161 research outputs found
DNA templated Click Chemistry via 5-vinyl-2′-deoxyuridine and an acridine-tetrazine conjugate induces DNA damage and apoptosis in cancer cells
No full text
High-dimensional non-Gaussian data analysis based on sample relationship
No full textHigh-dimensional data are omnipresent. Although many statistical methods developed for analysing high-dimensional data adopt the normality assumption, the Gaussian distribution could be a poor approximation of real data in many applications. In this thesis, we investigate how to properly analyse such high-dimensional non-Gaussian data. As quantifying sample relationships, such as measuring the inter-sample proximity and determining neighbours for samples, is an important step in numerous statistical approaches, this thesis develops three methods for analysing different high-dimensional non-Gaussian data types based on the sample relationship: dimension reduction for single cell RNA-sequencing data with missingness with a proposed proximity measure, dimension reduction for data of small counts with a developed proximity measure, and modelling skewed survival data with a proposed procedure of identifying neighbours for samples. In chapter 3, I develop an unbiased estimator of the Gram matrix, which characterises the proximity between samples. The proposed estimator improves a broad spectrum of dimension reduction methods when applied to single cell RNA-sequencing data with missingness. In addition, the consequences of directly applying existing dimension reduction methods to data with missingness are empirically and theoretically clarified. In chapter 4, I develop a dissimilarity measure for count data with an excess of zeros based on the Kullback-Leibler divergence and the empirical Bayes estimators. The proposed measure is shown to have better discriminative power compared with other popular measures. The proposed measure boosts the performance of standard dimension reduction methods on count data containing many zeros. In chapter 5, I clarify that graphs derived from features themselves can be beneficial for the analysis of high-dimensional survival data when used in graph convolutional networks. Besides, a sequential forward floating selection algorithm is proposed to simultaneously perform survival analysis and unveil the local neighbourhoods of samples with the aid of graph convolutional networks
Cooperation in the sphere of regional security strengthening – priority task of SCO
No full textThe author insists that cooperation in the sphere of security remains the main task of SCO. The achievements of recent 10 years as well as new threats and challenges for security are considered, the author argues for necessity to provide common for all members of SCO legal basis for further approaches to security issues in the region of Central Asia
Dimension Reduction for High-dimensional Small Counts with KL Divergence
No full textDimension reduction for high-dimensional count data with a large proportion of zeros is an important task in various applications. As a large number of dimension reduction methods rely on the proximity measure, we develop a dissimilarity measure that is well-suited for small counts based on the Kullback-Leibler divergence. We compare the proposed measure with other widely used dissimilarity measures and show that the proposed one has superior discrimination ability when applied to high-dimensional count data having an excess of zeros. Extensive empirical results, on both simulated and publicly-available real-world datasets that contain many zeros, demonstrate that the proposed dissimilarity measure can improve a wide range of dimension reduction methods
Enhanced reversibility of the magnetoelastic transition in (Mn,Fe)<sub>2</sub>(P,Si) alloys via minimizing the transition-induced elastic strain energy
No full textMagnetocaloric materials undergoing reversible phase transitions are highly desirable for magnetic refrigeration applications. (Mn,Fe)2(P,Si) alloys exhibit a giant magnetocaloric effect accompanied by a magnetoelastic transition, while the noticeable irreversibility causes drastic degradation of the magnetocaloric properties during consecutive cooling cycles. In the present work, we performed a comprehensive study on the magnetoelastic transition of the (Mn,Fe)2(P,Si) alloys by high-resolution transmission electron microscopy, in situ field- and temperature-dependent neutron powder diffraction as well as density functional theory calculations (DFT). We found a generalized relationship between the thermal hysteresis and the transition-induced elastic strain energy for the (Mn,Fe)2(P,Si) family. The thermal hysteresis was greatly reduced from 11 to 1 K by a mere 4 at.% substitution of Fe by Mo in the Mn1.15Fe0.80P0.45Si0.55 alloy. This reduction is found to be due to a strong reduction in the transition-induced elastic strain energy. The significantly enhanced reversibility of the magnetoelastic transition leads to a remarkable improvement of the reversible magnetocaloric properties, compared to the parent alloy. Based on the DFT calculations and the neutron diffraction experiments, we also elucidated the underlying mechanism of the tunable transition temperature for the (Mn,Fe)2(P,Si) family, which can essentially be attributed to the strong competition between the covalent bonding and the ferromagnetic exchange coupling. The present work provides not only a new strategy to improve the reversibility of a first-order magnetic transition but also essential insight into the electron-spin-lattice coupling in giant magnetocaloric materials.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.RST/Fundamental Aspects of Materials and Energ
On the Translation of English Hard News under Inter-cultural Background
No full textThe author firstly introduced the characteristics of hard news form the perspectives of the structure, linguistic features and language style. Secondly, numerous cultural background factors are classified into six types including geographical environment, life style, traditional customs, religious beliefs, historical allusion and literature connotations which are the underlying causes of cultural barriers occurred in the hard news translation. To remove the cultural barriers, based on the Lawrence Venuti’s (1995) foreignization and domesticaiton translation theory, the paper presents principles for hard news translation. By giving a large number of instances of hard news translations, this paper mainly focuses on such issues as the characteristics of hard news, the influence of the inter-culture on the hard news translation, and relevant translation strategies, and last but not least, attaches significance to the inter-culture awareness of the translator during the process of hard news translation.</jats:p
Survival Analysis of High-Dimensional Data With Graph Convolutional Networks and Geometric Graphs
No full textThis article proposes a survival model based on graph convolutional networks (GCNs) with geometric graphs directly constructed from high-dimensional features. First, we clarify that the graphs used in GCNs play an important role in processing the relational information of samples, and the graphs that align well with the underlying data structure could be beneficial for survival analysis. Second, we show that sparse geometric graphs derived from high-dimensional data are more favorable compared with dense graphs when used in GCNs for survival analysis. Third, from this insight, we propose a model for survival analysis based on GCNs. By using multiple sparse geometric graphs and a proposed sequential forward floating selection algorithm, the new model is able to simultaneously perform survival analysis and unveil the local neighborhoods of samples. The experimental results on real-world datasets show that the proposed survival analysis approach based on GCNs outperforms a variety of existing methods and indicate that geometric graphs can aid survival analysis of high-dimensional data
A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models
No full textThis is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant
2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant
2009I0016
Wireless network security status in Oulu : war-driving
No full textWireless networks have improvement not only in the timeliness, frequency, convenience and flexibility of connecting to the Internet, but also in economic cost and expansion of the number and location of access points that a user can connect to the internet. They have gained popularity especially after Wireless Local Area Network second evolvement about changing initial secure algorithm Wired Equivalent Privacy (WEP) to Wi-Fi Protected Access (WPA) and WPA2. WEP has been found to have vulnerabilities in cryptographic techniques and can’t defend against brute force attacks for more than a few minutes and is considered broken nowadays. WPA, a stronger encryption algorithm than WEP, made Wi-Fi a reliable network connection method.
Wi-Fi security issues have been found probably since the first wireless network was deployed, but it is widely known by people because of Peter Shipley’s wardriving experimentation and the statistic report which has been published in hacker conference in 2001. Several experiments have been carried out to reveal Wi-Fi security issues and to improve users’ awareness of Wi-Fi security. Wardriving is not a new concept, but only lately wardriving was becoming easier for wardrivers because of continued evolution of technology. The updated software and hardware that are utilized in wardriving have given this activity more economic value and attracted interest from other researchers too. But this method has not yet been used in Oulu, at least in academic research. No studies have reported about wireless network security status with wardriving method by flying a drone to discover wireless APs and most of wardriving has been done by car, walking, or biking. Furthermore, Oulu, as a technology hub with many ICT companies and citywide panOULU public Wi-Fi infrastructure, makes it an ideal location for this experiment.
What is Wi-Fi performance and security status in Oulu? Author will scan wireless networks in Oulu center area with a tool kit setting up with Raspberry Pi, Wi-Fi adaptor, GPS receiver and drone using a method called wardriving. Wardriving is the act of discovering and mapping wireless networks in a certain area and restoring access points’ data, such as an encryption standard, network name and location. The fundamental purpose is to find general information about Wi-Fi networks performance and security in Oulu center area and report the issues to raise the awareness of Wi-Fi security. Mobile devices do not need to be connected to wireless networks to be tracked. The Wi-Fi signal is transmitted continuously while a phone device tries to search for available networks. Whether discovered wireless devices quantification is indicative of local personnel density is another research question to be answered.
About 65.22% wireless APs have WPA-CCMP encryption standard and 4.2% Wi-Fi have unknown authentication in Oulu. The data showed that the majority of wireless networks in Oulu are secure. Less than 1% networks deployed WEP which has been found severe flaws in authentication method and 10% wireless access points had WPA-TKIP deployed which employed the same underlying mechanism as WEP, therefore it is vulnerable to similar attacks. The amount of insecure networks brings some concerns to the wireless network security state in Oulu.
Wardriving by drone turned out to be a more efficient method to discover wireless networks compared to wardriving on ground by walking or biking. The result also found that wireless device quantification is indicative of local personnel density, as almost everyone nowadays has a smartphone. This finding makes the Pi setup more practically usage, such as searching for lost people in forest. Thus, it becomes one future research direction, to build a real time indicator to show the direction and distance between the Pi setup and a specific wireless network device, based on the detected strength of signal
- …
