Factors Affecting Ion Thruster’s Performance

In this project, we investigated how ion thrusters produce propulsion and how the design of ion thrusters affects the performance of the thruster. In the experiment, we build a high voltage power supply (0- 50 kV) and foil rings to produce ion wind. When considering the design of the thruster, we focus on three variables: the volume of the space, where ions are produced and the electric field intensity. Thus, to investigate the first variable we made foil rings with different radius and change the distance between the ring and positive cathode. To determine the propulsion produced we use a speed sensor to determine the magnitude of the wind produced

Geo-electrical characterisation in the context of Geological Carbon Sequestration [Abstract]

Geo-electrical characterisation in the context of Geological Carbon Sequestration [Abstract

Hierarchical Pointer Net Parsing

Transition-based top-down parsing with pointer networks has achieved state-of-the-art results in multiple parsing tasks, while having a linear time complexity. However, the decoder of these parsers has a sequential structure, which does not yield the most appropriate inductive bias for deriving tree structures. In this paper, we propose hierarchical pointer network parsers, and apply them to dependency and sentence-level discourse parsing tasks. Our results on standard benchmark datasets demonstrate the effectiveness of our approach, outperforming existing methods and setting a new state-of-the-art.Comment: Accepted by EMNLP 201

戦前日本の中国人留学生予備教育 : 特設予科とその周辺

学位の種別:課程博士University of Tokyo(東京大学

On the Multiple Fault Attack on RSA Signatures with LSBs of Messages Unknown

In CHES 2009, Coron, Joux, Kizhvatov, Naccache and Paillier(CJKNP) introduced a fault attack on RSA signatures with partially unknown messages. They factored RSA modulus $N$ using a single faulty signature and increased the bound of unknown messages by multiple fault attack, however, the complexity multiple fault attack is exponential in the number of faulty signatures. At RSA 2010, it was improved which run in polynomial time in number of faults. Both previous multiple fault attacks deal with the general case that the unknown part of message is in the middle. This paper handles a special situation that some least significant bits of messages are unknown. First, we describe a sample attack by utilizing the technique of solving simultaneous diophantine approximation problem, and the bound of unknown message is $N^{\frac1{2}-\frac1{2\ell}}$ where $\ell$ is the number of faulty signatures. Our attacks are heuristic but very efficient in practice. Furthermore, the new bound can be extended up to $N^{\frac1{2}^{1+\frac1{\ell}}}$ by the Cohn-Heninger technique. Comparison between previous attacks and new attacks with LSBs of message unknown will be given by simulation test

Effects of Saline and Alkaline Stresses on Growth and Physiological Changes in Oat (Avena sativa L.) Seedlings

Two neutral salts (NaCl and Na2SO4) and alkaline salts (NaHCO3 and Na2CO3) were both mixed in 2:1 ratio, and the effects of saline and alkaline stresses on growth and physiological changes in oat seedlings were explored. The result showed that biomass, water content and chlorophyll content decreased while cell membrane permeability significantly increased under alkaline stress. Saline stress did not have an obvious effect on pH value in tissue fluids of shoot and root, but alkaline stress increased pH value in the root tissue fluid. The contents of Na+, Na+/K+, SO42- increased more, and K+, NO3-, H2PO4- decreased more under alkaline stress, the Cl- content increased obviously under saline stress but had little change under alkaline stress. The increments of proline and organic acid were both greater under alkaline stress, but organic acid content kept the same level under saline stress. Alkaline stress caused more harmful effects on growth and physiological changes in oat seedlings especially broke the pH stability in the root tissue fluid. Physiological adaptive mechanisms of oat seedlings under saline stress and alkaline stress were different, which mainly took the way of accumulating organic acid under alkali stress but accumulating Cl- under saline stress

Neural Ranking Models with Weak Supervision

Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from queries and documents when no supervised signal is available. Hence, in this paper, we propose to train a neural ranking model using weak supervision, where labels are obtained automatically without human annotators or any external resources (e.g., click data). To this aim, we use the output of an unsupervised ranking model, such as BM25, as a weak supervision signal. We further train a set of simple yet effective ranking models based on feed-forward neural networks. We study their effectiveness under various learning scenarios (point-wise and pair-wise models) and using different input representations (i.e., from encoding query-document pairs into dense/sparse vectors to using word embedding representation). We train our networks using tens of millions of training instances and evaluate it on two standard collections: a homogeneous news collection(Robust) and a heterogeneous large-scale web collection (ClueWeb). Our experiments indicate that employing proper objective functions and letting the networks to learn the input representation based on weakly supervised data leads to impressive performance, with over 13% and 35% MAP improvements over the BM25 model on the Robust and the ClueWeb collections. Our findings also suggest that supervised neural ranking models can greatly benefit from pre-training on large amounts of weakly labeled data that can be easily obtained from unsupervised IR models.Comment: In proceedings of The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2017

An Iterative Bidirectional Gradient Boosting Algorithm for CVR Baseline Estimation

This paper presents a novel iterative, bidirectional, gradient boosting (bidirectional-GB) algorithm for estimating the baseline of the Conservation Voltage Reduction (CVR) program. We define the CVR baseline as the load profile during the CVR period if the substation voltage is not lowered. The proposed algorithm consists of two key steps: selection of similar days and iterative bidirectional-GB training. In the first step, pre- and post-event temperature profiles of the targeted CVR day are used to select similar days from historical non-CVR days. In the second step, the pre-event and post-event similar days are used to train two GBMs iteratively: a forward-GBM and a backward-GBM. After each iteration, the two generated CVR baselines are reconciled and only the first and the last points on the reconciled baseline are kept. The iteration repeats until all CVR baseline points are generated. We tested two gradient boosting methods (i.e., GBM and LighGBM) with two data resolutions (i.e., 15- and 30-minute). The results demonstrate that both the accuracy and performance of the algorithm are satisfactory.Comment: 5 pages, 8 figures, 2 table

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of load profiles in one shot. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads to enable the generation of realistic synthetic load profiles in large quantity for meeting the emerging need in distribution system planning. The novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, it generates a group of load profiles bearing realistic spatial-temporal correlations in one shot. Second, two complementary metrics for evaluating realisticness of generated load profiles are developed: statistics metrics based on domain knowledge and a deep-learning classifier for comparing high-level features. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN outperforms state-of-the-art approaches in realisticness, computational efficiency, and robustness. With little finetuning, the MultiLoad-GAN approach can be readily extended to generate a group of load or PV profiles for a feeder, a substation, or a service area.Comment: Submitted to IEEE Transactions on Smart Gri

Cryptanalysis of an Identity-Based Provable Data Possession Protocol with Compressed Cloud Storage

This letter addresses some security issues of an identity-based provable data possession protocol with compressed cloud storage (published in IEEE TIFS, doi:10.1109/TIFS.2022. 3159152). Some serious flaws are identified and an attack to the protocol is designed. This attack is able to recover the ephemeral secret keys from two encrypted blocks with high probability to reveal the original plaintext file completely. Moreover, an adversary can impersonate a data owner to outsource any file to the cloud in a malicious way. The main ingredients of the attack is some classical number theoretic results