to develop a computerized algorithm comprising diagnosis, kidney biopsy, and transplant procedure codes. The algorithm was tested using PEDSnet, a national network of eight children’s hospitals with data on >6.5 million children. Patients with three or more nephrologist encounters (n=55,560) not meeting the computable phenotype definition of glomerular disease were defined as nonglomerular cases. A reviewer blinded to case status used a standardized form to review random samples of cases (n=800) and nonglomerular cases (n=798).ResultsThe final algorithm consisted of two or more diagnosis codes from a qualifying list or one diagnosis code and a pretransplant biopsy. Performance characteristics among the population with three or more nephrology encounters were sensitivity, 96% (95% CI, 94% to 97%); specificity, 93% (95% CI, 91% to 94%); positive predictive value (PPV), 89% (95% CI, 86% to 91%); negative predictive value, 97% (95% CI, 96% to 98%); and area under the receiver operating characteristics curve, 94% (95% CI, 93% to 95%). Requiring that the sum of nephrotic syndrome diagnosis codes exceed that of glomerulonephritis codes identified children with nephrotic syndrome or biopsy-based minimal change nephropathy, FSGS, or membranous nephropathy, with 94% sensitivity and 92% PPV. The algorithm identified 6657 children with glomerular disease across PEDSnet, ≥50% of whom were seen within 18 months.ConclusionsThe authors developed an EHR-based algorithm and demonstrated that it had excellent classification accuracy across PEDSnet. This tool may enable faster identification of cohorts of pediatric patients with glomerular disease for observational or prospective studies.
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Henzler, Philipp, Valentin Deschaintre, Niloy J. Mitra, and Tobias Ritschel. "Generative modelling of BRDF textures from flash images." ACM Transactions on Graphics 40, no. 6 (2021): 1–13. http://dx.doi.org/10.1145/3478513.3480507.
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We learn a latent space for easy capture, consistent interpolation, and efficient reproduction of visual material appearance. When users provide a photo of a stationary natural material captured under flashlight illumination, first it is converted into a latent material code. Then, in the second step, conditioned on the material code, our method produces an infinite and diverse spatial field of BRDF model parameters (diffuse albedo, normals, roughness, specular albedo) that subsequently allows rendering in complex scenes and illuminations, matching the appearance of the input photograph. Technically, we jointly embed all flash images into a latent space using a convolutional encoder, and -conditioned on these latent codes- convert random spatial fields into fields of BRDF parameters using a convolutional neural network (CNN). We condition these BRDF parameters to match the visual characteristics (statistics and spectra of visual features) of the input under matching light. A user study compares our approach favorably to previous work, even those with access to BRDF supervision. Project webpage: https://henzler.github.io/publication/neuralmaterial/.
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Gao, Youtao, Guocang Dong, and Limin Jin. "Design of An Intelligent Demodulation Method for X-ray Spectrum Communication." Journal of Physics: Conference Series 2447, no. 1 (2023): 012001. http://dx.doi.org/10.1088/1742-6596/2447/1/012001.
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Abstract X-ray communication based on X-ray spectrum modulation information is a novel communication method, which can effectively improve communication ability and anti-interference ability. In this paper, four characteristic X-rays are chosen to modulate information. The influence of channel attenuation, random noise in space, and miss-shooting rate caused by adjacent targets are discussed. Based on error-correcting output codes and a 10-fold cross-validation method, a neural network intelligent classifier is designed to realize intelligent information demodulation. Simulation results show that if the miss-shooting rate is lower than 0.4 and the photon retention rate after channel attenuation is higher than 0.0008, then the classifier can achieve a recognition success rate of more than 99%. This study provides reliable index requirements for the performance of the four-target material source equipment to be designed next. This paper also provides a theoretical basis for the subsequent further realization of space communication capabilities.
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Najafi, Fatemeh, Masoud Kaveh, Diego Martín, and Mohammad Reza Mosavi. "Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks." Sensors 21, no. 6 (2021): 2009. http://dx.doi.org/10.3390/s21062009.
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Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.