The Kingship of Jesus in the Gospel of john

The purpose of this thesis is to study the kingship motif with reference to the Johannine Jesus: his identity and function. To do so, I use postcolonialism as a major methodology. It leads us to an avenue from which to read the Gospel of John in the more complex and wider context, namely in the hybridised Jewish and Graeco-Roman worlds of the Roman Empire in the first century C.E. As a result, we gain a new perspective on the kingship of the Johannine Jesus, whose kingly identity is characterised by the hybridised Christological titles: Messiah, Son of God, Son of Man, Prophet, Saviour of the World, and Lord (My Lord and My God). It is stressed that these Christological terms are used in a unique and distinctive way in the Gospel of John to reveal the kingship of Jesus, particularly the title King (of the Jews) more explicitly. For the Johannine readers in the first century, who were exploited, suppressed, yet at odds with both the centre/the coloniser, and the margins/the colonised in the Roman Empire, the Gospel of John was deemed to reveal the identity of Jesus. Using many Christological titles, it presented Jesus as the universal king going beyond the Jewish Messiah(s) and the Roman emperors and also as the decoloniser who came to "his own" world to liberate his people from the darkness. The main concern of the Gospel of John manifests itself in suggesting that Jesus is the One to solve every conflict in societies. In this respect, the ideology of the Johannine Jesus is very different from that of the earthly empire. It emphasises that love, peace, freedom, service of the centre for the margins, and forgiveness are the ruling forces in the new world where the Johannine Jesus reigns as king. Raising an awareness of these ideologies, the Gospel of John asks the readers to overcome the conflicting world shrouded in darkness, thenceforth entering the new world shining in light

Adaptive Superpixel for Active Learning in Semantic Segmentation

Learning semantic segmentation requires pixel-wise annotations, which can be time-consuming and expensive. To reduce the annotation cost, we propose a superpixel-based active learning (AL) framework, which collects a dominant label per superpixel instead. To be specific, it consists of adaptive superpixel and sieving mechanisms, fully dedicated to AL. At each round of AL, we adaptively merge neighboring pixels of similar learned features into superpixels. We then query a selected subset of these superpixels using an acquisition function assuming no uniform superpixel size. This approach is more efficient than existing methods, which rely only on innate features such as RGB color and assume uniform superpixel sizes. Obtaining a dominant label per superpixel drastically reduces annotators' burden as it requires fewer clicks. However, it inevitably introduces noisy annotations due to mismatches between superpixel and ground truth segmentation. To address this issue, we further devise a sieving mechanism that identifies and excludes potentially noisy annotations from learning. Our experiments on both Cityscapes and PASCAL VOC datasets demonstrate the efficacy of adaptive superpixel and sieving mechanisms

Learning Debiased Classifier with Biased Committee

Neural networks are prone to be biased towards spurious correlations between classes and latent attributes exhibited in a major portion of training data, which ruins their generalization capability. We propose a new method for training debiased classifiers with no spurious attribute label. The key idea is to employ a committee of classifiers as an auxiliary module that identifies bias-conflicting data, i.e., data without spurious correlation, and assigns large weights to them when training the main classifier. The committee is learned as a bootstrapped ensemble so that a majority of its classifiers are biased as well as being diverse, and intentionally fail to predict classes of bias-conflicting data accordingly. The consensus within the committee on prediction difficulty thus provides a reliable cue for identifying and weighting bias-conflicting data. Moreover, the committee is also trained with knowledge transferred from the main classifier so that it gradually becomes debiased along with the main classifier and emphasizes more difficult data as training progresses. On five real-world datasets, our method outperforms prior arts using no spurious attribute label like ours and even surpasses those relying on bias labels occasionally.Comment: Conference on Neural Information Processing Systems (NeurIPS), New Orleans, 202

Active Learning for Semantic Segmentation with Multi-class Label Query

This paper proposes a new active learning method for semantic segmentation. The core of our method lies in a new annotation query design. It samples informative local image regions (e.g., superpixels), and for each of such regions, asks an oracle for a multi-hot vector indicating all classes existing in the region. This multi-class labeling strategy is substantially more efficient than existing ones like segmentation, polygon, and even dominant class labeling in terms of annotation time per click. However, it introduces the class ambiguity issue in training since it assigns partial labels (i.e., a set of candidate classes) to individual pixels. We thus propose a new algorithm for learning semantic segmentation while disambiguating the partial labels in two stages. In the first stage, it trains a segmentation model directly with the partial labels through two new loss functions motivated by partial label learning and multiple instance learning. In the second stage, it disambiguates the partial labels by generating pixel-wise pseudo labels, which are used for supervised learning of the model. Equipped with a new acquisition function dedicated to the multi-class labeling, our method outperformed previous work on Cityscapes and PASCAL VOC 2012 while spending less annotation cost

A Comparison Analysis of Surrogate Safety Measures with Car-Following Perspectives for Advanced Driver Assistance System

Surrogate Safety Measure (SSM) is one of the most widely used methods for identifying future threats, such as rear-end collision. Various SSMs have been proposed for the application of Advanced Driver Assistance Systems (ADAS), including Forward Collision Warning System (FCWS) and Emergency Braking System (EBS). The existing SSMs have been mainly used for assessing criticality of a certain traffic situation or detecting critical actions, such as severe braking maneuvers and jerking before an accident. The ADAS shows different warning signals or movements from drivers’ driving behaviours depending on the SSM employed in the system, which may lead to low reliability and low satisfaction. In order to explore the characteristics of existing SSMs in terms of human driving behaviours, this study analyzes collision risks estimated by three different SSMs, including Time-To-Collision (TTC), Stopping Headway Distance (SHD), and Deceleration-based Surrogate Safety Measure (DSSM), based on two different car-following theories, such as action point model and asymmetric driving behaviour model. The results show that the estimated collision risks of the TTC and SHD only partially match the pattern of human driving behaviour. Furthermore, the TTC and SHD overestimate the collision risk in deceleration process, particularly when the subject vehicle is faster than its preceding vehicle. On the other hand, the DSSM shows well-matched results to the pattern of the human driving behaviour. It well represents the collision risk even when the preceding vehicle moves faster than the follower one. Moreover, unlike other SSMs, the DSSM shows a balanced performance to estimate the collision risk in both deceleration and acceleration phase. These research findings suggest that the DSSM has a great potential to enhance the driver’s compliance to the ADAS, since it can reflect how the driver perceives the collision risks according to the driving behaviours in the car-following situation. Document type: Articl

Rhus verniciflua Stokes against Advanced Cancer: A Perspective from the Korean Integrative Cancer Center

Active anticancer molecules have been searched from natural products; many drugs were developed from either natural products or their derivatives following the conventional pharmaceutical paradigm of drug discovery. However, the advances in the knowledge of cancer biology have led to personalized medicine using molecular-targeted agents which create new paradigm. Clinical benefit is dependent on individual biomarker and overall survival is prolonged through cytostatic rather than cytotoxic effects to cancer cell. Therefore, a different approach is needed from the single lead compound screening model based on cytotoxicity. In our experience, the Rhus verniciflua stoke (RVS) extract traditionally used for cancer treatment is beneficial to some advanced cancer patients though it is herbal extract not single compound, and low cytotoxic in vitro. The standardized RVS extract's action mechanisms as well as clinical outcomes are reviewed here. We hope that these preliminary results would stimulate different investigation in natural products from conventional chemicals

Ion trap with gold-plated alumina: substrate and surface characterization

We describe a complete development process of a segmented-blade linear ion trap. Alumina substrate is characterized with an X-ray diffraction and loss-tangent measurement. The blade is laser-micromachined and polished, followed by the sputtering and gold electroplating. Surface roughness is examined at each step of the fabrication via both electron and optical microscopies. On the gold-plated facet, we obtain a height deviation of tens of nanometers in the vicinity of the ion position. Trapping of laser-cooled $^{174}$Yb$^{+}$ ions is demonstrated.Comment: 7 pages, 6 figure

Design of 6U Nanosatellites in Formation Flying for the Laser Crosslink Mission

With a recent growth in the volume of spaceborne data, free space optical (FSO) or laser communication systems are attracting attention, as they can enable super-high data rates faster than 1 Gbps. The Very high-speed Inter-satellite link Systems using Infrared Optical terminal and Nanosatellite (VISION) is a technical demonstration mission to establish and validate laser crosslink systems using two 6U nanosatellites in formation flying. The final goal is to achieve a Gbps-level data rate at a distance of thousands of kilometers. To establish space-to-space laser communication, the payload optical axes of each satellite should be precisely aligned during the crosslink. The payload is the laser communication terminal (LCT) including the deployable space telescope (DST), which improves optical link performances. The 6U nanosatellite bus is designed with commercial off-the shelf-(COTS) components for agile systems development. For precise formation flying, the bus is equipped a with relative navigation system with a GNSS receiver and RF crosslink, star tracker, 3-axis reaction wheels (RWs), and propulsion system. This proposed concept of the laser crosslink systems will contribute to the construction of the LEO communication constellation with high speed and secure links in future

Simulation Perspectives of Sub-1V Single-Supply Z2-FET 1T-DRAM Cells for Low-Power

With the upcoming Internet of Things (IoT), low-power devices are becoming mainstream these days. The need for memory elements able to operate at reduced biasing conditions is therefore of utmost importance. In this paper, one of the most promising capacitor-less dynamic RAM cell, the Z2-FET (zero subthreshold swings, zero impact ionization field-effect transistor), is analyzed through advanced numerical simulations to study its sub-1V operation capabilities. SiGe compounds and tuned workfunction are selected to further reduce the operating voltage to limit energy consumption. The results demonstrate functional SiGe cells with up to 75% energy reduction with respect to identical Si cells.This work was supported in part by the H2020 REMINDER European under Grant 687931, and in part by the Spanish under Project TEC2017-89800-R and Project IJCI-2016-27711