Low-Density Parity-Check Code Decoder Design and Error Characterization on an FPGA Based Framework

Low-Density Parity-Check (LDPC) codes have gained popularity in communication systems and standards due to their capacity approaching error correction performance. Among all the hard-decision based LDPC decoders, Gallager B (GaB), due to simplicity of its operations, poses as the most hardware friendly algorithm and an attractive solution for meeting the high-throughput demand in communication systems. However, GaB sufferers from poor error correction performance. In this work, we first propose a resource efficient GaB hardware architecture that delivers the best throughput while using fewest Field Programmable Gate Array (FPGA) resources with respect to the state of the art comparable LDPC decoding algorithms. We then introduce a Probabilistic GaB (PGaB) algorithm that disturbs the decisions made during the decoding iterations randomly with a probability value determined based on experimental studies. We achieve up to four orders of magnitude better error correction performance than the GaB with a 3.4% improvement in normalized throughput performance. PGaB requires around 40% less energy than GaB as the probabilistic execution results with reducing the average iteration count by up to 62% compared to the GaB. We also show that our PGaB consistently results with an improvement in maximum operational clock rate compared to the state of the art implementations. In this dissertation, we also present a high throughput FPGA based framework to accelerate error characterization of the LDPC codes. Our flexible framework allows the end user adjust the simulation parameters and rapidly study various LDPC codes and decoders. We first show that the connection intensive bipartite graph based LDPC decoder hardware architecture creates routing stress for longer codewords that are utilized in today's communications systems and standards. We address this problem by partitioning each processing element (PE) in the bipartite graph in such a way that the inputs of a PE are evenly distributed over its partitions. This allows depopulating the Loo Up Table (LUT) resources utilized for the decoder architecture by spreading the logic across the FPGA. We show that even though LUT usage increases, critical path delay reduces with the depopulation. More importantly, with the depopulation technique an unroutable design becomes routable, which allows longer codewords to be mapped on the FPGA. We then conduct two experiments on error correction performance analysis for the GaB and PGaB algorithms, demonstrate our framework's ability to reach a resolution level that is not attainable with general purpose processor (GPP) based simulations, which reduces the time scale of simulations to 24 hours from an estimated 199 years. We finally conduct the first study on identifying all possible codewords that are not correctable by the GaB for the case where a codeword has four errors. We reduce the time scale of this simulation that requires processing 117 billion codewords to 4 hours and 38 minutes with our framework from an estimated 7800 days on a single GPP

How to Combine Variational Bayesian Networks in Federated Learning

Federated Learning enables multiple data centers to train a central model collaboratively without exposing any confidential data. Even though deterministic models are capable of performing high prediction accuracy, their lack of calibration and capability to quantify uncertainty is problematic for safety-critical applications. Different from deterministic models, probabilistic models such as Bayesian neural networks are relatively well-calibrated and able to quantify uncertainty alongside their competitive prediction accuracy. Both of the approaches appear in the federated learning framework; however, the aggregation scheme of deterministic models cannot be directly applied to probabilistic models since weights correspond to distributions instead of point estimates. In this work, we study the effects of various aggregation schemes for variational Bayesian neural networks. With empirical results on three image classification datasets, we observe that the degree of spread for an aggregated distribution is a significant factor in the learning process. Hence, we present an investigation on the question of how to combine variational Bayesian networks in federated learning, while providing benchmarks for different aggregation settings

PAMOGK: A pathway graph kernel based multi-omics clustering approach for discovering cancer patient subgroups

Accurate classification of patients into homogeneous molecular subgroups is critical for the developmentof effective therapeutics and for deciphering what drives these different subtypes to cancer. However, the extensivemolecular heterogeneity observed among cancer patients presents a challenge. The availability of multi-omic datacatalogs for large cohorts of cancer patients provides multiple views into the molecular biology of the tumorswith unprecedented resolution. In this work, we develop PAMOGK, which integrates multi-omics patient data andincorporates the existing knowledge on biological pathways. PAMOGK is well suited to deal with the sparsity ofalterations in assessing patient similarities. We develop a novel graph kernel which we denote as smoothed shortestpath graph kernel, which evaluates patient similarities based on a single molecular alteration type in the contextof pathway. To corroborate multiple views of patients evaluated by hundreds of pathways and molecular alterationcombinations, PAMOGK uses multi-view kernel clustering. We apply PAMOGK to find subgroups of kidney renalclear cell carcinoma (KIRC) patients, which results in four clusters with significantly different survival times (p-value =7.4e-10). The patient subgroups also differ with respect to other clinical parameters such as tumor stage andgrade, and primary tumor and metastasis tumor spreads. When we compare PAMOGK to 8 other state-of-the-artexisting multi-omics clustering methods, PAMOGK consistently outperforms these in terms of its ability to partitionpatients into groups with different survival distributions. PAMOGK enables extracting the relative importance ofpathways and molecular data types. PAMOGK is available at github.com/tastanlab/pamog

Gri İlişkisel Analizi ile Bütünleştirilmiş Hata Türü ve Etkileri Analizi Yaklaşımı İçin Bir Uygulama

DergiPark: 289265tujesAlthough the Failure Mode and Effects Analysis (FMEA) is a systematic method of analysis, it has some shortcomings and limitations since it is a method based on intuitionistic and subjective statements of the person that rate the failure modes. In order to eliminate these constraints, the use of the method in conjunction with the grey relational analysis, which is one of the multi criteria decision making methods, helps to eliminate intuitionistic situations and prioritize the failure modes that need corrections and precautions.The classical FMEA and the FMEA integrated with the grey relational analysis approaches were applied, and their effectiveness was assessed in this study to identify and prioritize the failures and determine the measures to be taken in the wheat sieving machine production. For this purpose, first the Risk Priority Numbers (RPN) were calculated using the classical failure mode and effects analysis, then two separate grey RPNs were calculated on the assumptions that risk factors have either equal weight or different weight in the grey relational analysis-integrated FMEA approach, and the prioritization of the failures was performed. Three different RPN values obtained in the study were compared, and the priority optimizations to be made were recommended in order to prevent failures before reaching the customers as well as drawing the necessary conclusions accordingly.Hata Türü ve Etkileri Analizi (HTEA) sistematik bir analiz yöntemi de olsa hata türlerini değerlendiren kişilerin sübjektif ifadelerine dayanması sebebiyle aynı zamanda sezgisel de bir yöntemdir. Sezgilere dayanması ise uygulamada bazı eksikliklere ve kısıtlamalara yol açmaktadır. Bu problemleri ortadan kaldırabilmek için yöntemi çok kriterli karar verme yöntemlerinden biri olan gri ilişkisel analizi ile birlikte kullanmak, sezgisel durumları ortadan kaldırarak önlem alınmasını ve düzeltilmesi gereken hata türlerinin önceliklendirilmesini sağlamaktadır.Bu çalışmada buğday eleme makinesi üretimindeki hataların tespit edilerek önceliklendirilmesi ve alınacak önlemlerin belirlenmesi için klasik HTEA ve gri ilişkisel analizi ile bütünleştirilmiş HTEA yaklaşımları uygulanarak yaklaşımların etkinliği değerlendirilmiştir. Bunun için ilk önce klasik HTEA ile Risk Öncelik Sayıları (RÖS) daha sonra ise gri ilişkisel analizi ile bütünleştirilmiş HTEA yaklaşımıyla risk faktörlerinin hem eşit ağırlığa hem de farklı ağırlıklara sahip olduğu varsayımıyla iki ayrı gri RÖS hesaplanarak hataların önceliklendirilmesi yapılmıştır. Çalışma sonunda elde edilen üç ayrı RÖS değerleri karşılaştırılmış ve buna göre hataların müşteriye ulaşmaması için öncelikle yapılması gereken iyileştirmeler önerilmiş ve gerekli değerlendirmeler yapılmıştır

Can pretreatment hepatic artery perfusion scintigraphy in patients with liver malignancies predict the treatment response of the selective internal radiation therapy with 90Y resin microspheres?

PURPOSEWe aimed to evaluate whether the perfusion pattern from pretreatment hepatic artery perfusion scintigraphy (HAPS) in patients with liver malignancies can predict response to selective internal radiation therapy (SIRT).METHODSThis retrospective study analyzed 152 consecutive patients treated with yttrium-90 (90Y) resin microspheres between April 2015 and July 2017. HAPS using single-photon emission computed tomography/computed tomography (SPECT/CT) with 99mtechnetium macroaggregated albumin (99mTc-MAA) was performed before SIRT. Investigators visually classified perfusion patterns of tumors as heterogeneous or diffuse in HAPS. Between diffuse and heterogeneous pattern group, positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) were performed in third and sixth month after SIRT, and tumor response assessed and compared by using RECIST 1.1 or mRECIST. Overall survival (OS) and progression-free survival (PFS) were also compared with Kaplan-Meier/log-rank analyses.RESULTSOf 216 SIRT procedures, 172 were classified as heterogeneous and 44 as diffuse. Diffuse 99mTc- MAA uptake was associated with longer median OS than heterogeneous (22.2 vs. 14.4 months, respectively; P = .047). Subsegmental infusion was associated with longer OS than either lobar or segmental infusion (P = .090). Mean estimated OS was longer in patients with hepatocellular carcinoma (HCC) (34.2 months) than with colorectal carcinoma (CRC) (16.4 months) (P = .044). Patients with both diffuse and heterogeneous patterns were able to show complete response after SIRT. No statistically significant differences were observed between perfusion patterns and PFS or response rates to SIRT.CONCLUSIONAlthough tumor perfusion patterns from preplanning HAPS analyses are useful for estimating tumor uptake of 90Y, they may not reliably predict hepatic treatment response, as patients with different perfusion patterns can show clinical response to SIRT

Strong interlayer coupling in van der Waals heterostructures built from single-layer chalcogenides

Semiconductor heterostructures are the fundamental platform for many important device applications such as lasers, light-emitting diodes, solar cells and high-electron-mobility transistors. Analogous to traditional heterostructures, layered transition metal dichalcogenide (TMDC) heterostructures can be designed and built by assembling individual single-layers into functional multilayer structures, but in principle with atomically sharp interfaces, no interdiffusion of atoms, digitally controlled layered components and no lattice parameter constraints. Nonetheless, the optoelectronic behavior of this new type of van der Waals (vdW) semiconductor heterostructure is unknown at the single-layer limit. Specifically, it is experimentally unknown whether the optical transitions will be spatially direct or indirect in such hetero-bilayers. Here, we investigate artificial semiconductor heterostructures built from single layer WSe2 and MoS2 building blocks. We observe a large Stokes-like shift of ~100 meV between the photoluminescence peak and the lowest absorption peak that is consistent with a type II band alignment with spatially direct absorption but spatially indirect emission. Notably, the photoluminescence intensity of this spatially indirect transition is strong, suggesting strong interlayer coupling of charge carriers. The coupling at the hetero-interface can be readily tuned by inserting hexagonal BN (h-BN) dielectric layers into the vdW gap. The generic nature of this interlayer coupling consequently provides a new degree of freedom in band engineering and is expected to yield a new family of semiconductor heterostructures having tunable optoelectronic properties with customized composite layers.Comment: http://www.pnas.org/content/early/2014/04/10/1405435111.abstrac

Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network.

Grid cells fire in sequences that represent rapid trajectories in space. During locomotion, theta sequences encode sweeps in position starting slightly behind the animal and ending ahead of it. During quiescence and slow wave sleep, bouts of synchronized activity represent long trajectories called replays, which are well-established in place cells and have been recently reported in grid cells. Theta sequences and replay are hypothesized to facilitate many cognitive functions, but their underlying mechanisms are unknown. One mechanism proposed for grid cell formation is the continuous attractor network. We demonstrate that this established architecture naturally produces theta sequences and replay as distinct consequences of modulating external input. Driving inhibitory interneurons at the theta frequency causes attractor bumps to oscillate in speed and size, which gives rise to theta sequences and phase precession, respectively. Decreasing input drive to all neurons produces traveling wavefronts of activity that are decoded as replays

Monocyte-to-HDL-cholesterol ratio is associated with Ascending Aorta Dilatation in Patients with Bicuspid Aortic Valve

Background: The importance of monocyte count-to-HDL-cholesterol ratio (MHR) in cardio- vascular diseases has been shown in various studies. Ascending aortic dilatation (AAD) is a common complication in the patients with bicuspid aortic valve. In this study, we aimed to investigate the relationship between MHR and the presence of aortic dilatation in the patients with bicuspid aortic valve. Methods: The study population included totally 347 patients with bicuspid aortic valve.169 patients with aortic dilatation (ascending aorta diameter 65 4.0 cm) and 178 patients with no aortic dilatation. Echocardiographic and laboratory measurement was done and compared between groups. Results: The mean age of the participants was 44.7 \ub1 15.4 years and average ascending aorta diameter was 3.2 \ub1 0.3 cm in dilatation negative group and 4.4 \ub1 0.4 cm in positive group. MHR was significantly increased in in patients with aortic dilatation. MHR and uric acid level was independently associated with the presence of aortic dilatation in the patients with bicuspid aortic valve. Conclusion: We found a significant relationship between MHR and aortic dilatation in the patients with bicuspid aortic valve