Optimal Inference in Crowdsourced Classification via Belief Propagation

Crowdsourcing systems are popular for solving large-scale labelling tasks with low-paid workers. We study the problem of recovering the true labels from the possibly erroneous crowdsourced labels under the popular Dawid-Skene model. To address this inference problem, several algorithms have recently been proposed, but the best known guarantee is still significantly larger than the fundamental limit. We close this gap by introducing a tighter lower bound on the fundamental limit and proving that Belief Propagation (BP) exactly matches this lower bound. The guaranteed optimality of BP is the strongest in the sense that it is information-theoretically impossible for any other algorithm to correctly label a larger fraction of the tasks. Experimental results suggest that BP is close to optimal for all regimes considered and improves upon competing state-of-the-art algorithms.Comment: This article is partially based on preliminary results published in the proceeding of the 33rd International Conference on Machine Learning (ICML 2016

Embedding of Virtual Network Requests over Static Wireless Multihop Networks

Network virtualization is a technology of running multiple heterogeneous network architecture on a shared substrate network. One of the crucial components in network virtualization is virtual network embedding, which provides a way to allocate physical network resources (CPU and link bandwidth) to virtual network requests. Despite significant research efforts on virtual network embedding in wired and cellular networks, little attention has been paid to that in wireless multi-hop networks, which is becoming more important due to its rapid growth and the need to share these networks among different business sectors and users. In this paper, we first study the root causes of new challenges of virtual network embedding in wireless multi-hop networks, and propose a new embedding algorithm that efficiently uses the resources of the physical substrate network. We examine our algorithm's performance through extensive simulations under various scenarios. Due to lack of competitive algorithms, we compare the proposed algorithm to five other algorithms, mainly borrowed from wired embedding or artificially made by us, partially with or without the key algorithmic ideas to assess their impacts.Comment: 22 page

Iterative Bayesian Learning for Crowdsourced Regression

Crowdsourcing platforms emerged as popular venues for purchasing human intelligence at low cost for large volume of tasks. As many low-paid workers are prone to give noisy answers, a common practice is to add redundancy by assigning multiple workers to each task and then simply average out these answers. However, to fully harness the wisdom of the crowd, one needs to learn the heterogeneous quality of each worker. We resolve this fundamental challenge in crowdsourced regression tasks, i.e., the answer takes continuous labels, where identifying good or bad workers becomes much more non-trivial compared to a classification setting of discrete labels. In particular, we introduce a Bayesian iterative scheme and show that it provably achieves the optimal mean squared error. Our evaluations on synthetic and real-world datasets support our theoretical results and show the superiority of the proposed scheme

자율 주행을 위한 3D Point Cloud Data 기반 물체 탐지 및 분류 기법에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 서승우.A 3D LIDAR provides 3D surface information of objects with the highest position accuracy, among available sensors that can be utilized to develop perception algorithms for automated driving vehicles. In terms of automated driving, the accurate surface information gives the following benefits: 1) the accurate position information that is quite useful itself for collision avoidance is stably provided regardless of illumination condition, because the LIDAR is an active sensor. 2) the surface information can provide precise 3D shape-oriented features for object classification. Motivated by these characteristics, we propose three algorithms for a perception purpose of automated driving vehicles based on the 3D LIDAR in this dissertation. A very first procedure to utilize the 3D LIDAR as a perception sensor is segmentation that transform a stream of the LIDAR measurements into multiple point groups, where each point group indicate an individual object near the sensor. In chapter 2, a real-time and accurate segmentation is proposed. In particular, Gaussian Process regression is used to solve a problem called over-segmentation that increases False Positives by partitioning an object into multiple portions. The segmentation result can be utilized as input of another perception algorithm, such as object classification that is required for designing more human-likely driving strategies. For example, it is important to recognize pedestrians in urban driving environments because avoiding collisions with pedestrians are nearly a top priority. In chapter 3, we propose a pedestrian recognition algorithm based on a Deep Neural Network architecture that learns appearance variation. Another traffic participant that should be recognized with high-priority is a vehicle. Because various vehicle types of which appearances differ, such as a sedan, a bus, or a truck, are present on road, detection of the vehicles with similar performance regardless of the types is necessary. In chapter 4, we propose an algorithm that makes use of a common appearance of vehicles to solve the problem. To improve performance, a monocular camera is additionally employed, where the information from both sensors are integrated by a Dempster-Shafer Theory framework.Chapter 1 Introduction 1 1.1 Background and Motivations 1 1.2 Contributions and Outline of the Dissertation 3 1.2.1 Real-time and Accurate Segmentation of 3D Point Clouds based on Gaussian Process Regression 3 1.2.2 Pedestrian Recognition Based on Appearance Variation Learning 4 1.2.3 Vehicle Recognition using a Common Appearance Captured by a 3D LIDAR and a Monocular Camera 5 Chapter 2 Real-time and Accurate Segmentation of 3D Point Clouds based on Gaussian Process Regression 6 2.1 Introduction 6 2.2 Related Work 10 2.3 Framework overview 15 2.4 Clustering of Non-ground Points 16 2.4.1 Graph Construction 17 2.4.2 Clustering of Points on Vertical Surface 17 2.4.3 Cluster Extension 21 2.5 Accuracy Enhancement 24 2.5.1 Approach to Handling Over-segmentation 26 2.5.2 Handling Over-segmentation with GP Regression 27 2.5.3 Learning Hyperparameters 31 2.6 Experiments 32 2.6.1 Experiment Environment 32 2.6.2 Evaluation Metrics 33 2.6.3 Processing Time 36 2.6.4 Accuracy on Various Driving Environments 37 2.6.5 Impact on Tracking 46 2.7 Conclusion 48 Chapter 3 Pedestrian recognition based on appearance variation learning 50 3.1 Introduction 50 3.2 Related Work 53 3.3 Appearance Variation Learning 56 3.3.1 Primal Input Data for the Proposed Architecture 57 3.3.2 Learning Spatial Features from Appearance 57 3.3.3 Learning Appearance Variation 59 3.3.4 Classification 61 3.3.5 Data Augmentation 61 3.3.6 Implementation Detail 61 3.4 EXPERIMENTS 62 3.4.1 Experimental Environment 62 3.4.2 Experimental Results 65 3.5 CONCLUSIONS AND FUTURE WORKS 70 Chapter 4 Vehicle Recognition using a Common Appearance Captured by a 3D LIDAR and a Monocular Camera 72 4.1 Introduction 72 4.2 Related Work 75 4.3 Vehicle Recognition 77 4.3.1 Point Cloud Processing 78 4.3.2 Image Processing 80 4.3.3 Dempster-Shafer Theory (DST) for Information Fusion 82 4.4 Experiments 84 4.5 Conclusion 87 Chapter 5 Conclusion 89 Bibliography 91 국문초록 105Docto

Synergistic multi-doping effects on the Li7La3Zr2O12 solid electrolyte for fast lithium ion conduction.

Here, we investigate the doping effects on the lithium ion transport behavior in garnet Li7La3Zr2O12 (LLZO) from the combined experimental and theoretical approach. The concentration of Li ion vacancy generated by the inclusion of aliovalent dopants such as Al(3+) plays a key role in stabilizing the cubic LLZO. However, it is found that the site preference of Al in 24d position hinders the three dimensionally connected Li ion movement when heavily doped according to the structural refinement and the DFT calculations. In this report, we demonstrate that the multi-doping using additional Ta dopants into the Al-doped LLZO shifts the most energetically favorable sites of Al in the crystal structure from 24d to 96 h Li site, thereby providing more open space for Li ion transport. As a result of these synergistic effects, the multi-doped LLZO shows about three times higher ionic conductivity of 6.14 × 10(-4) S cm(-1) than that of the singly-doped LLZO with a much less efforts in stabilizing cubic phases in the synthetic condition

In vitro antioxidative activity of moss extract, and effect of moss on serum lipid level of mice fed with high-fat diet

Purpose: To evaluate the potential of active compounds derived from moss in the prevention and treatment of various diseases.Methods: Three species of moss were extracted with deionized water at 95 °C, and with 70.5 % ethanol at 85 °C. Analysis of total phenolic contents (TPC) of the extracts were performed by Folin- Ciocalteu (FC) method. The antioxidant activity of the extracts were determined using three methods, namely, by 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic) acid (ABTS), 1,1-diphenyl-2-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP). In vivo effects were evaluated in mice fed high fat diet (HFD) supplemented with 20 % ground moss. Cholesterol levels in HFD were evaluated by ophthalaldehyde method. Serum triglyceride levels were measured using triglyceride (TG) kit, while blood insulin level and leptin concentration were measured by enzyme-linked immunosorbent assay (ELISA) kit.Results: The moss extracts exhibited antioxidative effects, as evidenced of . TPC of 47.20 ± 11.20 to 119.87 ± 11.51 mg GAE/mg, respectively. ABTS scavenging activity was 1078.11 ± 18.95 to 2587.33 ± 46.19 μmol Trolox/mg, DPPH scavenging activity of were 42.11 ± 8.22 to 298.78 ± 20.02 μmol Trolox/mg, and FRAP value of 393.19 ± 24.64 to 1070.14 ± 17.92 μmol Trolox/mg, respectively. Mice fed with 20 % ground moss did not show any significant effect (p < 0.05) on visceral weight and blood lipid levels of HFD, while leptin concentrations reduced significantly to 4.74 ± 0.00 and 0.20 ± 0.00 ng/dL) relative to HFD alone (26.72 ± 6.53 ng/dL).Conclusion: Moss can potentially be used as an antioxidative ingredient, for the improvement of overall human health, suggesting that important medical benefits associated with moss consumption. However, further investigations are required to ascertain this.Keywords: Moss, total phenolic content, antioxidant activity, insulin, lepti

Synaptotagmin-12, a synaptic vesicle phosphoprotein that modulates spontaneous neurotransmitter release

Central synapses exhibit spontaneous neurotransmitter release that is selectively regulated by cAMP-dependent protein kinase A (PKA). We now show that synaptic vesicles contain synaptotagmin-12, a synaptotagmin isoform that differs from classical synaptotagmins in that it does not bind Ca2+. In synaptic vesicles, synaptotagmin-12 forms a complex with synaptotagmin-1 that prevents synaptotagmin-1 from interacting with SNARE complexes. We demonstrate that synaptotagmin-12 is phosphorylated by cAMP-dependent PKA on serine97, and show that expression of synaptotagmin-12 in neurons increases spontaneous neurotransmitter release by approximately threefold, but has no effect on evoked release. Replacing serine97 by alanine abolishes synaptotagmin-12 phosphorylation and blocks its effect on spontaneous release. Our data suggest that spontaneous synaptic-vesicle exocytosis is selectively modulated by a Ca2+-independent synaptotagmin isoform, synaptotagmin-12, which is controlled by cAMP-dependent phosphorylation

Hepatic protein kinase C is not activated despite high intracellular 1,2-sn-diacylglycerol in obese Zucker rats

AbstractHigh intracellular 1,2,-sn-diacylglycerol (DAG) usually activates protein kinase C (PKC). In choline-deficient Fischer 344 rats, we previously showed that fatty liver was associated with elevated hepatic DAG and sustained activation of PKC. Steatosis is a sequelae of many liver toxins, and we wanted to determine whether fatty liver is always associated with accumulation of DAG with activation of PKC. Obese Zucker rats had 11-fold more triacylglycerol in their livers and 2-fold more DAG in their hepatic plasma membrane than did lean control Zucker rats. However, this increased diacylglycerol was not associated with translocation or activation of PKC in hepatic plasma membrane (activity in obese rats was 897 pmol/mg protein×min−1 vs. 780 pmol/mg protein×min−1 in lean rats). No differences in PKC isoform expression were detected between obese and lean rats. In additional studies, we found that choline deficiency in the Zucker rat did not result in activation of PKC in liver, unlike our earlier observations in the choline deficient Fischer rat. This dissociation between fatty liver, DAG accumulation and PKC activation in Zucker rats supports previous reports of abnormalities in PKC signaling in this strain of rats

The Antioxidant Activity and Their Major Antioxidant Compounds from Acanthopanax senticosus and A. koreanum

The antioxidant activity and chlorogenic acid and caffeic acid contents were investigated from different parts of Acanthopanax senticosus and A. koreanum. Antioxidant activity was assessed by various in vitro assays such as DPPH, ABTS, FRAP, reducing power assays and ORAC, and the chlorogenic acid and caffeic acid were validated by HPLC chromatography. Among the various extracts, the fruit extracts of A. senticosus and A. koreanum exhibited strongest antioxidant activities including ABTS, FRAP, reducing power and ORAC, however, strongest DPPH radical scavenging activity was observed from the leaf extract of A. senticosus. In addition, the antioxidant activities of various extracts were correlated with total phenolic and proanthocyanidin contents. The major phenolic contents from various parts of these plants observed that leaf extract of A. senticosus expressed higher levels of chlorogenic acid (14.86 mg/dry weigh g) and caffeic acid (3.09 mg/dry weigh g) than other parts. Therefore, these results suggest that the leaf of A. senticosus may be an excellent natural source for functional foods and pharmaceutical agents, and the validated method was useful for the quality control of A. senticosus