Footprint: Detecting Sybil Attacks in Urban Vehicular Networks Abstract-In urban vehicular networks, where privacy, especially the location privacy of anonymous vehicles is highly concerned, anonymous verification of vehicles is indispensable. Consequently, an attacker who succeeds in forging multiple hostile identifies can easily launch a Sybil attack, gaining a disproportionately large influence. In this paper, we propose a novel Sybil attack detection mechanism, Footprint, using the trajectories of vehicles for identification while still preserving their location privacy. More specifically, when a vehicle approaches a road-side unit (RSU), it actively demands an authorized message from the RSU as the proof of the appearance time at this RSU. We design a location-hidden authorized message generation scheme for two objectives: first, RSU signatures on messages are signer ambiguous so that the RSU location information is concealed from the resulted authorized message; second, two authorized messages signed by the same RSU within the same given period of time (temporarily linkable) are recognizable so that they can be used for identification. With the temporal limitation on the linkability of two authorized messages, authorized messages used for long-term identification are prohibited. With this scheme, vehicles can generate a location-hidden trajectory for location-privacy-preserved identification by collecting a consecutive series of authorized messages. Utilizing social relationship among trajectories according to the similarity definition of two trajectories, Footprint can recognize and therefore dismiss "communities" of Sybil trajectories. Rigorous security analysis and extensive trace-driven simulations demonstrate the efficacy of Footprint. O VER the past two decades, vehicular networks have been emerging as a cornerstone of the next-generation Intelligent Transportation Systems (ITSs), contributing to safer and more efficient roads by providing timely information to drivers and concerned authorities. In vehicular networks, moving vehicles are enabled to communicate with each other via intervehicle communications as well as with road-side units (RSUs) in vicinity via roadside-to-vehicle communications. In urban vehicular networks where the privacy, especially the location privacy of vehicles should be guaranteed [1] , [2] , vehicles need to be verified in an anonymous manner. A wide spectrum of applications in such a network relies on collaboration and information aggregation among participating vehicles. Without identities of participants, such applications are vulnerable to the Sybil attack where a malicious vehicle masquerades as multiple identities [3] , overwhelmingly influencing the result. The consequence of Sybil attack happening in vehicular networks can be vital. For example, in safety-related applications such as hazard warning, collision avoidance, and passing assistance, biased results caused by a Sybil attack can lead to severe car accidents. Therefore, it is of great importance to detect Sybil attacks from the very beginning of their happening.. Detecting Sybil attacks in urban vehicular networks, however, is very challenging. First, vehicles are anonymous. There are no chains of trust linking claimed identities to real vehicles. Second, location privacy of vehicles is of great concern. Location information of vehicles can be very confidential. For example, it can be inferred that the driver of a vehicle may be sick from knowing the vehicle is parking at a hospital. It is inhibitive to enforce a one-to-one correspondence between claimed identities to real vehicles by verifying the physical presence of a vehicle at a particular place and time. Third, conversations between vehicles are very short. Due to high mobility of vehicles, a moving vehicle can have only several seconds [4] to communicate with another occasionally encountered vehicle. It is difficult to establish certain trustworthiness among communicating vehicles in such a short time. This makes it easy for a malicious vehicle to generate a hostile identity but very hard for others to validate. Furthermore, short conversations among vehicles call for online Sybil attack detection. The detection scheme fails if a Sybil attack is detected after the attack has terminated.. To eliminate the threat of Sybil attacks, it is straightforward to explicitly bind a distinct authorized identity (e.g., PKI-based signatures) [5] , [6] , [8] to each vehicle so that each participating vehicle can represent itself only once during all communications. Using explicit identities of vehicles has the potential to completely avoid Sybil attacks but violates the anonymity concern in urban vehicular networks. As an alternative scheme, resource testing [9] , [10] , [11] can be conducted to differentiate between malicious and normal vehicles, where the judgment is made whether a number of identities possess fewer resources (e.g., computational and storage ability) than would be expected if they were distinct. This scheme fails in heterogeneous environments where malicious vehicles can easily have more resources than normal ones. Considering the fact that a vehicle can present itself at only one location at a time, localization techniques or other schemes like the Global Positioning System (GPS) aiming to provide location information of vehicles can be exploited to detect hostile identities. However, these schemes often fail in complicated urban settings (e.g., bad GPS signals due to urban canyons, inaccurate localizations due to highly dynamic wireless signal quality). Recently, two group-signature-based schemes [16] , [17] have been proposed, where a message received from multiple distinct vehicles is considered to be trustworthy. Using group signatures can provide anonymity of vehicles and suppress Sybil attacks by restraining duplicated signatures signed by the same vehicles. One practical issue of these schemes is that different messages with similar semantics may be ignored from making the decision, which leads to a biased or no final decision. As a result, there is no existing successful solution, to the best of our knowledge, to tackling the online Sybil attack detection problem in urban vehicular networks.. In this paper, we propose a novel Sybil attack detection scheme Footprint, using the trajectories of vehicles for identification while still preserving the anonymity and location privacy of vehicles. Specifically, in Footprint, when a vehicle encounters an RSU, upon request, the RSU issues an authorized message for this vehicle as the proof of its presence at this RSU and time. Intuitively, authorized messages can be utilized to identify vehicles since vehicles located at different areas can get different authorized messages. However, directly using authorized messages will leak location privacy of vehicles because knowing an authorized message of a vehicle signed by a particular RSU is equivalent to knowing the fact that the vehicle has showed up near that RSU at that time. In Footprint, we design a location-hidden authorized message generation scheme for two purposes. First, RSU signatures on messages are signer-ambiguous which means an RSU is anonymous when signing a message. In this way, the RSU location information is concealed from the final authorized message. Second, authorized messages are temporarily linkable which means two authorized messages issued from the same RSU are recognizable if and only if they are issued within the same period of time. Thus, authorized messages can be used for identification of vehicles even without knowing the specific RSUs who signed these messages. With the temporal limitation on the linkability of two authorized messages, authorized messages used for longterm identification are prohibited. Therefore, using authorized messages for identification of vehicles will not harm anonymity of vehicles.. To be uniquely identified, a vehicle collects a consecutive series of authorized messages as it keeps traveling. Such a sequence of authorized messages constitutes a trajectory of this vehicle. In Footprint, a vehicle is free to start a new trajectory by using a new temporary public key. Furthermore, a malicious vehicle can abuse this freedom to elaborately generate multiple trajectories, trying to launch a Sybil attack. Based on the observation that Sybil trajectories generated by a malicious vehicle are very alike, Footprint establishes the relationship between a pair of trajectories according to our definition of similarity. With this relationship, Sybil trajectories generated by the same malicious vehicle form a "community." By finding and eliminating "communities" of Sybil trajectories, Footprint can detect and defend against Sybil attacks.. The advantages of Footprint are fourfold. First, Footprint does not need the identities of vehicles, which ensures the anonymity of vehicles. Second, no geographical information is leaked in Footprint, which guarantees the location privacy of vehicles. Third, Footprint only needs each vehicle to be equipped with a cheap commercial GPS receiver and DSRC wireless communication module. Last, Sybil attack detection can be online independently conducted by a conversation holder (e.g., an individual vehicle or an RSU) which initializes a conversation among vehicles. Besides the advantages, the main limitation of Footprint is that Footprint requires an infrastructure of RSUs and a trust authority (TA) existing in the system in order to generate trajectories and establish trust among entities, respectively. We verify that Footprint can achieve all design objectives through security, privacy, and performance analysis and extensive trace-driven simulations which involve 2,100 taxies in Shanghai city. Footprint can largely restrict Sybil attacks and enormously reduces the impact of Sybil attacks in urban settings (above 98 percent detection rate).. The remainder of this paper is organized as follows. Section 2 introduces previous work on the Sybil attack detection problem. In Section 3, we describe the system and attack models in vehicular networks, pointing out the requirements for designing Sybil attack detection schemes in urban vehicular networks. Section 4 elaborates the design of Footprint. In Section 5, we present the security, privacy, and performance analysis of Footprint. Several design issues that may be encountered in practice are discussed in Section 6. In Section 7, we conduct trace-driven simulations to evaluate the performance of Footprint and present the results. Finally, we give concluding remarks and outline the directions for future work in Section 8. In general, Footprint integrates three elegant techniques namely, infrastructure construction, location-hidden trajectory generation, and Sybil attack detection. More specifically, we adopt an incremental methodology to deploy RSUs. In the end, a limited number of available RSUs can achieve the maximum service coverage in terms of served traffic amount as well as good fairness in terms of geographical distribution. After the deployment of RSUs, a vehicle can require authorized messages from each RSU it passes by as a proof of its presence there. We adopt an event-oriented linkable ring signature scheme [24] for RSUs to issue authorized messages for vehicles. Such authorized messages are location hidden which refers to that RSU signatures are signer ambiguous and the authorized messages are temporarily linkable. Furthermore, a set of consecutive authorized messages issued for a vehicle are tightly chained together to form a location-hidden trajectory of the vehicle, which will be utilized for identifying this vehicle in future conversations. During a conversation which is initialized by a vehicle or an RSU, called a conversation holder, a participating vehicle should provide its trajectory for verification. With the trajectories sent from all participating vehicles, the conversation holder can conduct online Sybil attack detection according to the similarity relationship between each pair of trajectories. Among all trajectories, Sybil trajectories forged from the same attacker are bound to gather within the same "community." By treating each "community" as one single vehicle, Sybil trajectories can be largely eliminated.. In the following sections, we describe each technique in details. In this paper, we have developed a Sybil attack detection scheme Footprint for urban vehicular networks. Consecutive authorized messages obtained by an anonymous vehicle from RSUs form a trajectory to identify the corresponding vehicle. Location privacy of vehicles is preserved by realizing a location-hidden signature scheme. Utilizing social relationship among trajectories, Footprint can find and eliminate Sybil trajectories. The Footprint design can be incrementally implemented in a large city. It is also demonstrated by both analysis and extensive tracedriven simulations that Footprint can largely restrict Sybil attacks and can enormously reduce the impact of Sybil attacks in urban settings (above 98 percent detection rate).. With the proposed detection mechanism having much space to extend, we will continue to work on several directions. First, in Footprint, we assume that all RSUs are trustworthy. However, if an RSU is compromised, it can help a malicious vehicle generate fake legal trajectories (e.g., by inserting link tags of other RSUs into a forged trajectory). In that case, Footprint cannot detect such trajectories. However, the corrupted RSU cannot deny a link tag generated by itself nor forge link tags generated by other RSUs, which can be utilized to detect a compromised RSU in the system. In future work, we will consider the scenario where a small fraction of RSUs are compromised. We will develop cost-efficient techniques to fast detect the corruption of an RSU. Second, we will delve into designing better linkable signer-ambiguous signature schemes such that the computation overhead for signature verification and the communication overhead can be reduced. Last, we will validate our design and study its performance under realcomplex environments based on our ongoing realistic prototype testbed built at Xi'an Jiao Tong University. Improvements will be made based on the realistic studies before it comes to be deployed in large-scale systems. While it was first described and formalized by Douceur [3] , the Sybil attack has been a severe and pervasive problem in many forms. In a Sybil attack, an attacker can launch a Sybil attack by forging multiple identifies, gaining a disproportionately large influence. In the literature, there have been many different approaches proposed to detect or mitigate the attack.. Many studies have followed Douceur's approach, focusing on how to establish trust between participating entities based on trusted public key cryptographies or certificates in distributed systems, for example, P2P systems [3] , [5] , sensor networks [6] , [7] and mobile ad hoc networks [8] . Although deploying trusted certificates is the only approach that has the potential to completely eliminate Sybil attacks, it also violates both anonymity and location privacy of entities. In addition, most of these schemes rely on a centralized authority that must ensure each entity is assigned exactly one identity. Moreover, it is possible for an attacker to violate the assumption, getting more than one identities. This mechanism also has the problem of key revocation which is challenging, particularly in wireless mobile networks.. Another category of Sybil attack detection schemes is based on resource testing [9] , [10] , [11] . The goal of resource testing is to determine if a number of identities possess fewer resources than would be expected if they were independent. The resources being tested can be computing ability, storage ability, and network bandwidth, as well as IP addresses. These schemes assume that entities have homogeneous hardware configurations. In vehicular networks, this assumption cannot hold since malicious vehicles can easily have more powerful resources than the normal vehicles.. SybilGuard [12] is an interesting scheme studying the social network among entities. In this scheme, humanestablished real-world trust relationship among users is used for detecting Sybil attacks. Since even the attacker can generate as many as Sybil identities, building relationship between honest users and Sybil identities is much harder. Thus, there exists a small "cut" on the graph of trust relationship between the forged identities and the real ones. However, this scheme cannot be used in vehicular networks, since it is very challenging to establish such trust relationship among vehicles. This is because vehicles are highly mobile. Communications often happen among temporarily met and unfamiliar vehicles.. To exploit the fact that one single vehicle cannot present at multiple locations at the same time, Bouassida et al. [13] have proposed a detection mechanism utilizing localization technique based on Received Signal Strength Indication (RSSI). In this scheme, by successively measuring the RSSI variations, the relative locations among vehicles in vicinity can be estimated. Identities with the same estimated locations are considered as Sybil vehicles. In practice, the complicated outdoor environments can dramatically affect the wireless signal propagation so that RSSI measurements are highly time variant even measured at the same location. Xiao et al. [14] have proposed a Sybil attack detection scheme where the location of a particular vehicle can be determined by the RSSI measurements taken at other participating vehicles. In addition to the inaccuracy of RSSI measurements, this scheme also needs all neighboring vehicles to collaborate which may suffer a Sybil attack against the detection scheme itself. Zhou et al. [15] have proposed a privacy-preserving Sybil attack detection scheme using pseudonyms. In the scheme, the trust authority distributes a number of pseudonyms for each vehicle. Abused pseudonyms can be detected by RSUs. Since RSUs are heavily involved in the detection process, this scheme requires the full coverage of RSUs in the field. It is infeasible in practice due to the prohibitive cost. Furthermore, in such a scheme, vehicles should managed by a centralized trusted center. Each time RSU detects suspicious pseudonyms, it should send all the pseudonyms to the trust center for further decision, which makes the trust center be the bottleneck of the detection.. Recently, two group-signature-based schemes [16] , [17] have been proposed, ensuring that a verifier vehicle can identify those trustworthy messages from messages sent from neighboring vehicles. A message sent from a neighboring vehicle is said to be trustworthy if the content of the message is identical with at least a certain number of messages sent from other neighboring vehicles. To suppress duplicated messages from the same vehicle, particular group signature schemes are adopted for vehicles to sign on messages so that the anonymity of each vehicle can be achieved. Meanwhile, if a vehicle generates two signatures on the same message, these two signatures can be recognized by the verifier vehicle. One practical issue of these schemes is that they cannot handle similar but different messages. It is often the case that multiple vehicles observing the same driving environment will generate different messages with very similar semantics. In this case, the resolved trustworthy messages might be a minority of all observations which results in a biased or no final decision.. The most relevant work to Footprint is the Sybil attack detection schemes proposed in [18] , [19] . In these schemes, a number of location information reports about a vehicle are required for identification. In [18] , an RSU periodically broadcasts an authorized time stamp to vehicles in its vicinity as the proof of appearance at this location. Vehicles collect these authorized time stamps which can be used for future identity verification. In [19] , trajectories made up of consecutive time stamps and the corresponding public keys of RSUs are used for identification. However, these schemes did not take location privacy into consideration since RSUs use long-term identities to generate signatures. As a result, the location information of a vehicle can be inferred from the RSU signatures it collects. In Footprint, authorized messages issued from RSUs are signer-ambiguous which means the information about the location where the authorized message was issued is concealed, and temporarily linkable which means using a single trajectory for longterm identification of a vehicle is prohibited. Therefore, the privacy of location information and identity of vehicles are preserved in Footprint. In this section, we examine the performance of Footprint in recognizing forged trajectories (issued by malicious vehicles) and actual ones (provided by honest vehicles) through trace-driven simulations. We consider two key metrics:. 1. False positive error: is the proportion of all actual trajectories that are incorrectly identified as forged trajectories. 2. False negative error: is the proportion of all forged trajectories that are falsely identified as actual trajectories. In the following simulations, we use the digital map of Yangpu District in Shanghai where there are 659 intersections and 1,004 road segments. We use the 300-RSU deployment as described in Section 4.2 (see Fig. 1 in Appendix A, available in the online supplemental material). Then, we record the actual trajectories of 521 taxies according to the RSU deployment and the GPS reports of these taxies collected on 1 February 2007. We define an event as a period of 1 hour and truncate each trajectory into 24 subtrajectories. Thus, according to 24 hours in a day, we have 24 sets of subtrajectories. Both honest vehicles and malicious vehicles are randomly chosen from a subtrajectory set of 1 hour. According to our strategies to eliminate Sybil "communities" in Section 4.4.3 and the security analysis in Section 5.1, a malicious vehicle tries to provide as many Sybil trajectories that are longer than the average length of all actual trajectories as possible. If the length of the actual trajectory of the malicious vehicle jT j is less than the average, it provides all Sybil trajectories that are jT j À 1 long. For each subset and each simulation configuration, we run the simulation 20 times and get the average.

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The convergence test of transformation performance of resource cities in China considering undesirable output The main challenge for sustainable development of resource cities is to work out a feasible strategy for transformation processes. This paper introduces a new approach for analysis of transformation performance. Using the environmental production technology and a Malmquist resource performance index (MRPI), we conduct σ , absolute β and conditional β convergence tests for the transformation performance of 21 resource cities in China.The results show that MRPI does not follow the same trend as economic strength of three Chinese regions. In addition, the transformation performance results exhibit a convergence trend for the 21 resource cities.Crown The presence of natural resources leads to the emergence of resource cities. A centralization tendency during industrialization is the primary reason for the development of such cities. Therefore, resource cities can be understood as cities characterized by mineral resource exploitation and processing-oriented manufacturing. There is evidence of a ''curse of natural resources'' whereby regions with great natural resource wealth tend to grow more slowly than resource-poor regions [1] . Since the mid-1980s, resource extraction industries in a large number of resource cities of China have entered mature and declining stages. This leads to the traditional dilemma of ''mine dried up, city bad'' and the serious problem of how to maintain sustainable development. Fortunately, since the 1990s, the Chinese government and social institutions have focused on the development of resource cities.. Some concepts and models concerning transformation in resource cities are relevant here. Sun et al. probed the urban efficiency of 24 typical resource cities in China from 2000 to 2008 using a data envelopment analysis (DEA) model and a Malmquist productivity index [2] . Taking the south Indian city of Bangalore as example, Pani explored three phases of globalization for resource cities, i.e., the colonial phase, the garment phase and the information technology phase [3] . Yu et al. revealed principal factors controlling the degree of sustainable development of mineral resources in mining cities [4] . Halseth discussed three patterns of employment for resource cities and held the opinion that while most households in resource cities engage in educational and skills upgrading, there are potentially serious limitations to the future efficacy of some coping mechanisms [5] . Houghton studied the development of long-distance commuting patterns in Australia, and analyzed the pros and cons of a model and its impact on social and regional development [6] . Keana pointed out a number of conditions for the sustainable development of coal cities from a natural landscape and community evolution perspective [7] . Leadbeater and Goss analyzed the economic development of a single-industry resource region in Elliot Lake in Canada from the perspective of the residents and employment [8] .. DEA is popularly used to study the relative efficiency of homogeneous decision-making units since the method was first proposed in 1978 [9] and many scholars have extended and applied it [10] [11] [12] [13] . In the environmental and energy fields, DEA models are widely applied. Kortelainen analyzed the dynamic environmental performance of EU-20 countries from 1990 to 2003 using a Malmquist index method based on the concept of eco-efficiency [14] . The CO 2 emissions performance of OECD countries has been evaluated at a macro level using different DEA models [15] [16] [17] . Hu and Wang defined the concept of total factor energy efficiency (TFEE) as the ratio of the target energy input to the actual energy input in accordance with variable DEA [18] . Hu and Kao proposed a way of calculating the energy-saving rate on a TFEE basis, and then estimated the energy savings rate of APEC countries from 1991 to 2000 using the method [19] . Mukherjee estimated the energy efficiency of manufacturing industry and the six highest energy-intensive industries in the USA using DEA [20] . Oh and Heshmati constructed a continuous Malmquist-Luenberger productivity index to measure environmentally sensitive productivity considering variable technology and CO 2 emissions [21] .. Undesirable output can be addressed through several different DEA models. Tu calculated environment production efficiency to measure the coordination of environmental and industrial growth according to resource input, industrial production and environmental pollution data for 30 provinces in China [22] . Hu calculated the carbon environment technical efficiency of 30 provinces in China using a sustainability model [23] . Zhou et al. measured the carbon emissions efficiency of 18 countries whose carbon emissions are the highest in the world using a Malmquist productivity index approach based on DEA, and then analyzed the factors influencing this efficiency [24] . Wang et al. set up a Malmquist index using a DEA model containing desirable outputs to explore dynamic changes in carbon emission performance in China [25] . Hamid added environmental factors to a production effectiveness function to construct a dynamic model to analyze long-term economic growth for optimal policy design [26] . Ramanathan analyzed the energy and carbon emission efficiency of 17 countries in North Africa using a DEA method [27] . Lu et al. researched sustainable economic development in China under the constraints of energy and environmental security, adopting energy and carbon emission as inputs [28] . Du and Zou estimated carbon emission efficiency in various regions in China from 1995 to 2009, and analyzed regional differences and influencing factors [29] . Chen constructed an input-output database for 38 industries and estimated changes in industrial total factor productivity in China by means of a trans-log production function and green production accounting [30] . In these studies on environmental production efficiency, two approaches are used to deal with undesirable output: one is to set pollutants as undesirable output [22] [23] [24] [25] and the other is to set pollutants as inputs [26] [27] [28] [29] [30] . Although it is simple to take bad outputs as inputs in a DEA model, this does not reflect the true production process. It is more reasonable to treat bad outputs as undesirable outputs through environmental production technology (EPT), which is just the approach used in this study.. The convergence approach has been used to solve practical problems in many social fields. Baumol established a convergence model based on classical economic theory to examine regional economic convergence [31] . Assuming that per capita output has random and linear deterministic trends, Bernard and Durlarf gave a clear definition of convergence and common trends [32, 33] . Barro and Islam tested the absolute and conditional convergence of regional economic development gaps [34, 35] . Pan and Liu studied the innovation efficiency of industrial enterprises among various regions in China using β convergence [36] . Shi and Li tested the convergence of economic growth in eastern, central and western China using cointegration test methods [37] . Liu and Li investigated the total factor productivity of nationwide industrial enterprises using σ and β convergence [38] .. Research on the transformation of resource cities has rarely focused on quantitative analyses and rational evaluation of the transformation process. Moreover, there is no consensus among previous studies on whether differences of the transformation of resource cities will persist. A noteworthy point is that resource cities tend to have more seriously polluted environment. In order to achieve the goals of a harmonious society, regional economic development and the stability of mining and society, resource cities should give priority to the environmental protection during their transformation. In the present study we used DEA and introduced EPT to build an evaluation system for the transformation performance of resource cities. In an empirical analysis of 21 resources cities, we explore differences in their transformation performance by classifying them into three distinct areas, and then apply convergence theory to examine the convergence of this performance. Transformation of resource cities has become an important regional strategic issue for sustainable growth, social stability, and harmonious development. Environmental protection during the transformation of resource cities cannot be ignored. We introduced EPT to set up a Malmquist performance index for the transformation of resource cities under environmental regulation. We performed σ , absolute β and conditional β convergence tests. The MRPI results reveal great differences in transformation performance in different regions and the MRPI ranking among the three regions is not consistent with the ranking of their economic strength. Furthermore, test results reveal that the σ convergence trend is not obvious, the β convergence trend is significant, and the β conditional convergence is remarkable when some control variables are added.. The overall conclusion is that the transformation performance for 21 resource cities shows a convergence trend. In other words, difference in transformation performance among these cities is not apparent.. How to achieve economic growth during transformation remains a major issue for resource cities. Development and implementation of transformation strategies should be made after proper evaluation of the conditions for each city. Since resource cities are more environmentally polluted, environmental protection issues should be strongly highlighted during their transformation. However, there is little quantitative research in this field, especially with regard to environmental issues. This paper extends the Malmquist productivity index to a transformation performance index for resource cities using EPT. Using MRPI results for 21 resource cities, we carried out convergence tests. The transformation performance of resource cities can be accurately evaluated using this approach and their individual strengths and weaknesses can be clearly identified from convergence tests. The results provide a theoretical basis for the development of more targeted transition policies that consider environmental protection for resource cities.

O B I O B I O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O B I O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I


Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval Abstract-Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance.Index Terms-Classifier committee learning, content-based image retrieval, relevance feedback, asymmetric bagging, random subspace, support vector machines. With the explosive growth in image records and the rapid increase of computer power, retrieving images from a largescale image database becomes one of the most active research fields. Content-based image retrieval (CBIR) is a technique to retrieve images semantically relevant to the user's query from an image database. Relevance feedback (RF) is a way of bridging this gap and scaling the performance in CBIR systems. Support vector machines (SVM) based relevance feedback is widely employed. However, when the number of positive feedback samples is small, the performance of SVM RF becomes poor because of the following facts: 1) the SVM classifier is unstable for a small-sized training set, 2) the optimal hyperplane of SVM may be biased, and 3) SVM always encounters an overfitting problem. In this paper, we have designed a new asymmetric bagging and random subspace mechanism (with three algorithms) to address the three key problems. Extensive experiments on a Corel Photo database with 17,800 images have shown that the new mechanism can improve the performance of relevance feedback significantly.. In our proposed system, it is found that the kernel parameters affect the performance of relevance feedback algorithms. Unfortunately, how to select the kernel parameters is a problematic issue. Recently, a tuning method has been provided in our system for the user to select the parameters of SVM. In the future, the tuning method will be tested and generalized to select the parameters of kernelbased algorithms. For relevance feedback in CBIR, the size of the training set is small, so the leave-one-out method to tune the parameters will be a good choice. Xuelong Li is a lecturer at the University of London. His current research interests include visual surveillance, biometrics, data mining, multimedia information retrieval, cognitive computing, image processing, pattern recognition, and industrial applications. He has published more than 40 papers in the journals (TCSVT, TIP, TMM, TPAMI, etc.) and conferences (CVPR, ICASSP, ICDM, etc.). He was on the PCs of more than 30 conferences (BMVC, ECIR, ICDM, ICME, ICMLC, ICPR, PAKDD, PCM, SMC, WI, etc.). He has cochaired the fifth UKCI. He is an AE of the IJIG (World Scientific) and Neurocomputing (Elsevier). He is also on the EB of IJITDM (World Scientific). He is a GE for an IJCM (Taylor & Francis) special issue. He is a reviewer for around 60 journals and conferences, including nine IEEE transactions. He is a member of the IEEE.. Xindong Wu received the PhD degree in artificial intelligence from the University of Edinburgh, Britain. He is a professor and the chair of the Department of Computer Science at the University of Vermont. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM, and WWW, as well as 12 books and conference proceedings. Dr. Wu is the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering, the founder and current steering committee chair of the IEEE International Conference on Data Mining (ICDM), an honorary Editor-in-Chief of Knowledge and Information Systems (Springer), and a series editor of the Springer book series on advanced information and knowledge processing (AI&KP). He is the 2004 ACM SIGKDD Service Award winner. He is a senior member of the IEEE and the IEEE Computer Society.. . For more information on this or any other computing topic, please visit our Digital Library at www.computer.org/publications/dlib. Our scheme in Section 4 is different from boosting in general and AdaBoost in particular. In Tieu and Viola's AdaBoost image retrieval [26] , a set of weak two-class classifiers were incorporated by some weights and, thus, a strong classifier is built as a weighted sum of the weak classifiers. Our method differs from both Tieu and Viola's AdaBoost image retrieval in [26] and Guo's AdaBoost [6] , for the following reasons:. . Tieu and Viola employed more than 45,000 highly selective features. These features were demonstrated to be sparse with high kurtosis and were argued to be expressive for high-level semantic concepts [26] , [33] . However, our scheme is designed to work upon the traditional low-level visual features (as introduced in Section 4). The constrained similarity measure (CSM) [6] claimed that the SVM outperforms AdaBoost when the traditional visual features are selected as the image description method. . In [26] , AdaBoost was used for feature selection based on the training set (the selected feedback samples). In our work, the new scheme randomly selects features and training samples. . Boosting aims to minimize the training error by assigning a weight to each of a series of weak classifiers and then combining them for a vote. Conventional boosting schemes, such as AdaBoost, cannot boost the performance of strong classifiers (e.g., SVM) since the zero-training-error in strong classifiers is generally set as the criterion for stopping the boosting training. The zero-training-error is easy to get because the dimensionality of the low-level features is much larger than the number of the feedback samples in relevance feedback. Consequently, boosting methods degenerate to a single strong classifier. On the contrary, for our presented scheme, from the mathematical description of the working principle, we can see that the scheme can combine a series of diverse SVM classifiers and yield better performances. . Both SVM and Adaboost use the margin maximization criterion (MMC). However, SVM and AdaBoost use L 2 norm and L 1 norm for weight vectors, respectively. Furthermore, SVM and AdaBoost use L 2 norm and L inf norm for instance vectors, respectively. There are also significant differences between boosting and the bagging approach we have used. First, boosting trains weak classifiers based on a resampling using the training error information, so boosting can only improve the performance of basic classifiers, which cannot correctly classify some of the training samples. In relevance feedback, the samples' size is smaller than the number of the samples' feature dimensions, so boosting cannot work well in our context when the selected basic classifiers are SVMs. Second, for relevance feedback in retrieval, the feedback samples are limited, especially the positive feedback samples, so the weights for boosting-leant basic/weak classifiers are only optimal for the training set but not the testing set. Unlike the boosting, bagging does not generate any weights for its weak classifiers. Therefore, bagging does not meet the overfitting issues in boosting.. Furthermore, we do not use traditional supervised feature selection methods because they generally face an overfitting problem as well. This overfitting problem is due to the limited feedback samples especially the positive samples in the relevance feedback procedure. Unlike traditional supervised feature selection methods, the random subspace method does not select features to minimize the training error or maximize the margin between the positive and negative feedback samples, but utilizes the diverse characteristics of different features. The mathematical explanation of the working principle of the framework in Section 3.2 can well explain this claim.. For unsupervised feature selection, it is always based on some selection criterion. When the criterion is good enough to capture the user's preferences, it might work more effectively than ours. However, because the size of a real-world image database can be very large, to find such a criterion seems to be impossible. In addition, different users can have different viewpoints on the same images. Finally, unsupervised feature selection is time consuming and, thus, it does not fit with online learning. Consequently, using the same unsupervised feature selection criterion for all users is not a wise option.

B I O B I O O O O B I O O O O O O O O B I I O O O O O O B I I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O B I O O O O O O O O O B I O B I O O O O O O O O O O O O O B I O O O O B I I B I B I B I B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A fast method for calculating reliable event supports in tree reconciliations via Pareto optimality Background: Given a gene and a species tree, reconciliation methods attempt to retrieve the macro-evolutionary events that best explain the discrepancies between the two tree topologies. The DTL parsimonious approach searches for a most parsimonious reconciliation between a gene tree and a (dated) species tree, considering four possible macro-evolutionary events (speciation, duplication, transfer, and loss) with specific costs. Unfortunately, many events are erroneously predicted due to errors in the input trees, inappropriate input cost values or because of the existence of several equally parsimonious scenarios. It is thus crucial to provide a measure of the reliability for predicted events. It has been recently proposed that the reliability of an event can be estimated via its frequency in the set of most parsimonious reconciliations obtained using a variety of reasonable input cost vectors. To compute such a support, a straightforward but time-consuming approach is to generate the costs slightly departing from the original ones, independently compute the set of all most parsimonious reconciliations for each vector, and combine these sets a posteriori. Another proposed approach uses Pareto-optimality to partition cost values into regions which induce reconciliations with the same number of DTL events. The support of an event is then defined as its frequency in the set of regions. However, often, the number of regions is not large enough to provide reliable supports. Results: We present here a method to compute efficiently event supports via a polynomial-sized graph, which can represent all reconciliations for several different costs. Moreover, two methods are proposed to take into account alternative input costs: either explicitly providing an input cost range or allowing a tolerance for the over cost of a reconciliation. Our methods are faster than the region based method, substantially faster than the sampling-costs approach, and have a higher event-prediction accuracy on simulated data. Conclusions: We propose a new approach to improve the accuracy of event supports for parsimonious reconciliation methods to account for uncertainty in the input costs. Furthermore, because of their speed, our methods can be used on large gene families. Our algorithms are implemented in the ecceTERA program, freely available from http://mbb.univ-montp2.fr/MBB/. In a parsimony framework, the choice of the costs for basic events may have a strong impact on the set of predicted events. In this paper, we provide a new tool dealing with this problem, which both improves the accuracy of D, T, L events predicted by parsimonious reconciliation methods and scales up to handle the larger set of gene trees used in phylogenomic studies nowadays. This work combines the complementary ideas of the methods presented in [8] and [10] into new strategies that combine their strengths: speed and reliability. To deal with the inherent uncertainty of the input cost vectors, two approaches have been proposed and tested: explicitly providing an input cost range (strategy s3), or considering non-optimal reconciliations up to a fixed over-cost (strategies s4 and s5). Our approaches and models are flexible so that one can choose the strategy that fits the data better: considering either only parsimonious reconciliations or nearly optimal ones, choosing acceptable cost ranges, the over-cost, and even providing some-user defined additional constraints to filter the event count vectors list (see strategy s5). Our tests on simulated data demonstrate that models using dated (or partially dated) species trees seem to provide more accurate event predictions than those using undated species trees. Moreover, in our experiments, using median reconciliations is more effective than picking a random one, and better than taking the list of all events, confirming the findings of [8] . Furthermore, our approaches are faster and more accurate than both methods in [8] and [10] . Finally, this work emphasizes the benefits of using the reconciliation graph to manage the space of reconciliations efficiently. Many other tools could be developed by using this graph, for example calculating the reconciliation whose average of the supports is maximized.. This work confirms that erroneously inferred events can be, at least in part, filtered out. This filtering could probably be further improved by also taking into account reconciliations of close alternative gene tree/species tree topologies. Yet, taking such alternative reconciliations into account is challenging, as the reconciliation graph solution [7] , proposed to efficiently handle alternative reconciliations, was not designed to handle reconciliations between different gene/species trees. The evolutionary history of a gene family often differs from the history of the species containing those genes due to macro-evolutionary events other than speciation. Reconciliation methods compare gene trees with a species tree in order to recover these events. The DTL reconciliation model [1] [2] [3] [4] accounts for three types of events: gene duplications (D), losses (L), and transfers (T). This model is typically used in a parsimony framework, which searches for the reconciliations that minimize the overall cost, given a cost vector specifying the costs for D, T and L events. Unfortunately, ensuring the time-consistency of gene transfers, i.e., satisfying the chronological constraints among nodes of the species tree that are involved in transfer events, is NP-hard [4, 5] . However, if the internal nodes of the species tree are ordered, i.e. using a dated species tree, the problem can be efficiently solved [1, 6] . Although there can be an exponential number of optimal reconciliations for a given cost vector, the DTL-graph can be used to represent them compactly [7] .. Some of the events predicted by reconciliations methods may not be reliable due to potential inaccuracies in the input trees and the inherent imprecision of the input costs. Thus, it is necessary to estimate confidence values, or supports, for each predicted event. Supports can be defined, for example, as the frequency of an event over a space of alternative solutions.. In [7] [8] [9] , the support of an event is defined as the frequency of the reconciliations containing it over all reconciliations in the solution space. In [7, 8] , the DTL-graph was used to compute event supports in this sense over the set of all parsimonious solutions (for one cost vector in [7] and for several ones in [8] ). Moreover, in [8] , the authors used these event supports to identify the median reconciliation, that is the reconciliation that minimizes the sum of event-based distances between itself and all other parsimonious alternatives (here, the event-based distance between two reconciliations is defined as the number of events contained in one but not in the other). Bansal et al. [9] presented another way to address the problem, suitable for non-dated species trees as well as dated ones: they designed a method which samples the space of parsimonious reconciliations uniformly at random and then counts the frequency of each event within all sampled solutions.. However, the approaches presented in [7, 9] only focus on one cost vector; though, it is often difficult to know the appropriate cost vector, and the solutions can be very sensitive to the input costs. In [8] , the authors used a time-consuming approach that samples the space of cost vectors around the input one and then apply the algorithm presented in [7] to compute a DTL-graph for each sample. Libeskind-Hadas et al. [10] presented a method to estimate the sensibility of a parsimonious reconciliation with respect to the input costs. They use Pareto-optimal event count vectors to partition the cost space into regions such that the costs in the same region induce reconciliations with the same number of DTL events. These regions are then used to compute events supports, defined as the fraction of regions having an event in all (or one of, depending on the option used) their reconciliations. However, for the support measure to be reliable, the events should be recovered using biologically realistic costs, which can vary with respect to the phylum we are interested in (e.g., in Mammalian, duplications are much more frequent than transfers, whilst transfers are predominant in the evolution of Bacteria).. But, when analyzing a realistic cost space with the method in [10] , we observe that, often, a small number of regions, e.g. in many cases only one, is returned; thus, the corresponding supports can only be either 0 or 1 and are almost useless to filter out events. On the other hand, a larger cost space can account for more regions, but the event supports may be unreliable since they consider events retrieved using unrealistic costs (we will see in the Results section that this is indeed the case). Moreover, the method in [10] does not generate median reconciliations. It was shown in [8] that the median reconciliation has better event predictions than random ones.. Here, we use the definition of an event support given in [7, 8] and extend the work in [7] by taking into account several cost vectors. Rather than using the timeconsuming sampling method in [8] , we construct the space of all solutions corresponding to the input cost range and then compute the event supports over this space. As we shall see, our algorithm also makes use of Pareto-optimal reconciliations, which are efficiently computed via an extended version of the DTL-graph. This has the advantage of efficiently taking into account all relevant parsimonious reconciliations -even those that are parsimonious for very few event cost combinations and that are thus unlikely to be discovered by an ad hoc choice or a random sampling of event costs [10] .. The approaches presented in [7] [8] [9] only consider optimal parsimonious reconciliations, even though the real gene evolution might not be optimal. In this paper, we also present a method for computing near optimal reconciliations that allows a tolerance for the overall reconciliation cost. Our methods are faster than the one presented in [10] , substantially faster than the sampling-costs approach presented in [8] , and have a higher event prediction accuracy on simulated data than both methods. In this section, we will show how to compute all event count vectors of Pareto-optimal reconciliations (see Algorithm 1). This list of vectors is cleaned to keep either reconciliations that are parsimonious with respect to the given cost range (for solving Problem 1), or those having cost at most cost m (G, S , c) + (for solving Problem 2). Each of the returned lists is then used to construct the corresponding graph (Algorithm S1 in the Additional file 1) that represents all the solutions for the associated problem. Using this graph, we can compute supports for events, as well as median reconciliations.

O O O O O O O B O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O


Proteomic Approaches within the NCI Early Detection Research Network for the Discovery and Identification of Cancer Biomarkers ABSTRACT: In the postgenome era, proteomics provides a powerful approach for the analysis of normal and transformed cell functions, for the identification of disease-specific targets, and for uncovering novel endpoints for the evaluation of chemoprevention agents and drug toxicity. Unfortunately, the genomic information that has greatly expounded the genetic basis of cancer does not allow an accurate prediction of what is actually occurring at the protein level within a given cell type at any given time. The gene expression program of a given cell is affected by numerous factors in the in vivo environment resulting from tissue complexity and organ system orchestration, with cells acting in concert with each other and responding to changes in their microenvironment. Repositories of genomic information can be considered master "inventory lists" of genes and their maps, which need to be supplemented with proteinderived information. The National Cancer Institute's Early Detection Research Network is employing proteomics, or "protein walking", in the discovery and evaluation of biomarkers for cancer detection and for the identification of high-risk subjects. Armed with microdissection techniques, including the use of Laser Capture Microdissection (LCM) to procure pure populations of cells directly from human tissue, the Network is facilitating the development of technologies that can overcome the problem of tissue heterogeneity and address the need to identify markers in easily accessible biological fluids. Proteomic approaches complement plasma-based assays of circulating DNA for cancer detection and risk assessment. LCM, coupled with downstream proteomics applications, such as two-dimensional polyacrylamide gel electrophoresis and SELDI (surface enhanced laser desorption ionization) separation followed by mass spectrometry (MS) analysis, may greatly facilitate the characterization and identification of protein expression changes that track normal and disease phenotypes. We highlight recent work from Network investigators to demon- Genetic aberrations during carcinogenesis disturb the normal function of a cell, primarily through their effect upon the proteins encoded in the altered/mutated genome. Thus, while increasingly sophisticated techniques have rapidly expanded our knowledge of events at the nucleic acid level, protein-based analyses and assays remain desirable as they provide information that is more easily linked to function than nucleic acid-based assays. From a practical point of view, assays of protein tumor markers, due to their ease and robustness, lend themselves to routine clinical practice. Screening, even for high-risk groups, could only be successful when the procedures undertaken are simple, inexpensive, and minimally invasive. A most effective approach is to develop protein-based tests for easily accessible biological fluids such as urine, blood, feces, sputum, bladder, and bronchioalveolar lavage. The development, in particular, of tests to evaluate secretory proteins has proven value in the diagnosis of cancer and in the assessment of precancerous lesions. Proteomics provides the means to achieve the goal of early cancer detection.. An important step in the new postgenome era is to decode the functions of the huge numbers of new open reading frames (ORFs). Various avenues to elucidate what unknown genes do and how they interact within an organism are being utilized, including (1) the simultaneous assessment at the RNA level of all genes that are expressed in a cell population or tissue and (2) the mapping and quantitation of all the proteins expressed in a cell population or tissue. 1, 2 Valuable information that cannot be accurately predicted from nucleic acid data, such as protein location and posttranslational modification, may be obtained directly at the proteomic level. The ensemble of proteomic information constitutes the body of knowledge of the "proteome", the total protein output encoded by a genome. Proteome research, or proteomics, is an extension of protein (polypeptide) and gene-product mapping (linking proteins to their encoding genes). 3 The goal of proteomics is a comprehensive, quantitative description of protein expression and of the changes associated with biological perturbations such as disease (cancer) or drug treatment. Proteome analysis can be undertaken independently of genomic or RNA analysis. Its ability to identify and quantify proteins and confirm their expression and structure as presumed from nucleic acid data is highly complementary to genomic science.. The human genome encodes for some 30,000 genes that are scattered among 3.2 billion nucleotides. Added complexity stems from the findings of alternative splicing and due to the occurrence of posttranslational modifications that give rise to several protein products from a given gene. 2 The profiles of expressed proteins in normal and cancer states are different as some genes are up-or downregulated, whereas others are expressed constitutively. 4 To generate these profiles, a variety of technologies are employed for sample collection (cells, tissue, or fluid), processing and analysis of samples, and storage of data. Cytosolic proteins are most readily amenable to proteomic analysis. Whereas secreted proteins are easy to solubilize, their low abundance is problematic. On the other hand, membrane-bound proteins both occur in low abundance and require additional steps for their solubilization prior to analysis. 5 The inside of a cell is a crowded and dynamic compartment, where proteins are perpetually being produced and degraded. Understanding the structures, interactions, and functions of all of a cell's proteins is one of the grand goals of the postgenome era. Prospects for introducing proteomics technologies into the clinical laboratory will depend on their robustness, their user-friendliness, and the clinical relevance of the added information they provide that cannot be captured through other technologies in use in the clinical laboratory. The primary focus of proteome analysis is the rapid characterization of gene products as either "previously studied" or "novel". The overall goal is to apply this information for cancer screening and risk assessment. Efforts made in the EDRN are directed towards identification and characterization of biomarkers that can be used for early cancer detection and risk assessment.

O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O


A second-order cut-cell method for the numerical simulation of 2D flows past obstacles François Bouchon, Thierry Dubois, Nicolas James. A second-order cut-cell method for the numerical simulation of 2D flows past obstacles.. 35 pages. 2011. We present a new second-order method, based on the MAC scheme on cartesian grids, for the numerical simulation of two-dimensional incompressible flows past obstacles. In this approach, the solid boundary is embedded in the cartesian computational mesh. Discretizations of the viscous and convective terms are formulated in the context of finite volume methods ensuring local conservation properties of the scheme. Classical second-order centered schemes are applied in mesh cells which are sufficiently far from the obstacle. In the mesh cells cut by the obstacle, fisrt-order approximations are proposed. The resulting linear system is nonsymetric but the stencil remains local as in the classical MAC scheme on cartesian grids. The linear systems are solved by a direct method based on the capacitance matrix method. The time integration is achieved with a second-order projection scheme. While in cut-cells the scheme is locally first-order, a global second-order accuracy is recovered. This property is assessed by computing analytical solutions for a Taylor-Couette problem. The efficiency and robustness of the method is supported by numerical simulations of 2D flows past a circular cylinder at Reynolds number up to 9 500. Good agreement with experimental and published numerical results are obtained. Numerical simulation of fluid mechanics problems is one of the most challenging scientific computing's field of research of the last decades. With the increasing power of computers in terms of memory available, speed and number of processors, more and more complex problems arising in industrial applications become accessible to numerical simulations. In complex geometries, the discretization of the Navier Stokes Equation (NSE) by finite element or finite volume methods on body-fitted grids allows to simulate flows at low to moderate Reynolds numbers. However, generating an efficient conformal mesh is a challenging problem when the geometry gets complex and this pre-processing step is very CPU-time consuming. Indeed, generating a body-fitted grid can be even more expansive than computing the solution itself. Eventhough matrices issued from finite element or finite volume discretizations are dense and sparse, the number of connected nodes is much larger than in finite difference methods on cartesian grids. Specific and more complex solvers have to be used resulting in a significant overhead in the computational effort required to obtain discrete solutions. An alternative to the body-fitted methods is proposed with the Immersed Boundary (IB) methods. The aim of IB methods is to handle complex geometric configurations without the use of body-fitted meshes. Simulations are performed on cartesian grids so that the efficiency and robustness of cartesian grid solvers are achieved. Therefore, significant advances in the application of numerical simulations to more and more complex industrial problems could be expected.. Since the late 90s, IB methods have been investigated in many published works and among the most investigated problems is the numerical simulations of two or three-dimensional flows past obstacles. Several technics have been developed to take into account the presence of fixed or mobile obstacles in the flow. IB methods can be classified in three categories : momentum forcing methods, Cartesian grid methods and penalty methods. IB methods have been first developed by Peskin (see [1] , and [2] for a review) for fluid structure interaction problems with applications in Biology (blood flow). These methods use both Eulerian and Lagrangian variables, which are related via interaction equations. Discrete versions of dirac functions are used to describe the fluid structure interaction forcing, and to link the Eulerian and Lagrangian variables. In a similar approach, momentum forcing method, introduced by Mohd-Yusof [3] , have been developped in the context of B-Splines or finite difference methods and applied to the numerical simulations of flows in complex geometries. Here, the challenge is to be able to compute the force applied by the obstacle on the fluid which would be such that the velocity field satisfies the boundary conditions on the immersed boundary (see [4] , [5] and [6] for a review). Momentum forcing methods are 2 easy to implement and efficient: spectacular numerical simulations of flows in complex geometries have been presented in [7] (flows in an full engine) and in [8] (heart valve dynamics). The coupling of momentum forcing methods with projection schemes introduces difficulties in imposing at the same time level the contuinity equation and the boundary conditions on the immersed interface (see [9] for example). The incompressibility of the flow may even be violated in the vicinity of the immersed boundary [10] . This may result in serious diffulties when computing boundary layers at high Reynolds number.. The penalty methods [11] have a mathematical background. The presence of a solid obstacle in the computational domain is modeled by adding a penalty term, depending on a small parameter, in the incompressible NavierStokes equations. The difference between the penalty solution and the solution of the Navier-Stokes equations can be bounded in Sobolev norms (energy norm for instance) in terms of this parameter. The penalty method does not depend on the choice of the discretization schemes used to approximate the equations. However, in this approach, the immersed interface is not directly and accurately represented in the flow simulation. Depending on the value of the parameter, a diffusion of the immersed boundary in the computational cells may prevent to accurately predict turbulent flows induced by very thin boundary layers.. The third class of IB methods, called Cartesian grid methods or cut-cell methods, focus on the discretization of the equations in the mesh cells cut by the immersed boundary (see [12, 13, 14, 15] for instance). Techniques relying on high-order interpolations have been developed in order to impose directly the boundary conditions to the velocity field on the immersed boundary. In a different approach, the method of merging cells (see [12] , [14] ) consists in gathering cells in the vicinity of the obstacle: a cut-cell is merged with one of their neighbours to form a new polygonal cell. The new merged cell has then more than four neighbours (in dimension 2). Thus, this method breaks the five-point structure of the underlying matrices. In a recent paper, Cheny and Botella (see [16] ) proposed a new cut-cell method, called the LS-STAG method, based on the well-known second-order projection MAC scheme (see [17] ). They aim to keep the five-point structure of the classical MAC scheme on cartesian grids and to preserve the global conservation properties of the continuous equations at the discrete level. In order to accurately represent the immersed boundary on the computational cartesian grid, they use the signed algebraic distance to the obstacle boundary, as in level-set methods [18] . The staggered arrangement of the unknowns for both the velocity field and the pressure is adapted to the geometry of the cut-cells. The LS-STAG method is highly efficient from a computational point of view as it is based on the MAC solver on cartesian grids which has been extensively and successfully used in numerical simulations of turbulent flows, both in the context of direct and Large-Eddy simulations. However, due to a low order approximation of the viscous and convective fluxes in cut-cells, the LS-STAG method is globally first-order accurate.. The numerical scheme proposed in this paper is a new cut-cell method. As in [16] , the immersed boundary is geometrically represented by using the signed algebraic distance to the obstacle boundary. In fluid-cells, that is mesh cells which are far enough from the immersed boundary, classical centered, second-order finite volume schemes are used. In our approach, the location of the velocity component is, as in [16] , adapted to the geometry of cut-cells. However, the discrete pressure is placed at the center of the cartesian cells for both fluid-cells and cut-cells. In the vicinity of the obstacle, second-order interpolations using boundary conditions on the solid boundaries are introduced to evaluate the convective fluxes. This results in a local first-order approximation of the nonlinear terms in cut-cells. A pointwise approximation of the viscous terms is used in cut-cells. When boundary conditions on the immersed boundary can be used, a five-point stencil scheme for the viscous term is employed. Otherwise, a six-point first-order approximation is introduced. The resulting linear system is close to the five-point structure symmetric system obtained on cartesian mesh with the MAC scheme. A direct solver, based on a capacitance matrix method, is proposed. The efficiency of the solver is similar to the cartesian grid solver obtained with the MAC scheme. The incompressibility of the discrete velocity field is enforced up to the computer accuracy. While first-order truncation errors are locally introduced in the scheme in the cut-cells, a second-order global accuracy is recovered. Note that a similar superconvergence result has been proved by Yamamoto in [19] in the context of elliptic equations.. The paper is organized as follows: in section 2, we present the problem together with the notation, and we describe the mesh used for the space discretization. In section 3, we give details for the space-discretization in the mesh cells cut by the obstacle and propose a fast solver adapted from the capacitance matrix method (see [20] , [21] ) to solve the linear systems. Section 4 is then devoted to numerical results for different configurations: one of them (the Taylor-Couette flow) allows to check the second-order accuracy of the method, some more challenging tests at high Reynolds numbers show the robustness of the scheme and comparisons with both experimental and numerical results found in literature are given.. 2. The setting of the problem

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Slow oscillations in neural networks with facilitating synapses Abstract The synchronous oscillatory activity characterizing many neurons in a network is often considered to be a mechanism for representing, binding, conveying, and organizing information. A number of models have been proposed to explain high-frequency oscillations, but the mechanisms that underlie slow oscillations are still unclear. Here, we show by means of analytical solutions and simulations that facilitating excitatory (E f ) synapses onto interneurons in a neural network play a fundamental role, not only in shaping the frequency of slow oscillations, but also in determining the form of the up and down states observed in electrophysiological measurements. Short time constants and strong E f synapse-connectivity were found to induce rapid alternations between up and down states, whereas long time constants and weak E f synapse connectivity prolonged the time between up states and increased the up state duration. These results suggest a novel role for facilitating excitatory synapses onto interneurons in controlling the form and frequency of slow oscillations in neuronal circuits. The brain can display multiple forms and frequencies of oscillatory activity. Slow wave (0.1-1 Hz) oscillations recorded by an electroencephalogram (EEG) are manifested by reduced alertness, such as during sleep or anesthesia (Steriade et al. 1993a; Moruzzi and Magoun 1995) . However, the manner by which this type of activity arises remains unclear. A series of combined EEG and intracellular in vivo measurements have provided empirical evidence that the phases of the oscillations recorded with an EEG are correlated with repeated bi-stable states, called up and down states, of the cortical intracellular electrical activity (Contreras and Steriade 1995) . The up state is related to periods of relative depolarization and firing of cortical neurons. In contrast, the down state is associated with hyperpolarization or a silent phase (Steriade et al. 1993b Stern et al. 1997; Lampl et al. 1999 ).. An open question is whether slow oscillations are driven by recurrent loops with distal brain areas, inter-columnar interactions, or the intrinsic properties of the local recurrent microcircuit. Slow oscillations have been observed in neocortical slices, suggesting that they could be induced by the cellular and synaptic properties of the local network . Such local networks contain both excitatory and inhibitory cells, which can form a negative feedback loop and thus constitute a candidate mechanism for the generation of oscillations.. Since both the membrane and the synaptic time constants are usually on the order of several milliseconds, these oscillations are expected to be fast (above 40 Hz). Slow mechanisms, such as intrinsic after-hyperpolarization currents (Timofeev et al. 2000; Compte et al. 2003 ), short-term synaptic depression (Timofeev et al. 2000) or slow synaptic currents (NMDA and GABA B ) (Parga and Abbott 2007) have been proposed to generate slow oscillations in a local circuit. It is known, however, that synapses from excitatory to inhibitory neurons can exhibit short term plasticity in the form of facilitation (Thomson et al. 1993; Markram et al. 1998) . Such connections have a relatively long time constant (hundreds of milliseconds), and can therefore enable slow oscillations (below 5 Hz) in the network. We examined networks of excitatory and inhibitory cells, where the connection from the excitatory to the inhibitory neurons is facilitating and found that slow oscillations indeed arise in such networks. We show the conditions and properties of such oscillations, and discuss their significance. We showed that up and down states can emerge from the interplay between the excitatory and inhibitory populations with excitatory facilitating connection onto inhibitory population. The excitatory neurons elevate the firing rate of the network, which is then suppressed by increasing inhibition recruited by facilitating excitatory connections. Our study predicts that alterations of the facilitating synapses between pyramidal cells and interneurons will affect the oscillations observed in the neocortical microcircuit, as well as the transitions between up and down states. We also point at a fundamental difference between facilitation and depression based mechanisms of oscillation. These predictions can be tested experimentally. The left border of the oscillation regime (J L in Fig. 2(a) ) is defined by a divergence of the oscillations, and was thus searched for by simulating the network until the border between a stable limit cycle and divergent behavior was found. Slow oscillations have been revealed in EEG measurements for reduced alertness brain states (Moruzzi and Magoun 1995; Abbott and Blum 1996) . They were also observed experimentally in acute cortical slices . We propose a novel mechanism underlying the generation of slow oscillations in the neocortex. A network of excitatory and inhibitory cells with recurrent excitation and facilitating excitatory synapses to the inhibitory population can exhibit slow oscillations. The recurrent excitation initiates the active state by positive feedback. The active state is terminated by inhibition, which is elevated by facilitating excitatory synapses. Simulations and analytical analysis reveal that the properties of these facilitating synapses onto interneurons determine the frequency, duration, and shape of oscillations. Network oscillations depend on the dynamics of facilitating synapses as well as on their absolute strength. Shorter facilitation time constants and a strong connections cause high firing rates and longer periods of oscillation cycles.

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