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Статті в журналах з теми "Two party clustering"
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Kumari, Priya, and Seema Maitrey. "Two Party Hierarichal Clustering Over Horizontally Partitioned Data Set." International Journal of Data Mining & Knowledge Management Process 7, no. 3 (May 30, 2017): 33–43. http://dx.doi.org/10.5121/ijdkp.2017.7303.
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Tripathy, Animesh, and Ipsa De. "Privacy Preserving Two-Party Hierarchical Clustering Over Vertically Partitioned Dataset." Journal of Software Engineering and Applications 06, no. 05 (2013): 26–31. http://dx.doi.org/10.4236/jsea.2013.65b006.
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Bunn, Paul, and Rafail Ostrovsky. "Oblivious Sampling with Applications to Two-Party k-Means Clustering." Journal of Cryptology 33, no. 3 (May 12, 2020): 1362–403. http://dx.doi.org/10.1007/s00145-020-09349-w.
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Feldman, Nizan, and Tal Sadeh. "War and Third-party Trade." Journal of Conflict Resolution 62, no. 1 (April 28, 2016): 119–42. http://dx.doi.org/10.1177/0022002716644329.
Повний текст джерелаАнотація:
Few studies explain how wars affect trade with third parties. We argue that wartime trade policies should raise trade with friendly and enemy-hostile third parties but reduce trade with hostile and enemy-friendly third parties. At the same time, the private motivation of firms and households may be incompatible with national wartime trade policies and constrain the effectiveness of wartime trade policies. Our directed dyadic data set consists of almost all of the states from 1885 to 2000. Running a high definition fixed effects regression with two-way clustering of standard errors, we find that hostile third parties tended to reduce trade with a combatant state by roughly 30 percent. In addition, trade with third parties friendly to the enemy fell by a similar magnitude. In contrast, on average, war hardly affected trade with third parties because of substitution of war-ridden markets with third-party business partners.
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Jiang, Zoe L., Ning Guo, Yabin Jin, Jiazhuo Lv, Yulin Wu, Zechao Liu, Junbin Fang, S. M. Yiu, and Xuan Wang. "Efficient two-party privacy-preserving collaborative k-means clustering protocol supporting both storage and computation outsourcing." Information Sciences 518 (May 2020): 168–80. http://dx.doi.org/10.1016/j.ins.2019.12.051.
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Lee, Hyun-Chool, and Alexandre Repkine. "Do Political Parties Represent Voters’ Preference? A Clustering Analysis of Korea’s Local Election of 2018." Philippine Political Science Journal 44, no. 2 (August 15, 2023): 121–51. http://dx.doi.org/10.1163/2165025x-bja10045.
Повний текст джерелаАнотація:
Abstract We employ the results of a survey conducted on two thousand voters that have participated in Korean local election of 2018 to perform clustering analysis of Korean voters’ preferences. We attempt to test the hypothesis of these preferences being adequately represented by the five major political parties. We find that there was likely a significant mismatch between Korean voters and the five voting camps identified under the assumption of five being an optimal number of the voting clusters. After relaxing this assumption we found that the optimal number of voting camps in Korea is two or three, suggesting that a two or a tri-partite political party system would have been a more adequate match representing Korean voters’ preferences in 2018.
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Ping, Yuan, Bin Hao, Xiali Hei, Jie Wu, and Baocang Wang. "Maximized Privacy-Preserving Outsourcing on Support Vector Clustering." Electronics 9, no. 1 (January 17, 2020): 178. http://dx.doi.org/10.3390/electronics9010178.
Повний текст джерелаАнотація:
Despite its remarkable capability in handling arbitrary cluster shapes, support vector clustering (SVC) suffers from pricey storage of kernel matrix and costly computations. Outsourcing data or function on demand is intuitively expected, yet it raises a great violation of privacy. We propose maximized privacy-preserving outsourcing on SVC (MPPSVC), which, to the best of our knowledge, is the first all-phase outsourceable solution. For privacy-preserving, we exploit the properties of homomorphic encryption and secure two-party computation. To break through the operation limitation, we propose a reformative SVC with elementary operations (RSVC-EO, the core of MPPSVC), in which a series of designs make selective outsourcing phase possible. In the training phase, we develop a dual coordinate descent solver, which avoids interactions before getting the encrypted coefficient vector. In the labeling phase, we design a fresh convex decomposition cluster labeling, by which no iteration is required by convex decomposition and no sampling checks exist in connectivity analysis. Afterward, we customize secure protocols to match these operations for essential interactions in the encrypted domain. Considering the privacy-preserving property and efficiency in a semi-honest environment, we proved MPPSVC’s robustness against adversarial attacks. Our experimental results confirm that MPPSVC achieves comparable accuracies to RSVC-EO, which outperforms the state-of-the-art variants of SVC.
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A, Pranav Shriram, Nishat Koti, Varsha Bhat Kukkala, Arpita Patra, and Bhavish Raj Gopal. "Find Thy Neighbourhood: Privacy-Preserving Local Clustering." Proceedings on Privacy Enhancing Technologies 2023, no. 2 (April 2023): 23–39. http://dx.doi.org/10.56553/popets-2023-0039.
Повний текст джерелаАнотація:
Identifying a cluster around a seed node in a graph, termed local clustering, finds use in several applications, including fraud detection, targeted advertising, community detection, etc. However, performing local clustering is challenging when the graph is distributed among multiple data owners, which is further aggravated by the privacy concerns that arise in disclosing their view of the graph. This necessitates designing solutions for privacy-preserving local clustering and is addressed for the first time in the literature. We propose using the technique of secure multiparty computation (MPC) to achieve the same. Our local clustering algorithm is based on the heat kernel PageRank (HKPR) metric, which produces the best-known cluster quality. En route to our final solution, we have two important steps: (i) designing data-oblivious equivalent of the state-of-the-art algorithms for computing local clustering and HKPR values, and (ii) compiling the data-oblivious algorithms into its secure realisation via an MPC framework that supports operations over fixed-point arithmetic representation such as multiplication and division. Keeping efficiency in mind for large graphs, we choose the best-known honest-majority 3-party framework of SWIFT (Koti et al., USENIX'21) and enhance it with some of the necessary yet missing primitives, before using it for our purpose. We benchmark the performance of our secure protocols, and the reported run time showcases the practicality of the same. Further, we perform extensive experiments to evaluate the accuracy loss of our protocols. Compared to their cleartext counterparts, we observe that the results are comparable and thus showcase the practicality of the designed protocols.
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Oswald, Michael T., Meike Fromm, and Elena Broda. "Strategic clustering in right-wing-populism? ‘Green policies’ in Germany and France." Zeitschrift für Vergleichende Politikwissenschaft 15, no. 2 (June 2021): 185–205. http://dx.doi.org/10.1007/s12286-021-00485-6.
Повний текст джерелаАнотація:
AbstractPast research pointed to the idea that right-wing ideology and climate-change skepticism are inherently linked. Empirical reality proves differently however, since right-wing populist parties are starting to adapt pro environmentalist stances. In this paper, we look into two prominent cases of diametrical diverging environmental strategies by right-wing-populist-parties: France’s Rassemblement National and Germany’s Alternative für Deutschland. In order convey this point, we use comparative qualitative content analysis and examine several decisive determinants regarding environmental strategies of right-wing populist parties. We argue that right-wing-populism is remarkably adaptable considering political opportunity structures, even clustering in ideologically diametrical versions of the same issue while each party coherently extends its policy-orientation to its respective alignment of the issue. That means, populism might be far less ideological than assumed in the past.
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Anglou, Fiorentia Zoi, Stavros Ponis, and Athanasios Spanos. "A machine learning approach to enable bulk orders of critical spare-parts in the shipping industry." Journal of Industrial Engineering and Management 14, no. 3 (July 19, 2021): 604. http://dx.doi.org/10.3926/jiem.3446.
Повний текст джерелаАнотація:
Purpose: The main purpose of this paper is to propose a methodological approach and a decision support tool, based on prescriptive analytics, to enable bulk ordering of spare parts for shipping companies operating fleets of vessels. The developed tool utilises machine learning and operations research algorithms, to forecast and optimize bulk spare parts orders needed to cover planned maintenance requirements on an annual basis and optimize the company’s purchasing decisions.Design/methodology/approach: The proposed approach consists of three discrete methodological steps, each one supported by a decision support tool based on clustering and machine learning algorithms. In the first step, clustering is applied in order to identify high interest items. Next, a forecasting tool is developed for estimating the expected needs of the fleet and to test whether the needed quantity is influenced by the source of purchase. Finally, the selected items are cost-effectively allocated to a group of vendors. The performance of the tool is assessed by running a simulation of a bulk order process on a mixed fleet totaling 75 vessels.Findings: The overall findings and approach are quite promising Indicatively, shifting demand planning focus to critical spares, via clustering, can reduce administrative workload. Furthermore, the proposed forecasting approach results in a Mean Absolute Percentage Error of 10% for specific components, with a potential for further reduction, as data availability increases. Finally, the cost optimizer can prescribe spare part acquisition scenarios that yield a 9% overall cost reduction over the span of two years.Originality/value: By adopting the proposed approach, shipping companies have the potential to produce meaningful results ranging from soft benefits, such as the rationalization of the workload of the purchasing department and its third party collaborators to hard, quantitative benefits, such as reducing the cost of the bulk ordering process, directly affecting a company’s bottom line.
Дисертації з теми "Two party clustering"
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Chim, Sonali. "Towards Effcient Privacy-Preserving Two-Party k-Means Clustering Protocol." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5180.
Повний текст джерелаАнотація:
Two-party data mining is a win-win game if played with a guarantee of data privacy from each other.
This guarantee is provided by the use of cryptographic techniques in designing the two-party protocol.
The need to obtain collaborative data mining results is growing and so is the need for privacy-preserving data mining protocols.
Clustering is one of the data mining techniques and one of the popular clustering algorithms is k-means clustering.
We studied the recent work for the secure two-party k-means clustering by Bunn and Ostrovosky and found that the protocol is inefficient for practical purposes.
The protocol requires communication rounds which are linear in security parameter for the center initialization step and are quadratic in security parameter for an iterative Lloyd's step of the k-means clustering algorithm.
The challenge in the secure two-party k-means clustering is the exorbitant communication cost occurring due to the high number of interactions between the parties for performing computations on the data.
Our work attempts to resolve this problem of inefficiency in k-means clustering protocol in a two-party setting by proposing some modifications.
We have come up with two comparison protocols that are required in the k-means clustering protocol.
One of the protocols is to find a minimum of two shared numbers which runs in constant communication rounds.
Using this protocol as a building block, another protocol is designed to find a minimum of n shared numbers, which runs in O(n) communication rounds.
We have also improved a protocol that selects a random value from a domain oblivious to both parties.
Apart from this, the idea to avoid the two-party integer division altogether is incorporated in the k-means clustering protocol.
With these improvements, we propose a two-party k-means clustering protocol for which the initialization step requires communication rounds linear in security parameter and Lloyd's step requires communications rounds that are independent of the security parameter.
The protocol provides a security guarantee in the semi-honest model except for some minor information leakage.
We argue that this leakage in the protocol can be tolerated considering the substantial gain in the communication cost
We have verified the gain in performance of the modified protocol by implementing both the k-means clustering protocols and running their instances in the same set-up.
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Sousa, David Rodrigues Alves de. "The contributing factors of large wildfires : exploring the main structural factors driving large wildfire ignition and spread in central Portugal (2005-2015)." Master's thesis, 2019. http://hdl.handle.net/10362/59016.
Повний текст джерелаАнотація:
Dissertation presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management, with a specialization in Information Analysis and ManagementLarge wildfires have devastating human, environmental and economic consequences and are responsible for the majority of total burned area in Mediterranean Europe, even though they account for only a marginal portion of all fire occurrences. Most predictions suggest a global intensification of fire danger, and among all European Mediterranean countries Portugal displays the highest fire incidence. The purpose of this work is to examine the main factors driving large wildfire ignition and spread in central Portugal between 2005 and 2015, contributing with empiric knowledge on their importance and variability throughout the study area. This research was successful at listing a comprehensive set of elements contributing to fire occurrence and at gathering data on these phenomena. Spatial cluster analysis was used to find homogeneous regions within the study area concerning the main factors influencing both fire ignition and burned area. Probit and two-part regression techniques were used to model the contribution of the different elements driving large fire occurrence and propagation. The main findings of this analysis confirm the presence of spatial variability in the contribution exerted by most structural factors driving large wildfire ignition and spread in central Portugal. Additionally, while vegetation characteristics appear much more relevant for fire propagation, socioeconomic elements seem to be connected to fire incidence. All in all, this research provides relevant input with implementation in different fields, from large fire awareness and prevention to the development of wildfire policies, as well as appropriate contributions to methodological concerns in fire danger and fire risk analyses.
Os grandes incêndios rurais têm como consequência impactos socioeconómicos e ambientais devastadores e são responsáveis pela maior parte do total de área ardida na Europa mediterrânica, ainda que representem apenas uma fração pouco expressiva do total de ocorrências. A maioria dos estudos prevê uma intensificação do perigo de incêndio, sendo que, entre todos os países europeus da bacia mediterrânica, é Portugal quem apresenta a mais alta incidência deste fenómeno. O objetivo deste trabalho é estudar os fatores que mais contribuíram para a ignição e propagação de grandes incêndios rurais no centro de Portugal entre 2005 e 2015, concorrendo assim com conhecimento empírico relativamente à sua importância e variabilidade na área de estudo. Esta investigação conseguiu listar um conjunto abrangente de elementos que contribuem para a ocorrência de incêndios rurais, assim como reunir os dados necessários. Uma análise de clusters espacial foi aplicada para identificar regiões homogéneas dentro da área de estudo no que respeita aos principais fatores influenciando a ignição e o alastrar dos grandes incêndios. Modelos probit e em duas partes foram utilizados para analisar a contribuição dos diferentes elementos para a ocorrência e propagação dos fogos. Os resultados deste estudo confirmam a presença de variação espacial no impacto exercido pela maioria dos fatores estruturais que contribuem para a ocorrência e propagação dos grandes incêndios rurais. Por outro lado, enquanto as características da vegetação se revelam mais relevantes na perspetiva do alastrar dos incêndios, os fatores socioeconómicos parecem estar relacionados com a ignição destes fenómenos. Em suma, este estudo contribui com informação relevante, a implementar em diferentes âmbitos, desde a consciencialização das populações à prevenção e ao desenvolvimento de políticas na área dos fogos rurais. Este apresenta ainda contributos apropriados na área de metodologias de análise do perigo e risco de incêndio.
Книги з теми "Two party clustering"
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Grigor'ev, Anatoliy, and Evgeniy Isaev. Methods and algorithms of data processing. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.
Повний текст джерелаАнотація:
The tutorial deals with selected methods and algorithms of data processing, the sequence of solving problems of processing and analysis of data to create models behavior of the object taking into account all the components of its mathematical model. Describes the types of technological methods for the use of software and hardware for solving problems in this area. The algorithms of distributions, regressions vremenny series, transform them with the aim of obtaining mathematical models and prediction of the behavior information and economic systems (objects).
The second edition is supplemented by materials that are in demand by researchers in the part of the correct use of clustering algorithms. Are elements of the classification algorithms to identify their capabilities, strengths and weaknesses. Are the procedures of justification and verify the adequacy of the results of the cluster analysis, conducted a comparison and evaluation of different clustering techniques, given information about visualization of multidimensional data and examples of practical application of clustering algorithms.
Meets the requirements of Federal state educational standards of higher education of the last generation.
For students of economic specialties, specialists, and graduate students.
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Penney, Kathryn L., Kyriaki Michailidou, Deanna Alexis Carere, Chenan Zhang, Brandon Pierce, Sara Lindström, and Peter Kraft. Genetic Epidemiology of Cancer. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0005.
Повний текст джерелаАнотація:
Chapter 5 reviews epidemiologic studies conducted to identify germline (inherited) susceptibility loci. These studies can involve associations observed within high-risk family pedigrees or in large studies of unrelated individuals. The chapter reviews the methods used to estimate the aggregate contribution of inherited genetic susceptibility and to identify specific genetic loci associated with risk. Although there is considerable variability across cancers, most cancers exhibit familial clustering, driven in part by a small number of known rare variants with large relative risks and a larger number of common variants with modest relative risks. The chapter discusses the implications of these findings for clinical care, public health, and tumor biology. It closes with a discussion of open questions, most notably the puzzle of “missing heritability”: the fact that—despite tremendous advances—multiple lines of evidence suggest that most specific risk variants, both rare and common, have yet to be discovered.
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Wang, Bin. Intraseasonal Modulation of the Indian Summer Monsoon. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.616.
Повний текст джерелаАнотація:
The strongest Indian summer monsoon (ISM) on the planet features prolonged clustered spells of wet and dry conditions often lasting for two to three weeks, known as active and break monsoons. The active and break monsoons are attributed to a quasi-periodic intraseasonal oscillation (ISO), which is an extremely important form of the ISM variability bridging weather and climate variation. The ISO over India is part of the ISO in global tropics. The latter is one of the most important meteorological phenomena discovered during the 20th century (Madden & Julian, 1971, 1972). The extreme dry and wet events are regulated by the boreal summer ISO (BSISO). The BSISO over Indian monsoon region consists of northward propagating 30–60 day and westward propagating 10–20 day modes. The “clustering” of synoptic activity was separately modulated by both the 30–60 day and 10–20 day BSISO modes in approximately equal amounts. The clustering is particularly strong when the enhancement effect from both modes acts in concert. The northward propagation of BSISO is primarily originated from the easterly vertical shear (increasing easterly winds with height) of the monsoon flows, which by interacting with the BSISO convective system can generate boundary layer convergence to the north of the convective system that promotes its northward movement. The BSISO-ocean interaction through wind-evaporation feedback and cloud-radiation feedback can also contribute to the northward propagation of BSISO from the equator. The 10–20 day oscillation is primarily produced by convectively coupled Rossby waves modified by the monsoon mean flows. Using coupled general circulation models (GCMs) for ISO prediction is an important advance in subseasonal forecasts. The major modes of ISO over Indian monsoon region are potentially predictable up to 40–45 days as estimated by multiple GCM ensemble hindcast experiments. The current dynamical models’ prediction skills for the large initial amplitude cases are approximately 20–25 days, but the prediction of developing BSISO disturbance is much more difficult than the prediction of the mature BSISO disturbances. This article provides a synthesis of our current knowledge on the observed spatial and temporal structure of the ISO over India and the important physical processes through which the BSISO regulates the ISM active-break cycles and severe weather events. Our present capability and shortcomings in simulating and predicting the monsoon ISO and outstanding issues are also discussed.
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Benestad, Rasmus. Climate in the Barents Region. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.655.
Повний текст джерелаАнотація:
The Barents Sea is a region of the Arctic Ocean named after one of its first known explorers (1594–1597), Willem Barentsz from the Netherlands, although there are accounts of earlier explorations: the Norwegian seafarer Ottar rounded the northern tip of Europe and explored the Barents and White Seas between 870 and 890 ce, a journey followed by a number of Norsemen; Pomors hunted seals and walruses in the region; and Novgorodian merchants engaged in the fur trade. These seafarers were probably the first to accumulate knowledge about the nature of sea ice in the Barents region; however, scientific expeditions and the exploration of the climate of the region had to wait until the invention and employment of scientific instruments such as the thermometer and barometer. Most of the early exploration involved mapping the land and the sea ice and making geographical observations. There were also many unsuccessful attempts to use the Northeast Passage to reach the Bering Strait. The first scientific expeditions involved F. P. Litke (1821±1824), P. K. Pakhtusov (1834±1835), A. K. Tsivol’ka (1837±1839), and Henrik Mohn (1876–1878), who recorded oceanographic, ice, and meteorological conditions.The scientific study of the Barents region and its climate has been spearheaded by a number of campaigns. There were four generations of the International Polar Year (IPY): 1882–1883, 1932–1933, 1957–1958, and 2007–2008. A British polar campaign was launched in July 1945 with Antarctic operations administered by the Colonial Office, renamed as the Falkland Islands Dependencies Survey (FIDS); it included a scientific bureau by 1950. It was rebranded as the British Antarctic Survey (BAS) in 1962 (British Antarctic Survey History leaflet). While BAS had its initial emphasis on the Antarctic, it has also been involved in science projects in the Barents region. The most dedicated mission to the Arctic and the Barents region has been the Arctic Monitoring and Assessment Programme (AMAP), which has commissioned a series of reports on the Arctic climate: the Arctic Climate Impact Assessment (ACIA) report, the Snow Water Ice and Permafrost in the Arctic (SWIPA) report, and the Adaptive Actions in a Changing Arctic (AACA) report.The climate of the Barents Sea is strongly influenced by the warm waters from the Norwegian current bringing heat from the subtropical North Atlantic. The region is 10°C–15°C warmer than the average temperature on the same latitude, and a large part of the Barents Sea is open water even in winter. It is roughly bounded by the Svalbard archipelago, northern Fennoscandia, the Kanin Peninsula, Kolguyev Island, Novaya Zemlya, and Franz Josef Land, and is a shallow ocean basin which constrains physical processes such as currents and convection. To the west, the Greenland Sea forms a buffer region with some of the strongest temperature gradients on earth between Iceland and Greenland. The combination of a strong temperature gradient and westerlies influences air pressure, wind patterns, and storm tracks. The strong temperature contrast between sea ice and open water in the northern part sets the stage for polar lows, as well as heat and moisture exchange between ocean and atmosphere. Glaciers on the Arctic islands generate icebergs, which may drift in the Barents Sea subject to wind and ocean currents.The land encircling the Barents Sea includes regions with permafrost and tundra. Precipitation comes mainly from synoptic storms and weather fronts; it falls as snow in the winter and rain in the summer. The land area is snow-covered in winter, and rivers in the region drain the rainwater and meltwater into the Barents Sea. Pronounced natural variations in the seasonal weather statistics can be linked to variations in the polar jet stream and Rossby waves, which result in a clustering of storm activity, blocking high-pressure systems. The Barents region is subject to rapid climate change due to a “polar amplification,” and observations from Svalbard suggest that the past warming trend ranks among the strongest recorded on earth. The regional change is reinforced by a number of feedback effects, such as receding sea-ice cover and influx of mild moist air from the south.
Частини книг з теми "Two party clustering"
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De, Ipsa, and Animesh Tripathy. "A Secure Two Party Hierarchical Clustering Approach for Vertically Partitioned Data Set with Accuracy Measure." In Advances in Intelligent Systems and Computing, 153–62. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01778-5_16.
Повний текст джерела
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Jiang, Zoe L., Ning Guo, Yabin Jin, Jiazhuo Lv, Yulin Wu, Yating Yu, Xuan Wang, S. M. Yiu, and Junbin Fang. "Efficient Two-Party Privacy Preserving Collaborative k-means Clustering Protocol Supporting both Storage and Computation Outsourcing." In Algorithms and Architectures for Parallel Processing, 447–60. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05063-4_34.
Повний текст джерела
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Dai, Qionghai, and Yue Gao. "Typical Hypergraph Computation Tasks." In Artificial Intelligence: Foundations, Theory, and Algorithms, 73–99. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_5.
Повний текст джерелаАнотація:
AbstractAfter hypergraph structure generation for the data, the next step is how to conduct data analysis on the hypergraph. In this chapter, we introduce four typical hypergraph computation tasks, including label propagation, data clustering, imbalance learning, and link prediction. The first typical task is label propagation, which is to predict the labels for the vertices, i.e., assigning a label to each unlabeled vertex in the hypergraph, based on the labeled information. In general cases, label propagation is to propagate the label information from labeled vertices to unlabeled vertices through structural information of the hyperedges. In this part, we discuss the hypergraph cut on hypergraphs and random walk interpretation of label propagation on hypergraphs. The second typical task is data clustering, which is formulated as dividing the vertices into several parts in a hypergraph. In this part, we introduce a hypergraph Laplacian smoothing filter and an embedded model for hypergraph clustering tasks. The third typical task is cost-sensitive learning, which targets on learning with different mis-classification costs. The fourth typical task is link prediction, which aims to discover missing relations or predict new coming hyperedges based on the observed hypergraph.
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Faouzi, Johann, and Olivier Colliot. "Classic Machine Learning Methods." In Machine Learning for Brain Disorders, 25–75. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3195-9_2.
Повний текст джерелаАнотація:
AbstractIn this chapter, we present the main classic machine learning methods. A large part of the chapter is devoted to supervised learning techniques for classification and regression, including nearest neighbor methods, linear and logistic regressions, support vector machines, and tree-based algorithms. We also describe the problem of overfitting as well as strategies to overcome it. We finally provide a brief overview of unsupervised learning methods, namely, for clustering and dimensionality reduction. The chapter does not cover neural networks and deep learning as these will be presented in Chaps. 3, 4, 5, and 6.
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Cocco, Christelle, and Raphaël Ceré. "Computer Vision and Mathematical Methods Used to Analyse Children’s Drawings of God(s)." In When Children Draw Gods, 213–44. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-94429-2_9.
Повний текст джерелаАнотація:
AbstractIn contrast to mainstream research methods in psychology, the project Children’s Drawings of Gods encompasses computer vision and mathematical methods to analyse the data (drawings and drawing annotations). The first part of the present work describes a set of methods designed to extract measures, namely features, directly from the drawings and from annotations of the images. Then, the dissimilarities between the drawings are computed based on particular features (such as the gravity centre of the smallest image unit, namely pixel, or the annotated position of god) and combined in order to measure numerically the differences between the drawings. In the second part, we conduct an exploratory data analysis based on these dissimilarities, including multidimensional scaling and clustering, in order to determine whether the chosen features permit us to distinguish the different strategies that the children used to draw god.
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Brazdil, Pavel, Jan N. van Rijn, Carlos Soares, and Joaquin Vanschoren. "Dataset Characteristics (Metafeatures)." In Metalearning, 53–75. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_4.
Повний текст джерелаАнотація:
SummaryThis chapter discusses dataset characteristics that play a crucial role in many metalearning systems. Typically, they help to restrict the search in a given configuration space. The basic characteristic of the target variable, for instance, determines the choice of the right approach. If it is numeric, it suggests that a suitable regression algorithm should be used, while if it is categorical, a classification algorithm should be used instead. This chapter provides an overview of different types of dataset characteristics, which are sometimes also referred to as metafeatures. These are of different types, and include so-called simple, statistical, information-theoretic, model-based, complexitybased, and performance-based metafeatures. The last group of characteristics has the advantage that it can be easily defined in any domain. These characteristics include, for instance, sampling landmarkers representing the performance of particular algorithms on samples of data, relative landmarkers capturing differences or ratios of performance values and providing estimates of performance gains. The final part of this chapter discusses the specific dataset characteristics used in different machine learning tasks, including classification, regression, time series, and clustering.
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Walrand, Jean. "Speech Recognition: A." In Probability in Electrical Engineering and Computer Science, 205–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49995-2_11.
Повний текст джерелаАнотація:
AbstractSpeech recognition can be formulated as the problem of guessing a sequence of words that produces a sequence of sounds. The human brain is remarkably good at solving this problem, even though the same words correspond to many different sounds, because of accents or characteristics of the voice. Moreover, the environment is always noisy, to that the listeners hear a corrupted version of the speech.Computers are getting much better at speech recognition and voice command systems are now common for smartphones (Siri), automobiles (GPS, music, and climate control), call centers, and dictation systems. In this chapter, we explain the main ideas behind the algorithms for speech recognition and for related applications.The starting point is a model of the random sequence (e.g., words) to be recognized and of how this sequence is related to the observation (e.g., voice). The main model is called a hidden Markov chain. The idea is that the successive parts of speech form a Markov chain and that each word maps randomly to some sounds. The same model is used to decode strings of symbols in communication systems.Section 11.1 is a general discussion of learning. The hidden Markov chain model used in speech recognition and in error decoding is introduced in Sect. 11.2. That section explains the Viterbi algorithm. Section 11.3 discusses expectation maximization and clustering algorithms. Section 11.4 covers learning for hidden Markov chains.
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Eeckhout, Bart, Rob Herreman, and Alexander Dhoest. "A Gay Neighborhood or Merely a Temporary Cluster of “Strange” Bars? Gay Bar Culture in Antwerp." In The Life and Afterlife of Gay Neighborhoods, 221–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66073-4_10.
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AbstractThis chapter investigates the historical permutations of those areas that come closest to qualifying as lesbian and gay neighborhoods in Antwerp, the largest city in Flanders (the northern, Dutch-speaking part of Belgium). Although Antwerp has come to be represented as the “gay capital” of Flanders, it never developed a full-fledged gay neighborhood in the Anglo-American tradition of the concept. The clustering of sexual minorities in the city has been limited largely to the economic, social, and cultural business of (nightlife) entertainment, with lesbian and gay meeting places historically concentrating in particular neighborhoods that, moreover, have shifted over time and dissipated again. The chapter’s fine-grained analysis intends to reveal geographic, social, and cultural specificities for which a more detailed understanding of both the Antwerp and the Belgian contexts is necessary. Its tripartite structure is shaped by the specific heuristic conditions set by it. Because the larger historical context for the investigated subject remains to be written, the chapter first undertakes a substantial and panoramic survey of the emergence of gay nightlife in Antwerp during the early half of the twentieth century. This provides the framework needed for a more detailed analysis in the second part, which zooms in on an area in the immediate vicinity of the Central Station and takes as its emblematic focus one sufficiently long-term and iconic gay bar, called Café Strange. Finally, the chapter zooms out again to sketch how even such a limited gay nightlife cluster in Antwerp has evaporated again in the course of the twenty-first century, leaving a landscape that is hard to map and largely virtual.
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Auyoung, Elaine. "Patterns Real and Imagined." In Politics on Display, 100–123. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190926311.003.0006.
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In this chapter, we confront the stereotype of the “yard sign war” in which the politically engaged respond to their neighbors’ displays by displaying signs of their own. Another possibility, one that is more consistent with our understanding of social interaction, is that households feel empowered by the presence of same-party signs and deterred by the presence of other-party signs. Focusing on the 2008 and 2012 presidential elections, we look for evidence of these patterns in our geo-coded observational data of sign display. To assess these competing characterizations, we introduce two measures: a clustering measure that describes the distribution of a single sign across a sample space and a joint clustering measure that captures the similarity of two signs’ spatial patterns. Overall, we find little evidence of yard sign wars and consistent evidence that Democratic and Republican signs inhabit distinct spaces in neighborhoods.
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Sarddar, Debabrata, Raktim Kumar Dey, Rajesh Bose, and Sandip Roy. "Topic Modeling as a Tool to Gauge Political Sentiments from Twitter Feeds." In Research Anthology on Social Media's Influence on Government, Politics, and Social Movements, 636–58. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7472-3.ch031.
Повний текст джерелаАнотація:
As ubiquitous as it is, the Internet has spawned a slew of products that have forever changed the way one thinks of society and politics. This article proposes a model to predict chances of a political party winning based on data collected from Twitter microblogging website, because it is the most popular microblogging platform in the world. Using unsupervised topic modeling and the NRC Emotion Lexicon, the authors demonstrate how it is possible to predict results by analyzing eight types of emotions expressed by users on Twitter. To prove the results based on empirical analysis, the authors examine the Twitter messages posted during 14th Gujarat Legislative Assembly election, 2017. Implementing two unsupervised clustering methods of K-means and Latent Dirichlet Allocation, this research shows how the proposed model is able to examine and summarize observations based on underlying semantic structures of messages posted on Twitter. These two well-known unsupervised clustering methods provide a firm base for the proposed model to enable streamlining of decision-making processes objectively.
Тези доповідей конференцій з теми "Two party clustering"
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Bunn, Paul, and Rafail Ostrovsky. "Secure two-party k-means clustering." In the 14th ACM conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1315245.1315306.
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Hamidi, Mona, Mina Sheikhalishahi, and Fabio Martinelli. "Secure Two-party Agglomerative Hierarchical Clustering Construction." In 4th International Conference on Information Systems Security and Privacy. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006659704320437.
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Akhter, Rahena, Rownak Jahan Chowdhury, Keita Emura, Tamzida Islam, Mohammad Shahriar Rahman, and Nusrat Rubaiyat. "Privacy-Preserving Two-Party k-Means Clustering in Malicious Model." In 2013 IEEE 37th International Computer Software and Applications Conference Workshops (COMPSACW). IEEE, 2013. http://dx.doi.org/10.1109/compsacw.2013.53.
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Su, Chunhua, Feng Bao, Jianying Zhou, Tsuyoshi Takagi, and Kouichi Sakurai. "Privacy-Preserving Two-Party K-Means Clustering via Secure Approximation." In 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07). IEEE, 2007. http://dx.doi.org/10.1109/ainaw.2007.295.
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Lin, Zhenmin, and Jerzy W. Jaromczyk. "Privacy preserving two-party k-means clustering over vertically partitioned dataset." In 2011 IEEE International Conference on Intelligence and Security Informatics (ISI 2011). IEEE, 2011. http://dx.doi.org/10.1109/isi.2011.5983998.
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Vatsalan, Dinusha, Peter Christen, and Vassilios S. Verykios. "Efficient two-party private blocking based on sorted nearest neighborhood clustering." In the 22nd ACM international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2505515.2505757.
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Liu, Xiaoyan, Zoe L. Jiang, S. M. Yiu, Xuan Wang, Chuting Tan, Ye Li, Zechao Liu, Yabin Jin, and Junbin Fang. "Outsourcing Two-Party Privacy Preserving K-Means Clustering Protocol in Wireless Sensor Networks." In 2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN). IEEE, 2015. http://dx.doi.org/10.1109/msn.2015.42.
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Jacintho, Lucas Henrique Mantovani, Tiago Pinho Da Silva, Antonio Rafael Sabino Parmezan, and Gustavo Enrique de Almeida Prado Alves Batista. "Brazilian Presidential Elections: Analysing Voting Patterns in Time and Space Using a Simple Data Science Pipeline." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/kdmile.2020.11979.
Повний текст джерелаАнотація:
Since 1989, the first year of the democratic presidential election after a long period of a dictatorship regime, Brazil conducted eight presidential elections. This period was marked by short and long-term shifts of power and two impeachment processes. Such instability is a case of study in electoral studies, e.g., the study of the population voting behavior. Understanding patterns in the population behavior can give us insight into factors and influences that affect the quality of democratic political decisions. In light of this, our paper focuses on analyzing the Brazilian presidential election voting behavior across the years and the Brazilian territory. Following a data science pipeline, we divided the analysis process into five steps: (i) data selection; (ii) data preprocessing; (iii) identification of spatial patterns, in which we seek to understand the role of space in the election results using spatial autocorrelation techniques; (iv) identification of temporal patterns, where we investigate similar trends of votes over the years using a hierarchical clustering method; and (v) evaluation of the results. It is noteworthy that the data in this work represents the election results at the municipal level, from 1994 to 2018, of the two most relevant parties of this period: the Brazilian Social Democracy Party (PSDB) and the Workers’ Party (PT). Through the results obtained, we found the existence of spatial dependence in every electoral year investigated. Moreover, despite the changes in the political-economic context over the years, neighboring cities seem to present similar voting behavior trends.
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Rojas Arciniegas, Alvaro J., and Harrison M. Kim. "Incorporating Security Considerations Into Optimal Product Architecture and Component Sharing Decision in Product Family Design." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28515.
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Multiple factors affect the decisions of selecting the appropriate components to share in product family design. Some of the challenges that the designers face are maintaining uniqueness and the desired performance in each variant while taking advantage of a common structure. In this paper, the sharing decision making process is analyzed for the case when a firm knows a priori that some of the components contain sensitive information that could be exposed to the user, third-party manufacturers, or undesired agents; thence, it is important to enclose it and protect it. Two important aspects to consider are defining the architecture of the product while protecting the sensitive information. This paper proposes tools to help the designers to identify components that are candidates for sharing among the family and finds the most desirable component arrangement that facilitates sharing while protecting the sensitive information that has been previously identified. The proposed framework is applied to three printers in which the architecture used for the ink cartridges and printheads are significantly different. Third-party manufacturers and remanufacturers offer their own alternatives for these subsystems (ink cartridges and printheads) since the customer for printer supplies is always looking for a cheaper alternative; meanwhile, the OEMs attempt to secure their products and retain their customers with original supplies. Having identified the sensitive components for each printer, the optimal clustering strategy is found, as well as the set of components that are candidates for sharing, according to their connectivity and the security considerations.
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Zhang, Xiaotong, Xianchao Zhang, Han Liu, and Jiebo Luo. "Multi-Task Clustering with Model Relation Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/435.
Повний текст джерелаАнотація:
Multi-task clustering improves the clustering performance of each task by transferring knowledge among the related tasks. An important aspect of multi-task clustering is to assess the task relatedness. However, to our knowledge, only two previous works have assessed the task relatedness, but they both have limitations. In this paper, we propose a multi-task clustering with model relation learning (MTCMRL) method, which automatically learns the model parameter relatedness between each pair of tasks. The objective function of MTCMRL consists of two parts: (1) within-task clustering: clustering each task by introducing linear regression model into symmetric nonnegative matrix factorization; (2) cross-task relatedness learning: updating the parameter of the linear regression model in each task by learning the model parameter relatedness between the clusters in each pair of tasks. We present an effective alternating algorithm to solve the non-convex optimization problem. Experimental results show the superiority of the proposed method over traditional single-task clustering methods and existing multi-task clustering methods.
Звіти організацій з теми "Two party clustering"
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Crowe, Jeff. PR-261-15609-R02 Machine Learning Algorithms for Smart Meter Diagnostics � Part III (TR2777). Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), June 2017. http://dx.doi.org/10.55274/r0011029.
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Our objective of this work was to investigate exclusively Daniel USMs. Sixty five thousand individual data points were used in MLA development which totaled over 18 hours of USM data from seven experimental data sets generated at three flow facilities. Six disturbance types were investigated (baseline, single elbow, double elbow out of plane, liquid, elbow header, and tee header). All experimental data was labeled with the disturbance type, if any, and deviation from baseline error. The MLA feature set was improved from the 2015 work by using gas flow conditions to compare measured and predicted flow velocities (flow profiles) and adding features that quantify the stability of the USM flow measurement. Supervised clustering and regression algorithms were fit to the labeled USM data and the accuracy of the MLAs was calculated using a cross-validation technique.
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Russo, Margherita, Fabrizio Alboni, Jorge Carreto Sanginés, Manlio De Domenico, Giuseppe Mangioni, Simone Righi, and Annamaria Simonazzi. The Changing Shape of the World Automobile Industry: A Multilayer Network Analysis of International Trade in Components and Parts. Institute for New Economic Thinking Working Paper Series, January 2022. http://dx.doi.org/10.36687/inetwp173.
Повний текст джерелаАнотація:
In 2018, after 25 years of the North America Trade Agreement (NAFTA), the United States requested new rules which, among other requirements, increased the regional con-tent in the production of automotive components and parts traded between the three part-ner countries, United States, Canada and Mexico. Signed by all three countries, the new trade agreement, USMCA, is to go into force in 2022. Nonetheless, after the 2020 Presi-dential election, the new treaty's future is under discussion, and its impact on the automo-tive industry is not entirely defined. Another significant shift in this industry – the acceler-ated rise of electric vehicles – also occurred in 2020: while the COVID-19 pandemic largely halted most plants in the automotive value chain all over the world, at the reopen-ing, the tide is now running against internal combustion engine vehicles, at least in the an-nouncements and in some large investments planned in Europe, Asia and the US. The definition of the pre-pandemic situation is a very helpful starting point for the analysis of the possible repercussions of the technological and geo-political transition, which has been accelerated by the epidemic, on geographical clusters and sectorial special-isations of the main regions and countries. This paper analyses the trade networks emerg-ing in the past 25 years in a new analytical framework. In the economic literature on inter-national trade, the study of the automotive global value chains has been addressed by us-ing network analysis, focusing on the centrality of geographical regions and countries while largely overlooking the contribution of countries' bilateral trading in components and parts as structuring forces of the subnetwork of countries and their specific position in the overall trade network. The paper focuses on such subnetworks as meso-level structures emerging in trade network over the last 25 years. Using the Infomap multilayer clustering algorithm, we are able to identify clusters of countries and their specific trades in the automotive internation-al trade network and to highlight the relative importance of each cluster, the interconnec-tions between them, and the contribution of countries and of components and parts in the clusters. We draw the data from the UN Comtrade database of directed export and import flows of 30 automotive components and parts among 42 countries (accounting for 98% of world trade flows of those items). The paper highlights the changes that occurred over 25 years in the geography of the trade relations, with particular with regard to denser and more hierarchical network gener-ated by Germany’s trade relations within EU countries and by the US preferential trade agreements with Canada and Mexico, and the upsurge of China. With a similar overall va-riety of traded components and parts within the main clusters (dominated respectively by Germany, US and Japan-China), the Infomap multilayer analysis singles out which com-ponents and parts determined the relative positions of countries in the various clusters and the changes over time in the relative positions of countries and their specialisations in mul-tilateral trades. Connections between clusters increase over time, while the relative im-portance of the main clusters and of some individual countries change significantly. The focus on US and Mexico and on Germany and Central Eastern European countries (Czech Republic, Hungary, Poland, Slovakia) will drive the comparative analysis.
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MORELLI, D. Long-distance transport of live animals: WOAH’s standards and best practices including societal perception and communication aspects. O.I.E (World Organisation for Animal Health), October 2022. http://dx.doi.org/10.20506/tt.3334.
Повний текст джерелаАнотація:
During the 88th General Session held virtually in May 2021, the World Organisation for Animal Health (WOAH: founded as OIE) Regional Commission for Europe agreed “Long-distance transport of live animals: WOAH’s standards and best practices including societal perception and communication aspects” as the Technical Item I to be presented during the 30th Conference of the Regional Commission in Catania (Italy), from 3 to 7 October 2022. An online questionnaire was designed and distributed to WOAH Members of the Regional Commission for Europe from 21 June to 8 July 2022 (with minor finalisations by 2 August 2022). The persons responsible for completing the questionnaire (of 47 Members in total) were mainly WOAH Delegates, National Focal Points for animal welfare or National Contact Points for long-distance transportation. The qualitative analysis of the information provided was carried out by grouping similar answers and, when proper, the United Nations geoscheme was applied to highlight any spatial clustering of the results. The wide majority of the Members (46 out of 47) declared to have in place specific legislation on animal welfare during transport, and most of them stated to be “generally aligned” with WOAH standards, there are still many countries in the Region where certain crucial requirements are not mandatory. Journey and contingency plans are commonly part of the specific legislation on animal welfare during transport, as well as monitoring and evaluation of the implementation of the legal requirements concerning animal transport by the Competent Authority or other certification bodies. The presence of major gaps in budget and/or available resources and trained personnel was declared by almost half of the responding Members (21 out of 47). Concerning the awareness of the civil society regarding animal welfare issues during transport, 11 Members reported a “low” level of awareness, and they were mostly included in the areas of Southern Europe, Western and Central Asia. The greatest part of Members responding “high awareness” clustered in the Northern and Western Europe geographical areas. Members were also asked to indicate possible WOAH initiatives that could improve the implementation of the standards, and most of them suggested to develop training activities and provide additional guidance through revised and/or new standards in the Terrestrial Animal Health Code and/or through other WOAH documents.
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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7587232.bard.
Повний текст джерелаАнотація:
The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel sequences involved in these processes B. Comparing expression profiles following the perception of various artificial as well as natural signals known to induce dormancy release, and searching for gene showing similar expression patterns, as candidates for further study of pathways having potential to play a central role in dormancy release. We first created targeted EST collections from V. vinifera and V. riparia mature buds. Clones were randomly selected from cDNA libraries prepared following controlled dormancy release and controlled dormancy induction and from respective controls. The entire collection (7920 vinifera and 1194 riparia clones) was sequenced and subjected to bioinformatics analysis, including clustering, annotations and GO classifications. PCR products from the entire collection were used for printing of cDNA microarrays. Bud tissue in general, and the dormant bud in particular, are under-represented within the grape EST database. Accordingly, 59% of the our vinifera EST collection, composed of 5516 unigenes, are not included within the current Vitis TIGR collection and about 22% of these transcripts bear no resemblance to any known plant transcript, corroborating the current need for our targeted EST collection and the bud specific cDNA array. Analysis of the V. riparia sequences yielded 814 unigenes, of which 140 are unique (keilin et al., manuscript, Appendix B). Results from computational expression profiling of the vinifera collection suggest that oxidative stress, calcium signaling, intracellular vesicle trafficking and anaerobic mode of carbohydrate metabolism play a role in the regulation and execution of grape-bud dormancy release. A comprehensive analysis confirmed the induction of transcription from several calcium–signaling related genes following HC treatment, and detected an inhibiting effect of calcium channel blocker and calcium chelator on HC-induced and chilling-induced bud break. It also detected the existence of HC-induced and calcium dependent protein phosphorylation activity. These data suggest, for the first time, that calcium signaling is involved in the mechanism of dormancy release (Pang et al., in preparation). We compared the effects of heat shock (HS) to those detected in buds following HC application and found that HS lead to earlier and higher bud break. We also demonstrated similar temporary reduction in catalase expression and temporary induction of ascorbate peroxidase, glutathione reductase, thioredoxin and glutathione S transferase expression following both treatments. These findings further support the assumption that temporary oxidative stress is part of the mechanism leading to bud break. The temporary induction of sucrose syntase, pyruvate decarboxylase and alcohol dehydrogenase indicate that temporary respiratory stress is developed and suggest that mitochondrial function may be of central importance for that mechanism. These finding, suggesting triggering of identical mechanisms by HS and HC, justified the comparison of expression profiles of HC and HS treated buds, as a tool for the identification of pathways with a central role in dormancy release (Halaly et al., in preparation). RNA samples from buds treated with HS, HC and water were hybridized with the cDNA arrays in an interconnected loop design. Differentially expressed genes from the were selected using R-language package from Bioconductor project called LIMMA and clones showing a significant change following both HS and HC treatments, compared to control, were selected for further analysis. A total of 1541 clones show significant induction, of which 37% have no hit or unknown function and the rest represent 661 genes with identified function. Similarly, out of 1452 clones showing significant reduction, only 53% of the clones have identified function and they represent 573 genes. The 661 induced genes are involved in 445 different molecular functions. About 90% of those functions were classified to 20 categories based on careful survey of the literature. Among other things, it appears that carbohydrate metabolism and mitochondrial function may be of central importance in the mechanism of dormancy release and studies in this direction are ongoing. Analysis of the reduced function is ongoing (Appendix A). A second set of hybridizations was carried out with RNA samples from buds exposed to short photoperiod, leading to induction of bud dormancy, and long photoperiod treatment, as control. Analysis indicated that 42 genes were significant difference between LD and SD and 11 of these were unique.