Architecting, programming, and evaluating an on-chip incoherent multi-processor memory hierarchy

New architectures for extreme-scale computing need to be designed for higher energy efficiency than current systems. The DOE-funded Traleika Glacier architecture is a recently-proposed extreme-scale manycore that radically simplifies the architecture, and proposes a cluster-based on-chip memory hierarchy without hardware cache coherence. Programming for such an environment, which can use scratchpads or incoherent caches, is challenging. Hence, this thesis focuses on architecting, programming, and evaluating an on-chip incoherent multiprocessor memory hierarchy. This thesis starts by examining incoherent multiprocessor caches. It proposes ISA support for data movement in such an environment, and two relatively user-friendly programming approaches that use the ISA. The ISA support is largely based on writeback and self-invalidation instructions, while the programming approaches involve shared-memory programming either inside a cluster only, or across clusters. The thesis also includes compiler transformations for such an incoherent cache hierarchy. Our simulation results show that, with our approach, the execution of applications on incoherent cache hierarchies can deliver reasonable performance. For execution within a cluster, the average execution time of our applications is only 2% higher than with hardware cache coherence. For execution across multiple clusters, our applications run on average 20% faster than a naive scheme that pushes all the data to the last-level shared cache. Compiler transformations for both regular and irregular applications are shown to deliver substantial performance increases. This thesis then considers scratchpads. It takes the design in the Traleika Glacier architecture and performs a simulation-based evaluation. It shows how the hardware exploits available concurrency from parallel applications. However, it also shows the limitations of the current software stack, which lacks smart memory management and high-level hints for the scheduler

Variational Noise Model Composition Through Model Perturbation for Robust Speech Recognition with Time-Varying Background Noise

Abstract This study proposes a novel model composition method to improve speech recognition performance in time-varying background noise conditions. It is suggested that each element of the cepstral coefficients represents the frequency degree of the changing components in the envelope of the log-spectrum. With this motivation, in the proposed method, variational noise models are formulated by selectively applying perturbation factors to the mean parameters of a basis model, resulting in a collection of noise models that more accurately reflect the natural range of spectral patterns seen in the log-spectral domain. The basis noise model is obtained from the silence segments of the input speech. The perturbation factors are designed separately for changes in the energy level and spectral envelope. The proposed variational model composition (VMC) method is employed to generate multiple environmental models for our previously proposed parallel combined gaussian mixture model (PCGMM) based feature compensation algorithm. The mixture sharing technique is integrated to reduce computational expenses, caused by employing the variational models. Experimental results prove that the proposed method is considerably more effective at increasing speech recognition performance in time-varying background noise conditions, with +31.31%, +10.65%, and +20.54% average relative improvements in word error rate for speech babble, background music, and real-life in-vehicle noise conditions respectively, compared to the original basic PCGMM method

DNN Transfer Learning based Non-linear Feature Extraction for Acoustic Event Classification

Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments

Intersite Coulomb Interactions in Charge Ordered Systems

Using {\it ab initio} approaches for extended Hubbard interactions coupled to phonons, we reveal that the intersite Coulomb interaction plays important roles in determining various distinctive phases of the paradigmatic charge ordered materials of Ba$_{1-x}$K$_x A$O$_3$ ($A=$ Bi and Sb). We demonstrated that all their salient doping dependent experiment features such as breathing instabilities, anomalous phonon dispersions, and transition between charge-density wave and superconducting states can be accounted very well if self-consistently obtained nearest neighbor Hubbard interaction are included, thus establishing a minimal criterion for reliable descriptions of spontaneous charge orders in solids.Comment: 4 pages, 2 additional pages for references and 4 pages supplementary materials, title and abstract are modifie

Correction to: Comprehensive proteome and phosphoproteome profiling shows negligible influence of RNAlater on protein abundance and phosphorylation

In the version of this article that was originally published [1], some information in the Acknowledgements section was omitted.This study was supported by the Collaborative Genome Program for Fostering New Post-Genome Industry (NRF-2017M3C9A5031397) and the Brain Research Program (Grant No. NRF-2017M3C7A1027472) through the National Research Foundation (NRF) funded by the Ministry of Science and ICT (MSIT) of Republic of Korea. This work was also supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (No. 2019R1C1C1006262). The Biospecimens and data used in this study were provided by the Biobank of Seoul National University Hospital, a member of Korea Biobank Network (SNUH2017-0021)

Clinicopathologic Features of Polypoid Lesions of the Gallbladder and Risk Factors of Gallbladder Cancer

It is difficult to differentiate benign and malignancy in polypoid lesions of the gallbladder (PLG) by solely depending on imaging studies. Therefore clinicopathologic features of benign and malignant polyps are compared in an attempt to identify the risk factors of malignant polypoid lesions. The medical records of 291 patients who were confirmed to have PLG through cholecystectomy were reviewed and analyzed for age, sex, symptom, associated gallstone, morphology of PLG, size of PLG, number of PLG, and preoperative tumor markers. Benign PLG was found in 256 patients (88.0%) and malignant PLG in 35 patients (12.0%). Compared with benign group, the malignant group were older (61.1 yr vs. 47.1 yr, P<0.001), more often accompanied with symptoms (62.9% vs. 28.9%, P<0.001). Malignant PLG tended to be sessile (60.0% vs. 10.5%, P<0.001), larger (28.0 mm vs. 8.6 mm, P<0.001) and single lesion (65.7% vs. 44.1%, P<0.016). Age over 60 yr (P=0.021, odds ratio [OR], 8.16), sessile morphology (P<0.001, OR, 7.70), and size over 10 mm (P=0.009, OR, 8.87) were identified as risk factors for malignant PLG. Careful decision making on therapeutic plans should be made with consideration of malignancy for patients over 60 yr, with sessile morphology of PLG, and with PLG size of over 10 mm

Defective Localization With Impaired Tumor Cytotoxicity Contributes to the Immune Escape of NK Cells in Pancreatic Cancer Patients

Tumor-infiltrating lymphocytes (TILs), found in patients with advanced pancreatic ductal adenocarcinoma (PDAC), are shown to correlate with overall survival (OS) rate. Although majority of TILs consist of CD8+/CD4+ T cells, the presence of NK cells and their role in the pathogenesis of PDAC remains elusive. We performed comprehensive analyses of TIL, PBMC, and autologous tumor cells from 80 enrolled resectable PDAC patients to comprehend the NK cell defects within PDAC. Extremely low frequencies of NK cells (&lt;0.5%) were found within PDAC tumors, which was attributable not to the low expression of tumor chemokines, but to the lack of chemokine receptor, CXCR2. Forced expression of CXCR2 in patients' NK cells rendered them capable of trafficking into PDAC. Furthermore, NK cells exhibited impaired cell-mediated killing of autologous PDAC cells, primarily due to insufficient ligation of NKG2D and DNAM-1, and failed to proliferate within the hypoxic tumor microenvironment. Importantly, these defects could be overcome by ex-vivo stimulation of NK cells from such patients. Importantly, when the proliferative capacity of NK cells in vitro was used to stratify patients on the basis of cell expansion, patients whose NK cells proliferated &lt;250-fold experienced significantly lower DFS and OS than those with ≥250-fold. Ex-vivo activation of NK cells restored tumor trafficking and reactivity, hence provided a therapeutic modality while their fold expansion could be a potentially significant prognostic indicator of OS and DFS in such patients

Management of Asymptomatic Sporadic Nonfunctioning Pancreatic Neuroendocrine Neoplasms (ASPEN) <= 2 cm: Study Protocol for a Prospective Observational Study

Introduction: The optimal treatment for small, asymptomatic, nonfunctioning pancreatic neuroendocrine neoplasms (NF-PanNEN) is still controversial. European Neuroendocrine Tumor Society (ENETS) guidelines recommend a watchful strategy for asymptomatic NF-PanNEN <2 cm of diameter. Several retrospective series demonstrated that a non-operative management is safe and feasible, but no prospective studies are available. Aim of the ASPEN study is to evaluate the optimal management of asymptomatic NF-PanNEN ≤2 cm comparing active surveillance and surgery. Methods: ASPEN is a prospective international observational multicentric cohort study supported by ENETS. The study is registered in ClinicalTrials.gov with the identification code NCT03084770. Based on the incidence of NF-PanNEN the number of expected patients to be enrolled in the ASPEN study is 1,000 during the study period (2017–2022). Primary endpoint is disease/progression-free survival, defined as the time from study enrolment to the first evidence of progression (active surveillance group) or recurrence of disease (surgery group) or death from disease. Inclusion criteria are: age >18 years, the presence of asymptomatic sporadic NF-PanNEN ≤2 cm proven by a positive fine-needle aspiration (FNA) or by the presence of a measurable nodule on high-quality imaging techniques that is positive at 68Gallium DOTATOC-PET scan. Conclusion: The ASPEN study is designed to investigate if an active surveillance of asymptomatic NF-PanNEN ≤2 cm is safe as compared to surgical approach

Class-GE2E: Speaker Verification Using Self-Attention and Transfer Learning with Loss Combination

Recent studies prove that speaker verification performance improves by employing an attention mechanism compared to using temporal and statistical pooling techniques. This paper proposes an advanced multi-head attention method, which utilizes a sorted vector of the frame-level features to consider a higher correlation. In this study, we also propose a transfer learning scheme to maximize the effectiveness of the two loss functions, which are the classifier-based cross entropy loss function and metric-based GE2E loss function, to learn the distance between embeddings. The sorted multi-head attention (SMHA) method outperforms the conventional attention methods showing 4.55% in equal error rate (EER). The proposed transfer learning scheme with Class-GE2E loss function significantly improved our attention-based systems. In particular, the EER of the SMHA decreased to 4.39% by employing transfer learning with Class-GE2E loss. The experimental results demonstrate that our effort to include a greater correlation between frame-level features for multi-head attention processing, and the combining of two different loss functions through transfer learning, is highly effective for improving speaker verification performance

Class-GE2E: Speaker Verification Using Self-Attention and Transfer Learning with Loss Combination

Recent studies prove that speaker verification performance improves by employing an attention mechanism compared to using temporal and statistical pooling techniques. This paper proposes an advanced multi-head attention method, which utilizes a sorted vector of the frame-level features to consider a higher correlation. In this study, we also propose a transfer learning scheme to maximize the effectiveness of the two loss functions, which are the classifier-based cross entropy loss function and metric-based GE2E loss function, to learn the distance between embeddings. The sorted multi-head attention (SMHA) method outperforms the conventional attention methods showing 4.55% in equal error rate (EER). The proposed transfer learning scheme with Class-GE2E loss function significantly improved our attention-based systems. In particular, the EER of the SMHA decreased to 4.39% by employing transfer learning with Class-GE2E loss. The experimental results demonstrate that our effort to include a greater correlation between frame-level features for multi-head attention processing, and the combining of two different loss functions through transfer learning, is highly effective for improving speaker verification performance