Per-Client Network Performance Isolation in VDE-based Cloud Computing Servers

Authors' final versionIn a cloud server where multiple virtual machines owned by different clients are cohosted, excessive traffic generated by a small group of clients may well jeopardize the quality of service of other clients. It is thus very important to provide per-client network performance isolation in a cloud computing environment. Unfortunately, the existing techniques are not effective enough for a huge cloud computing system since it is difficult to adopt them in a large scale and they often require non-trivial modification to the established network protocols. To overcome such difficulties, we propose per-client network performance isolation using VDE (Virtual Distributed Ethernet) as a base framework. Our approach begins with per-client weight specification and support client-aware fair share scheduling and packet dispatching for both incoming and outgoing traffic. It also provides hierarchical fairness between a client and its virtual machines. Our approach supports full virtualization of a guest OS, wide scale adoption, limited modification to the existing system, low run-time overhead and work-conserving servicing. Our experimental results show the effectiveness of the proposed approach. Every client received at least 99.4% of its bandwidth share as specified by its weight.OAIID:oai:osos.snu.ac.kr:snu2012-01/102/0000004193/4SEQ:4PERF_CD:SNU2012-01EVAL_ITEM_CD:102USER_ID:0000004193ADJUST_YN:NEMP_ID:A005174DEPT_CD:4541CITE_RATE:.175FILENAME:11-12-23 JISE-VDE.pdfDEPT_NM:전기·컴퓨터공학부EMAIL:[email protected]_YN:YCONFIRM:

Comparison of modulation efficiency between normal and degenerated primate retina

With electrical stimulation, retinal prostheses bypass dysfunctional photoreceptors and activate the surviving bipolar or retinal ganglion cells (RGCs). Therefore, the effective modulation of RGCs is crucial for developing retinal prostheses. Substantial research has been performed on the ability of an electrical stimulus to generate a reliable RGC response. However, different experimental conditions show varying levels of how well the electrical stimulation evokes RGC spikes. Therefore, in this study, we attempted to extract an indicator to understand how the electrical stimulation effectively evokes RGC spikes. Six cynomolgus monkeys were used: three as controls and three as an N-methyl-N-nitrosourea (MNU)-induced retinal degeneration model. The retinal recordings were performed using 8 × 8 multi-electrode arrays (MEAs). Electrical stimulation consisted of symmetrical biphasic pulses of varying amplitudes and durations. The number of stimulation conditions that resulted in significantly higher post-stimulation firing rates than pre-stimulus firing rates was defined as the modulation efficiency ratio (MER). The MER was significantly lower in degenerated retinas than in normal retinas. We investigated the relationship between the variables and the MER in normal and degenerated primate RGCs. External variables, such as duration and inter-electrode distance, and internal variables, such as average firing rates and statistics (mean, standard deviation, and coefficient of variation [CV]) of inter-spike intervals (ISIs) of spontaneous spikes, were used. External variables had similar effects on MER in normal and degenerated RGCs. In contrast, internal variables affected MER differently in normal and degenerated RGCs. While in normal RGCs, they were not related to MER, in degenerated RGCs, the mean ISIs were positively correlated with MER, and the CV of ISIs was negatively correlated with MER. The most important variable affecting MER was the mean ISI. A shorter ISI indicates hyperactive firing in the degenerated retina, which prevents electrical stimulation from evoking more RGCs. We believe that this hyperactivity in degenerated retinas results in a lower MER than that in the normal retina. Our findings can be used to optimize the selection of stimulation channels for in vitro MEA experiments and practical calibration methods to achieve higher efficiency when testing retinal prostheses

Stage-Dependent Changes of Visual Function and Electrical Response of the Retina in the rd10 Mouse Model

One of the critical prerequisites for the successful development of retinal prostheses is understanding the physiological features of retinal ganglion cells (RGCs) in the different stages of retinal degeneration (RD). This study used our custom-made rd10 mice, C57BL/6-Pde6bem1(R560C)Dkl/Korl mutated on the Pde6b gene in C57BL/6J mouse with the CRISPR/Cas9-based gene-editing method. We selected the postnatal day (P) 45, P70, P140, and P238 as representative ages for RD stages. The optomotor response measured the visual acuity across degeneration stages. At P45, the rd10 mice exhibited lower visual acuity than wild-type (WT) mice. At P140 and older, no optomotor response was observed. We classified RGC responses to the flashed light into ON, OFF, and ON/OFF RGCs via in vitro multichannel recording. With degeneration, the number of RGCs responding to the light stimulation decreased in all three types of RGCs. The OFF response disappeared faster than the ON response with older postnatal ages. We elicited RGC spikes with electrical stimulation and analyzed the network-mediated RGC response in the rd10 mice. Across all postnatal ages, the spikes of rd10 RGCs were less elicited by pulse amplitude modulation than in WT RGCs. The ratio of RGCs showing multiple peaks of spike burst increased in older ages. The electrically evoked RGC spikes by the pulse amplitude modulation differ across postnatal ages. Therefore, degeneration stage-dependent stimulation strategies should be considered for developing retinal prosthesis and successful vision restoration

An innovative strategy for standardized, structured, and interoperable results in ophthalmic examinations

Background Although ophthalmic devices have made remarkable progress and are widely used, most lack standardization of both image review and results reporting systems, making interoperability unachievable. We developed and validated new software for extracting, transforming, and storing information from report images produced by ophthalmic examination devices to generate standardized, structured, and interoperable information to assist ophthalmologists in eye clinics. Results We selected report images derived from optical coherence tomography (OCT). The new software consists of three parts: (1) The Area Explorer, which determines whether the designated area in the configuration file contains numeric values or tomographic images; (2) The Value Reader, which converts images to text according to ophthalmic measurements; and (3) The Finding Classifier, which classifies pathologic findings from tomographic images included in the report. After assessment of Value Reader accuracy by human experts, all report images were converted and stored in a database. We applied the Value Reader, which achieved 99.67% accuracy, to a total of 433,175 OCT report images acquired in a single tertiary hospital from 07/04/2006 to 08/31/2019. The Finding Classifier provided pathologic findings (e.g., macular edema and subretinal fluid) and disease activity. Patient longitudinal data could be easily reviewed to document changes in measurements over time. The final results were loaded into a common data model (CDM), and the cropped tomographic images were loaded into the Picture Archive Communication System. Conclusions The newly developed software extracts valuable information from OCT images and may be extended to other types of report image files produced by medical devices. Furthermore, powerful databases such as the CDM may be implemented or augmented by adding the information captured through our program.This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant No.HI19C0373). The publication cost of this article was funded by KHIDI and it had no role in the design or conduct of this researc

On the Dynamics of Inferential Behavior while Reading Expository and Narrative Texts

Inference plays a key role in reading comprehension. This study examines changes in inferential behavior while reading different genres. The inferential behavior of 28 students with reading disabilities (RDs) and 44 students without RDs was quantified while they read expository and narrative texts. First, the average rates of inference attempts and correct inferences were measured during reading. Then, the same rates were measured separately during early and late reading to see if there was a change in inferential behavior. The results show that the change in inferential behavior depends on the genre. While reading the expository text, both groups showed no significant change in their inference making. In contrast, while reading the narrative text, both groups showed higher rates of inference attempts, and only the students without RD showed a significant increase in correct inferences. The implications of these findings for the design of more engaging and effective reading programs are discussed

Group Assignments for Project-Based Learning Using Natural Language Processing—A Feasibility Study

Group learning is commonly used in a wide range of classes. However, effective methods used to form groups are not thoroughly understood. In this study, we explore a quantitative method for creating project teams based on student knowledge and interests expressed in project proposals. The proposals are encoded to vector representations, ensuring that closely related proposals yield similar vectors. During this step, two widely used natural language processing algorithms are used. The first algorithm is based solely on the frequency of words used in the text, while the other considers context information using a deep neural network. The similarity scores for the proposals generated by the two algorithms are compared with those generated by human evaluators. The proposed method was applied to a group of senior students in a capstone design course in South Korea based on their project proposals on autonomous cars written in Korean. The results indicate that the contextualized encoding scheme produces more human-like text similarity vectors compared to the word frequency-based encoding scheme. This discrepancy is discussed from a context information standpoint in this study

Group Assignments for Project-Based Learning Using Natural Language Processing—A Feasibility Study

Group learning is commonly used in a wide range of classes. However, effective methods used to form groups are not thoroughly understood. In this study, we explore a quantitative method for creating project teams based on student knowledge and interests expressed in project proposals. The proposals are encoded to vector representations, ensuring that closely related proposals yield similar vectors. During this step, two widely used natural language processing algorithms are used. The first algorithm is based solely on the frequency of words used in the text, while the other considers context information using a deep neural network. The similarity scores for the proposals generated by the two algorithms are compared with those generated by human evaluators. The proposed method was applied to a group of senior students in a capstone design course in South Korea based on their project proposals on autonomous cars written in Korean. The results indicate that the contextualized encoding scheme produces more human-like text similarity vectors compared to the word frequency-based encoding scheme. This discrepancy is discussed from a context information standpoint in this study