Unconstrained endpoint profiling (googling the internet) Understanding Internet access trends at a global scale, i.e., what do people do on the Internet, is a challenging problem that is typically addressed by analyzing network traces. However, obtaining such traces presents its own set of challenges owing to either privacy concerns or to other operational difficulties. The key hypothesis of our work here is that most of the information needed to profile the Internet endpoints is already available around us -on the web.In this paper, we introduce a novel approach for profiling and classifying endpoints. We implement and deploy a Google-based profiling tool, which accurately characterizes endpoint behavior by collecting and strategically combining information freely available on the web. Our 'unconstrained endpoint profiling' approach shows remarkable advances in the following scenarios: (i) Even when no packet traces are available, it can accurately predict application and protocol usage trends at arbitrary networks; (ii) When network traces are available, it dramatically outperforms state-ofthe-art classification tools; (iii) When sampled flow-level traces are available, it retains high classification capabilities when other schemes literally fall apart. Using this approach, we perform unconstrained endpoint profiling at a global scale: for clients in four different world regions (Asia, South and North America and Europe). We provide the first-of-its-kind endpoint analysis which reveals fascinating similarities and differences among these regions. Understanding what people are doing on the Internet at a global scale, e.g., which applications and protocols they use, which sites they access, and who they try to talk to, is an intriguing and important question for a number of reasons. Answering this question can help reveal fascinating cultural differences among nations and world regions. It can shed more light on important social tendencies (e.g., [36] ) and help address imminent security vulnerabilities (e.g., [34, 44] ). Moreover, understanding shifts in clients' interests, e.g., detecting when a new application or service becomes popular, can dramatically impact traffic engineering requirements as well as marketing and IT-business arenas. YouTube [19] is probably the best example: it came 'out of nowhere,' and it currently accounts for more than 10% of the total Internet traffic [24] .. The most common way to answer the above questions is to analyze operational network traces. Unfortunately, such an approach faces a number of challenges. First, obtaining 'raw' packet traces from operational networks can be very hard, primarily due to privacy concerns. As a result, researchers are typically limited to traces collected at their own institutions' access networks (e.g., [29, 30] ). While certainly useful, such traces can have a strong 'locality' bias and thus cannot be used to accurately reveal the diversity of applications and behaviors at a global Internet scale. Moreover, sharing such traces among different institutions is again infeasible due to privacy concerns.. Even when there are no obstacles in obtaining non-access, i.e., core-level traces, problems still remain. In particular, accurately classifying traffic in an online fashion at high speeds is an inherently hard problem. Likewise, gathering large amounts of data for off-line post-processing is an additional challenge. Typically, it is feasible to collect only flow-level, or sampled flow-level information. Unfortunately, the state-of-the-art packet-level traffic classification tools (e.g., [29] ) are simply inapplicable in such scenarios, as we demonstrate below.. In this paper, we propose a fundamental change in approaching the 'endpoint profiling problem': depart from strictly relying on (and extracting information from) network traces, and look for answers elsewhere. Indeed, our key hypothesis is that the large and representative amount of information about endpoint behavior is available in different forms all around us.. For communication to progress in the Internet, in the vast majority of scenarios, information about servers, i.e., which IP address one must contact in order to proceed, must be publicly available. In p2p-based communication, in which all endpoints can act both as clients and servers, this means that association between an endpoint and such an application becomes publicly visible. Even in classical client-server communication scenarios, information about clients does stay publicly available for a number of reasons (e.g., at website user access logs, forums, proxy logs, etc.). Given that many other forms of communication and various endpoint behavior (e.g., game abuses) does get captured and archived, this implies that enormous information, invaluable for characterizing endpoint behavior at a global scale, is publicly available -on the web.. The first contribution of this paper is the introduction of a novel methodology, which we term 'unconstrained endpoint profiling', for characterizing endpoint behavior by strategically combining information from a number of different sources available on the web. The key idea is to query the Google search engine [6] with IP addresses corresponding to arbitrary endpoints. In particular, we search on text strings corresponding to the standard dotted decimal representation of IP addresses, and then characterize endpoints by extracting information from the responses returned by Google. The core components of our methodology are (i) a rule generator that operates on top of the Google search engine, and (ii) an IP tagger, which tags endpoints with appropriate features based solely on information collected on the web. The key challenge lies in automatically and accurately distilling valuable information from the web and creating a semantically-rich endpoint database.. We demonstrate that the proposed methodology shows remarkable advances in the following scenarios: (i) even when no operational traces from a given network are available, it can accurately predict traffic mixes, i.e., relative presence of various applications in given networks, (ii) when packet-level traces are available, it can help dramatically outperform state of the art traffic classification algorithms, e.g., [29] , both quantitatively and qualitatively and, (iii) when sampled flow-level traces are available, it retains high classification capabilities when other state-of-the-art schemes literally fall apart.. Our second contribution lies in exploiting our methodology to perform, to the best of our knowledge, the first-of-its-kind Internet access trend analysis for four world regions: Asia, S. and N. America, and Europe. Not only do we confirm some common wisdom, e.g., Google massively used all around the world, Linux operating system widely deployed in France and Brazil, or multiplayer online gaming highly popular in Asia; we confirm fascinating similarities and differences among these regions. For example, we group endpoints into different classes based on their application usage. We find that in all explored regions, the online gaming users strongly protrude as a separate group without much overlap with others. At the same time, we explore locality properties, i.e., where do clients fetch content from. We find strong locality bias for Asia (China) , but also for N. America (US), yet much more international behavior by clients in S. America (Brazil) and Europe (France). This paper is structured as follows. In Section 2 we explain our unconstrained endpoint profiling methodology which we evaluate in a number of different scenarios in Section 3, and apply this approach to four different world regions in Section 4. We discuss related issues in Section 5, and provide an overview of related work in Section 6. Finally, we conclude in Section 7. In this paper, we proposed a novel approach to the endpoint profiling problem. The key idea is to shift the research focus from mining operational network traces to extracting the information about endpoints from the web. We developed and deployed a profiling tool that operates on top of the Google search engine. It is capable of collecting, automatically processing, and strategically combining information about endpoints, and finally tagging the same with extracted features. We demonstrated that the proposed approach can (i) accurately predict application and protocol usage trends even when no network traces are available; (ii) dramatically outperform state-of-the-art classification tools when packet traces are available; and (iii) retain high classification capabilities even when only sampled flow-level traces are available.. We applied our approach to profile endpoints residing at four different world regions, and provided a unique and comprehensive set of insights about (i) network applications and protocols used in these regions, (ii) characteristics of endpoint classes that share similar access patterns, and (iii) clients' locality properties. Our approach opens the doors for revealing people's interests and affinities far beyond those related to network applications and protocols. Indeed, the Internet is only a medium that people use to express their social interests and needs. Generalizing our approach to understand such interests and needs, i.e., by exploring the content that clients access, is an exciting research challenge we plan to tackle. (23) fedora (39) unix (13) opensuse (11) ubuntu (72) debian (34) opensuse(105) mandrivalinux(78) slackware (20) debian (17) mandrivalinux (10) suse (10) suse (20) unix (13) fedora (12) unix(76) mandrake(60) suse (13) gentoo (10) gentoo (7) mandrake (5) gentoo (10) slackware (2) suse(50) fedora-fr(26) gentoo(19) fedora (10) opensuse (4) slackware (5) mandrake (2) knoppix-fr (10) (29) opera (54) netscape (49) netscape (72) opera (20) netscape (75) opera (29) maxthon (14) enigma (17) maxthon (3) enigma (8) (92) virus (272) dnsbl (239) malware (112) dnsbl (89) banlist (116) blacklist (98) worm (30) virus (29) trojan (21) malware (210) bots (90) net-abuse(85) spyware(54) googlebot(98) dnsbl(50) spyware (17) hijack (5) googlebot (48) trojan (35) virus (52) hijack (32) virus (50) bots (35) quakeabuse (4) stormworm (4) quakeabuse (34) banlist (28) adservers (24) worm (20) adservers (16) spyware (15) banlist (4) spyware (12) worm (10) stormworm (12) trojan (7) stormworm (9) trojan (7) hijack (8) stormworm (10) banlist (5) quakeabuse (4) hijack (5) (14) ed2k (4) Foxy (3) bitcomet (3) emule (1) (6) mysql(79) pop3 (13) mms (23) pop3 (13) mysql(66) mms(33) pop3(2) netbios (1) mms (9) netbios (2) netbios (2) mysql (1) netbios (20) (4) irc (93) skype (1) messenger (8) skype (6) skype(12) Gaming counter-strike(37) quake (36) sims (261) poker (145) worldofwarcraft (32) counter-strike(49) quake(43) mmorpg (30) starcraft (21) counter-strike(144) mmorpg (30) poker (14) halo (5) poker (26) sims (23) poker (14) warcraft (6) sims (4) warcraft (19) quake (9) quake (4) sims (2) warcraft(7) mmorpg(7) world of warcraft (8) halo (4) cstrike (1) world of warcraft (5) halo (5) How accurate is the information on the web? The first question we discuss here is how trustworthy is the information on the web? To get a sense for this, we performed small scale experiments. In particular, we checked links posted on forums; also, we did a port-scan against randomly chosen servers from various server lists available on the web. We found that the information is highly accurate. The vast majority of links posted on forums were active, pointing to the 'right' content. Likewise, the ports that were found active on the servers that we checked fully correlate with the information available on the web.. How up-to-date is the information on the web? This is related to the following two questions: (i) How quickly can we detect new or updated information about endpoints? (ii) How can we detect if the information on a given site is outdated? For the first issue, we depend upon Google, which is capable of quickly detecting new content on the web; the Google crawler determines how frequently content changes on a page and schedules the frequency of crawl to that page accordingly [7] . For detecting outdated information, we can leverage the following information: First, many websites provide information about the time the content was 'last updated'. Likewise, entries on Internet forums typically indicate the date and time of access. In both cases, this information could be used to filter-out outdated information, e.g., older than a given date. Dynamic IP addresses. The number of endpoints using dynamic IP addresses is not negligible in today's Internet [45] . Because such IP addresses are used by multiple endpoints, different clients' activities can be projected on the same address. Note that servers are typically not run over dynamic IP addresses and even if they are, they have to use a dynamic DNS service to be mapped to a DNS name. In this case, our endpoint classification could be enhanced with information about dynamic IP addresses as obtained from logs maintained at dynamic DNS registries. While our current endpoint classification approach is primarily dependent on information about servers hosted on static IP addresses, it can also be used to accurately detect dynamic IPs. Indeed, if an IP address shows highly active behavior and matches to an abnormally large number of different applications, that could imply a dynamic IP address.. Using other sources of information. Not all information about the endpoints is directly available on the web. The most important example is p2p file sharing or streaming. Indeed, the 'entry points' to such systems are necessarily available on the web (e.g., torrentportal.com). Yet, the next stage in communication, i.e., getting the appropriate peer IP address to download a file from, is not necessarily available in the web. Still, this information is publicly available. It could be collected in a straight-forward way by crawling such systems (e.g., [31] ).. Non-Latin keywords. While we currently rely on parsing Latin language scripts to generate our keyword set, even this allows us to develop interesting insights about the non-Latin language speaking countries, as we have shown while analyzing a network trace from Asia. In future, however, we plan to extend our methodology towards parsing non-Latin language pages in order to develop a more comprehensive keyword set. Information available on the web has traditionally been crawled and indexed by generic search engines such as Google [6] , Yahoo [18], Ask [2] and Microsoft Search [13] . However, recently there has been a steady increase in 'vertical search engines' that crawl and index only specific content such as Indeed [8], a job search engine and Spock [15] , a people search engine. To the best of our knowledge, this paper is the first to propose using information available on the web for understanding endpoints, i.e., IP addresses. In this regards, our work can be considered as a first but important step towards developing a vertical search engine for endpoints. Indeed, one of our future research directions is to build such a crawler to index IP address information from the web (instead of overriding on generic search engines).. In the context of correlating multiple sources of information, our work is closely related to [45] and [23] . The authors in [45] correlate email addresses with IP addresses to determine which IP addresses are dynamic. The authors in [23] correlate various IP address lists such as Bots, Phishing sites, Port scanners and Spammers to conclude that botnet activity predicts spamming and scanning while phishing activity appears to be unrelated to others. While similar to [23] one of the tags generated by our method is malware we also provide for a wide variety of tags (Table1) using a complete behavior profile for an endpoint.. Most existing traffic classification techniques classify traffic on the basis of characteristics of the traffic stream itself: (i) port numbers are used to classify traffic in [26, 28, 29, 37, 38, 42] , however, they have been rendered ineffective because applications continually change port numbers to evade detection, e.g., Skype; (ii) payload signatures are used in [26, 28, 43] . However, their demerit is that payload inspection is expensive and ineffective on encrypted payloads; and (iii) numerical and statistical techniques in [20, 27, 29, 35, 38, 42] inspect flows for their properties such as average packet size, average flow duration, distribution of ports, etc., and cluster flows accordingly. However, their effectiveness decreases rapidly with sampling rate as shown in Section 3 for a representative technique, BLINC [29] . We depart from looking into the traffic stream to characterize it, and propose a fundamental shift in the traffic classification problem by first classifying the endpoints themselves via information available on the web. Our 'unconstrained endpoint profiling' is able to achieve high classification rates even at high sampling rates. Here, we propose a new methodology, which we term 'Unconstrained Endpoint Profiling' (UEP). Our goal is to characterize endpoints by strategically combining information available at a number of different sources on the web. Our key hypothesis is that records about many Internet endpoints' activities inevitably stay publicly archived. Of course, not all active endpoints appear on the web, and not all communication leaves a public trace. Still, we show that enormous amounts of information do stay publicly available, and that a 'purified' version of it could be used in a number of contexts that we explore later in the paper. First, we introduce an algorithm we selected to perform endpoint clustering. The key objective of such clustering is to better understand endpoints' behavior at a large scale in different world regions. Employing clustering in networking has been done before (e.g., [22, 25, 46] ). We select the autoclass algorithm [21] , mainly because it provides unsupervised clustering. This means that, in a Bayesian manner, it can actually infer the different classes from the input data and classify the given inputs with a certain probability into one of these classes. The autoclass algorithm selects the optimal number of classes and also the definition of these classes using a Bayesian maximum posterior probability criterion. In addition to accurate clustering, the algorithm also provides a ranking of the variables according to their significance in generating the classification.. For each of the regions we explore, input to the endpoint clustering algorithm is a set of tagged IP addresses from the region's network. Since in this case we are interested in the access behavior of users in the network, we determine the tags via an extension of the mapping in Table 4 . For regions with traces, if an in-network IP address sends/receives traffic to/from an out-network IP address which is tagged by a server tag, e.g., as website, then the innetwork address is tagged appropriately (using the mapping from column 2 to 3 in the table) as browsing. For regions with no trace (Europe), if an in-network IP address has a client tag found via the endpoint method, then it is tagged via the mapping from column 1 to 3 in the table and we also note the URL 9 of the site where the tag was obtained from. Thus, the in-network IP addresses are tagged as browsing, chat, mail, p2p, ftp, streaming, gaming, malware or combination thereof. The sample set for the explored networks is around 4,000 in-network IP addresses for all regions except N. American, where we gather about 21,000 addresses. Table 7 lists the top clusters generated for each region. It also provides the proportion of endpoints from a region that were grouped into a cluster. It should be noted that this result captures correlation in clients' behavior, not necessarily the absolute presence of a given characteristic. The insights from Table 7 are as follows.

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 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 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 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 O O 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Collision avoidance using a model of the locust LGMD neuron The lobula giant movement detector (LGMD) system in the locust responds selectively to objects approaching the animal on a collision course. In earlier work we have presented a neural network model based on the LGMD system which shared this preference for approaching objects.We have extended this model in order to evaluate its responses in a real-world environment using a miniature mobile robot. This extended model shows reliable obstacle detection over an eight-fold range of speeds, and raises interesting questions about basic properties of the biological system. For animals the ability to detect approaching objects is important for survival, serving both to prevent collisions as the animal moves and also to avoid capture by predators [30] . While the fate of a mobile robot is unlikely to involve being eaten the ability to avoid collisions is equally important. Traditionally robotic technology has involved active sensors, such as ultrasound and infra-red devices, or high-precision sensors, such as laser scanners, for the detection of obstacles [7] . In biology, however, many examples are found of systems which rely on visual information to accomplish this task, and these have been successfully applied to real-world robotic control tasks [8, 49] . Neurons tuned to respond to approaching objects have been identified in species as diverse as pigeons [42] and locusts [31, 38] .. The lobula giant movement detector (LGMD), a large visual interneuron in the optic lobe of the locust [24] , is one such neuron. Originally the LGMD was thought to be tuned to detect novel movement of small objects [35] . However, more recent work has shown that the LGMD responds most strongly to approaching objects [31, 38] and that it is tightly tuned to objects approaching the animal on a direct collision course [14] . Receding objects produce little or no response [31] . Approaching objects are distinguished by the LGMD using the increasing speed of edge movement and increasing length of the edges [40] . The response dynamics during the approach of an object are the subject of two conflicting reports, one showing that the spike rate increases continuously during approach [31] , the other showing that the spike rate may peak before collision [9, 12] . At the time of writing, this conflict is unresolved. The LGMD is believed to play a role in triggering escape jumps and steering responses during flight. This belief is supported by the strong connection of the LGMD to the descending contralateral movement detector (DCMD) neuron [23] which in turn makes connections with interneurons and motoneurons in the thoracic ganglia [5, 39] . Responses in the DCMD match responses in the LGMD one-for-one [23, 27] .. A neural network model of the input circuitry of the LGMD was developed by Rind and Bramwell [29] . Based closely on the anatomy and physiology of the optic lobe, the network comprised three principal layers:. • the input photoreceptive layer, which responded to changes in the image in order to detect the edges of moving objects; • a processing layer, where excitation passed retinotopically through the network while delayed inhibition spread laterally; • the output layer, which represented the LGMD, where the excitation and inhibition were combined. In addition, a feed-forward pathway inhibited the output of the model LGMD during large changes in the image, such as those caused by ego-motion. This model displayed the same preference for approaching objects as the LGMD and revealed that, at least for simple stimuli, this preference results from a critical race between the excitation produced by the movement of an object's edges and the delayed lateral flow of inhibition within the network. This model was subsequently extended to allow the network to respond to textured stimuli [3] .. In this paper we evaluate the behaviour of the LGMD network in a real-world environment. We show that robust obstacle detection can be achieved using an insect-based solution which relies on vision. Our results have interesting implications for the understanding of the biological LGMD system and demonstrate the potential of our LGMD model for real-world obstacle detection applications. In our investigations we attempt to develop biologically realistic models of sensory processing which can be applied to mobile platforms. The LGMD circuit investigated in the present study is a step in this direction. This approach illustrates the synergistic relationship between basic neuroscientific research, modelling studies using digital simulation and real-world devices, and neuromorphic engineering.. We have demonstrated that our model of the LGMD system of the locust detected approaching obstacles robustly in a complex visual environment, but that the responses were less predictable than those obtained using simple stimuli. We will continue to investigate the responses of both the neuron and our robot-based model to complex stimuli in order to determine the behavioural properties of the LGMD and assess possible applications in robot control systems. Although the precision of visual systems such as the LGMD is limited when compared with traditional robotic range sensors, their widespread use in diverse animal species suggests that such systems can become valuable tools for robotics engineers. In this project we evaluated a biologically based model of the locust LGMD in behavioural terms using a mobile robot. We found that the model responded reliably as the robot approached obstacles in its path over an eight-fold range of speeds. This illustrates that robust obstacle avoidance can be achieved using an insect based solution which relies on vision. Our experiments provided new insights into the dynamic responses of our model, which will be investigated in future experiments using both the model and the neuron. The directional selectivity of both the LGMD neuron and our neural network model is illustrated in Fig. 9 . During the approach of an object the spike rate from the LGMD increases whereas there is only a brief response to receding objects. This response is found over a wide range of speeds of movement [28] and is tightly coupled to objects moving on a direct collision course [14] .. In our present study we ran two series of experiments. In the first set of experiments, using only reac- tive infra-red based control for the robot, we recorded the responses of the LGMD model in isolation. In the second, the LGMD responses were used to control the robot. The parameters of the LGMD model, which are given in Appendix A, were modified to match the responses to the speed of movement of the robot, a strategy also observed in insect visual systems [22] .

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


Criticisms on and comparison of experimental channel backscattering extraction methods In this paper we critically review and compare experimental methods, based on the Lundstrom model, to extract the channel backscattering ratio in nano MOSFETs. Basically two experimental methods are currently used, the most common of them is based on the measurement of the saturation drain current at different temperatures. We show that this method is affected by very poor assumptions and that the extracted backscattering ratio strongly underestimates its actual value posing particular attention to the backscattering actually extracted in nano devices. The second method is based on the direct measurement of the inversion charge by CV characteristics and gets closer to the physics of the backscattering model. We show, through measurements in high mobility p-germanium devices, how the temperature-based method gives the same result of the CV -based method once that its approximations are removed. Moreover we show that the CV-based method uses a number of approximations which are partially inconsistent with the model. In particular we show, with the aid of 2D quantum corrected device simulations, that the value of the barrier lowering obtained through the CV-based method is totally inconsistent with the barrier lowering used to correct the inversion charge and that the extracted saturation inversion charge is underestimated. Due to the continuous downscaling of MOSFET geometry, improved physical models are needed to accurately study the charge transport in the channel. One of the simplest and most successful models was proposed by Lundstrom and co-workers [1] [2] [3] [4] built on the Natori model for ballistic MOSFETs [5] . In his Backscattering model, the charge transport in the channel is regulated by the injection of the near equilibrium thermal carriers at the top of the source-channel potential barrier (the virtual source). Only a fraction of the injected carriers reaches the drain side due to scattering in the channel. In saturation the backscattering coefficient r is defined as the ratio between the backward-going flux (I À ) and the forward-going flux (I + ) at the virtual source (Fig. 1) . The strength of the model is that it provides just a number, the backscattering coefficient r, which includes all scattering mechanisms in the channel. Quasi-ballistic transistors have r close to zero [6] , so that all the injected carriers reach the drain side providing maximum current drive. Technology developers and transistor designers must aim at devices with low r in order to enhance performance. In this sense the backscattering coefficient is a parameter which provides information about the advantage, in terms of ON current, related to the scaling of a given technology (material and/or architecture). For these reasons it is very important to have experimental procedures which accurately estimate the backscattering coefficient [23] . Up to now, two methods have been used to extract r: a temperature-based method proposed by Chen et al. [7] , in which the backscattering coefficient is extracted by measuring the saturation current at different temperatures, and a capacitance-voltage (CV)-based method proposed by Lochtefeld and co-workers [8] [9] [10] , in which the inversion charge is obtained from the measurement of the gate-to-channel capacitance. Both methods are based on approximations that can lead to sensible errors in the backscattering estimation. Especially the first, which is more used due to its simplicity [11] [12] [13] [14] [15] , is affected by a number of approximations resulting in an excessive underestimation of r. In this paper we compare the two methods and address their approximations. We show how the temperature-based method gives the same result of the CV-based method once that its approximations are removed. Moreover we show that the CV-based method uses itself a number of approximations which are partially inconsistent with the model. In particular we show, with the aid of 2D quantum corrected device simulations, that the value of the barrier lowering obtained through the CV-based method is totally inconsistent with the barrier lowering used to calculate the saturation inversion charge and that the extracted saturation inversion charge is underestimated. The remainder of the paper is divided as stated in the following. In Section 2 the backscattering model is presented. In Section 3 the temperature-based method and the CV -based method are presented. In Section 4 and in Appendix A the methods are compared through measurements in short channel germanium p-MOSFETs. In Section 5 the issues related to the Lochtefeld method are discussed with the aid of two-dimensional device simulation. In Section 6 we present our conclusions. In this paper we have revised and discussed the issues related to the principal methods used in the literature to extract the channel backscattering ratio in nano MOSFETs based on the Lundstrom transport model. We have shown that the Chen method, which is actually the most used, is affected by a number of strong approximations which lead to an underestimation of the extracted backscattering. In particular we found that the stronger approximation is that of assuming a linear shape of the potential inside the critical layer. The method has been compared to the Lochtefeld method, which better captures the physics of the backscattering model, through measurements in high mobility germanium p-MOSFETs. Moreover we discussed, with the aid of 2D quantum corrected device simulations, the inconsistencies of the Lochtefeld method which are mainly related to the saturation inversion charge calculation. We found that the barrier lowering used to calculate the charge is strongly different from the barrier lowering obtained as the result from the method and that the charge calculation is done neglecting the contribution of scattering. These issues, together with a difficult estimation of the charge at the virtual source, lead in erroneous calculation of backscattering and inversion charge.

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Robust Weighted LAD Regression The least squares linear regression estimator is well-known to be highly sensitive to unusual observations in the data, and as a result many more robust estimators have been proposed as alternatives. One of the earliest proposals was least-sum of absolute deviations (LAD) regression, where the regression coefficients are estimated through minimization of the sum of the absolute values of the residuals. LAD regression has been largely ignored as a robust alternative to least squares, since it can be strongly affected by a single observation (that is, it has a breakdown point of 1/n, where n is the sample size). In this paper we show that judicious choice of weights can result in a weighted LAD estimator with much higher breakdown point. We discuss the properties of the weighted LAD estimator, and show via simulation that its performance is competitive with that of high breakdown regression estimators, particularly in the presence of outliers located at leverage points. We also apply the estimator to several real data sets. The linear regression problem is certainly one of the most important data analysis situations (if not the most important). In this situation the data analyst is presented with n observations of a response variable y and some number p of predicting variables x 1 , . . ., x p satisfying y = Xβ + ε,. where ε is a vector of errors and. (note that usually x 1 is a column of ones, but this is not required). We assume that X is of full rank (that is, r(X) = p).. It is well-known that if the errors ε are normally distributed with constant variance the optimal estimator of β is the ordinary least squares (OLS) estimator, based on minimizing the 2 -norm y − X β 2 = n i=1 (y i − x i β) 2 of the residuals. Unfortunately, it is also wellknown that the least squares estimator is very nonrobust, being highly sensitive to unusual observations in the y space (outliers) and X space (leverage points).. A way of quantifying this sensitivity is through the notion of the breakdown point of a regression estimator (Hampel, 1968) . Suppose we estimate the regression parameters β by some technique τ from data (X, y), yielding the estimate β τ . If we contaminate m. (1 ≤ m < n) rows of the data in a way so that row i is replaced by some arbitrary data x i , y i , we obtain some new data X, y . The same technique τ applied to X, y yields estimates β τ X, y that are different from the original ones. We can use any norm · on . the minimum proportion of rows of (X, y) that if replaced by arbitrary new data make the regression technique τ break down. The minimum possible value of the breakdown point is 1/n and the maximum value is .5 (the latter value since otherwise it is impossible to distinguish between the uncontaminated data and the contaminated data). Clearly, the larger the breakdown point, the more robust is the regression estimator.. The least squares regression estimator has breakdown 1/n, and many alternative estimators have been proposed to provide more robust regression estimation. One of the earliest proposals was regression performed through minimization of the 1 norm of the residuals,. There is a good deal of empirical evidence going back more than 30 years that LAD regression is more robust than OLS in the presence of fat-tailed errors (see, e.g., Sharpe, 1971) .. Despite this, LAD regression has gained relatively little favor in the robustness statistical literature, for several perceived reasons:. 1. LAD estimation is more than difficult than OLS estimation, because of the nondifferentiability of the criterion function. In fact, as Portnoy and Koenker (1997) pointed out, modern linear programming techniques have made the computation time of LAD regression competitive with, or even superior to, OLS even for extremely large data sets. This can be contrasted with high breakdown regression estimators, which are far more computationally intensive than OLS (Rousseeuw and Leroy, 1987; Hawkins and Olive, 2002) . As an example of the gains these new methods can provide, LAD estimation for a dataset with 100,000 observations and 100 variables takes more than 12 minutes using the Barrodale-Roberts modified simplex algorithm and only 30 seconds using the Frisch-Newton interior point algorithm on a Pentium 4 PC (3.2GHz processor) using the quantreg package (Koenker, 2004) .. 2. The asymptotic theory for LAD regression is not as well-developed as for OLS regression. While this is true to a certain degree, it is also true for high-breakdown regression estimators.. 3. The LAD regression estimator is not at all robust to observations with unusual predictor values; that is, it has a low breakdown point.. It is this final claim that is the starting point for this paper. In the next section, we discuss what is known about the breakdown of LAD regression, focusing on recent exact results. We discuss how the use of a weighted version of LAD regression can improve its robustness considerably, and describe an algorithm for choosing weights that leads to such improvements. In Section 3 we give the asymptotic properties of the resultant estimators, and examine finite-sample properties using Monte Carlo simulations. We apply the method to several data sets in Section 4. We conclude the paper with discussion of potential future work. In this paper we have proposed a weighted version of LAD regression designed to increase the breakdown of the estimator that is easy to compute and has performance competitive with high breakdown estimators, particularly in the presence of leverage points. These weighting ideas also apply to other estimators. Given the good bias properties of the WLAD estimator, it is reasonable to wonder if a similar weighting scheme used for a more efficient GM-estimator, such as that of Krasker and Welsch (1982) , would reduce the variance while preserving robustness, and be even more effective than WLAD. The LAD estimator is a special case of regression quantile estimators (Koenker and Bassett, 1978; Koenker, 2000) , which have been shown to be useful in highlighting interesting structure in regression problems, including nonnormality and heteroscedasticity in the error distribution; it would be interesting to see if weighted versions of such estimators would be more resistant to the effects of unusual observations.

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


Fan-type degree condition restricted to triples of induced subgraphs ensuring Hamiltonicity In 1984, Fan gave a sufficient condition involving maximum degree of every pair of vertices at distance two for a graph to be Hamiltonian. Motivated by Fan's result, we say that an induced subgraph H of a graph G is f -heavy if for every pair of verticesG is called R-f -heavy if every induced subgraph of G isomorphic to R is f -heavy. For a family R of graphs, G is R-f -heavy if G is R-f -heavy for every R ∈ R. In this note we show that every 2-connected graph G has a Hamilton cycle if G is {K 1,3 , P 7 , D}-f -heavy or {K 1,3 , P 7 , H}-f -heavy, where D is the deer and H is the hourglass. Our result is a common generalization of previous theorems of Broersma et al. and Fan on Hamiltonicity of 2-connected graphs. In this note, we give a new sufficient condition for Hamiltonicity of graphs by restricting. Fan's condition to triples of induced subgraphs of graphs.. In fact, the idea that one can guarantee Hamiltonicity of graph by restricting Fan's condition to pairs of induced subgraphs dated from Bedrossian, Chen and Schelp [2] .. Later, Chen, Wei and Zhang [8, 9] , and Li, Wei and Gao [13] got related results with this similar idea. Note that Bedrossian [1] characterized all pairs of forbidden subgraphs {R, S} for Hamiltonicity of 2-connected graphs. Thus we can pose this problem: which two connected graphs R and S other than P 3 imply that every 2-connected {R, S}-f -heavy graph is Hamiltonian? Recently, this problem has been completely solved in [14] .. Brousek [7] gave a complete characterization of triples of connected graphs {K 1,3 , R, S} such that a graph G being 2-connected and {K 1,3 , R, S}-free is Hamiltonian. Thus we can pose the following problem naturally.

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Region- and action-aware virtual world clients We propose region-and action-aware virtual world clients. To develop such clients, we present a parameterized network traffic model, based on a large collection of Second Life traces gathered by us. Our methodology is also applicable to virtual worlds other than Second Life. With the traffic model, various optimization criteria can be adopted, including visual quality, response time, and energy consumption. We use energy consumption as the show case, and demonstrate via trace-driven simulations that, compared to two existing schemes, a mobile client can save up to 36% and 41% communication energy by selectively turning on its WiFi network interface. Virtual worlds are computer-simulated environments that allow users to interact with each other via graphical avatars. Various virtual worlds, such as Second Life [2010] , Habbo Hotel [2010] , and Playstation Home [2010] , enable many potential applications in entertainments, distance learning, virtual work space, virtual shopping malls, and online art galleries, and thus are becoming increasingly popular. Second Life, as one of the most well-known virtual worlds, was fairly popular since late 2000's. For example, Second Life had almost one million users in August 2010 [Second Life Statistics 2010] , and major companies and organizations like IBM, US Army, Time Warner, and Walt Disney are adopting Second Life for training, teleconference, and marketing [Second Life Studies 2010] . We consider Second Life as an example of virtual worlds throughout this article. However, we acknowledge that the population (number of users) of Second Life seems to be saturated at the time of writing. This is not a serious concern, as the proposed methodology and some of the insights derived from our experiments are also applicable to other 3D virtual worlds, including those that may be developed in the future.. A virtual world is a space consisting of multiple regions. The users interact with the virtual world via various actions, such as walk and fly. Different from online games that distribute static game data to users using physical media such as DVDs, virtual worlds consist of user-generated and dynamic inworld objects that can only be downloaded on demand. The bandwidth requirements of virtual worlds therefore are much higher than online games [Kumar et al. 2008] . More importantly, our analysis, presented in Section 4, reveals that the bandwidth requirement of a virtual world client highly depends on the avatar's current action and region. For example, within the same area of a region, a standing avatar incurs less traffic than a flying avatar; moreover, for the same action, an avatar in a downtown area imposes more traffic than another avatar in a rural area. Therefore, we propose novel virtual world clients, which infer traffic statistics from current region and action, and leverage the statistics for various optimization criteria, such as visual quality, energy consumption, and response time. We refer to such clients as region-and action-aware virtual world clients. We present a case study in Section 7, in which we consider a mobile device that opportunistically connects to two heterogeneous access networks. A region-and action-aware Second Life mobile client can dynamically distribute network traffic between the two access networks and/or turn off one of them when traffic demand is low, so as to minimize the energy consumption.. To implement region-and action-aware virtual world clients, it requires a traffic model that accurately predicts the traffic statistics in different regions and with various actions. However, the existing virtual world traffic models [Antonello et al. 2009; Ferreira and Morla 2010] are trace-specific, which means model parameters are estimated for an already-captured network trace, and thus cannot be used to predict future network traffic. To address this limitation, we develop a general, parameterized traffic model for Second Life to predict the two important traffic characteristics: packet size and packet inter-arrival time 1 . We systematically derive the model parameters, and validate the model accuracy using real traffic traces collected from Second Life. We also demonstrate in Section 7 how to use these two basic models to predict client bandwidth usage so as to guide the Second Life client to adopt to diverse and dynamic network resource demands. Our simulation results indicate that, compared to two current schemes, the proposed region-and action-aware client saves up to 36% and 41% of communication energy consumption, respectively, which are nontrivial to battery-powered mobile devices, such as smartphones.. This article makes the following contributions.. -We develop a fully automated testbed for efficient and large-scale virtual world traffic analysis, and we collect Second Life traces from 100 diversified regions. The total length of our traces is over 60 hours, and the resulting trace files are over 3GB in size.. -We propose region-and action-aware virtual life clients that leverage parameterized traffic models to optimize the client performance based on various criteria such as visual quality, energy consumption, and response time. As a case study, we demonstrate how to conserve energy by dynamically distributing traffic between two access networks on mobile devices. Region-and action-aware virtual world clients have not been considered in the literature.. -We develop the first parameterized traffic model for virtual worlds, which is of interest in its own right, because: (i) software developers may use it to optimize virtual worlds, (ii) researchers may use it to conduct realistic simulations, and (iii) Internet Service Providers (ISPs) may use it to better engineer their networks.. The rest of this article is organized as follows. Section 2 presents an overview on Second Life, a popular virtual world. We describe our network traffic collection methodology in Section 3. This is followed by traffic analysis in Section 4. In Section 5 we develop the virtual world traffic model, and we evaluate its accuracy in Section 6. Section 7 presents a case study of energy-efficient mobile Second Life client. We survey other virtual world traffic models in Section 8, and conclude the work in Section 9. We proposed region-and action-aware virtual world clients that work with different optimization criteria, including high visual quality, low energy consumption, and short response time. We instantiated a region-and action-aware Second Life client that minimizes the communication energy consumption of a mobile device. The trace-driven simulation results indicate that our mobile client can save up to 36% and 41% communication energy, compared to the other two current schemes. To realize region-and action-aware Second Life clients, we developed a testbed to collect a large number of network traces from the live Second Life network. We then constructed a parameterized Second Life network traffic model. In contrast to existing trace-specific models [Antonello et al. 2009; Ferreira and Morla 2010] , our model is general in the sense that it works for various regions and different actions. We showed that our model generates realistic network traces: it achieves a small average λ 2 value of 0.19, which is 50% smaller than that of a state-of-the-art model [Antonello et al. 2009 ]. Last, despite that our work was based on Second Life, the proposed methodology is also applicable to other virtual worlds. To the best of our knowledge, region-and action-aware clients have not been studied before. In this section, we survey the network traffic and avatar mobility models proposed for virtual worlds in the literature.

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


Randomized diffusion for indivisible loads We present a new randomized diffusion-based algorithm for balancing indivisible tasks (tokens) on a network. Our aim is to minimize the discrepancy between the maximum and minimum load. The algorithm works as follows. Every vertex distributes its tokens as evenly as possible among its neighbors and itself. If this is not possible without splitting some tokens, the vertex redistributes its excess tokens among all its neighbors randomly (without replacement). In this paper we prove several upper bounds on the load discrepancy for general networks. These bounds depend on some expansion properties of the network, that is, the second largest eigenvalue, and a novel measure which we refer to as refined local divergence. We then apply these general bounds to obtain results for some specific networks. For constant-degree expanders and torus graphs, these yield exponential improvements on the discrepancy bounds compared to the algorithm of Rabani, Sinclair, and Wanka [14] . For hypercubes we obtain a polynomial improvement. In contrast to previous papers, our algorithm is vertex-based and not edge-based. This means excess tokens are assigned to vertices instead to edges, and the vertex reallocates all of its excess tokens by itself. This approach avoids nodes having "negative loads" (like in [8, 10] ), but causes additional dependencies for the analysis. During the last years, large parallel networks became widely available for industrial and academic users. An important prerequisite for their efficient usage is to balance the workload efficiently. Load balancing also has applications in scheduling, routing, numerical computation, and finite element computations.. In this paper we analyze a very simple neighborhood-based load balancing algorithm. We assume that the processors are connected by an arbitrary d-regular network. In the beginning, every vertex has a certain number of tokens (load). The goal is to distribute the tokens as evenly as possible. More precisely, we aim at minimizing the difference between the minimum load and the maximum load, which we call discrepancy.. In general, neighborhood-based load balancing algorithms operate in parallel steps (sometimes also called rounds). In each step, every processor is allowed to probe the load of all of its neighbors (diffusion load balancing), or to probe the load of one neighbor (dimension exchange). Then each processor decides how much load it will forward to its neighbors. In this paper, we consider a very natural diffusion-based approach. In the continuous diffusion model, where tokens can be split arbitrarily, the method works as follows. Along each edge a load of load -difference/(d + 1) is sent from the vertex with the higher load to the vertex with less tokens. Note that this method balances the load perfectly if the number of steps is sufficiently large. Here we consider the (arguably more realistic [15] ) case of discrete diffusion where tokens are indivisible. Quantifying by how much the integrality assumption decreases the efficiency of load balancing is an interesting question and has been posed by many authors (e.g., [8, [11] [12] [13] [14] [15] ).. Most results known so far ( [9, 10, 14] ) employ an edge-oriented view where each edge decides between forwarding either load -difference/(d + 1) or load -difference/(d+1) tokens (referred to as rounding up or rounding down). Rounding up results in a load balancing algorithm that keeps sending tokens back and forth between processors with a small load difference. Another disadvantage is that the approach can gener-ate "negative loads" for vertices with only a few tokens. On the other hand, always rounding down results in a discrepancy of up to d·diam(G), where diam(G) denotes the diameter of the underlying graph G. To overcome these problems we adopt a vertex-oriented view in this paper where the vertices (not edges) decide randomly how much they are sending. We presented a new diffusion-based load-balancing scheme which is very simple and avoids negative load. We show bounds on the discrepancy for general graphs depending on the local (or refined local) divergence and the eigenvalue gap of the graph. For (constant-degree) expander graphs we prove a discrepancy of O(log log n), for hypercubes of O(log n), and for r-dimensional torus graphs of O( √ log n ).

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Periodic Plus Smooth Image Decomposition Abstract When the Discrete Fourier Transform of an image is computed, the image is implicitly assumed to be periodic. Since there is no reason for opposite borders to be alike, the "periodic" image generally presents strong discontinuities across the frame border. These edge effects cause several artifacts in the Fourier Transform, in particular a wellknown "cross" structure made of high energy coefficients along the axes, which can have strong consequences on image processing or image analysis techniques based on the image spectrum (including interpolation, texture analysis, image quality assessment, etc.). In this paper, we show that an image can be decomposed into a sum of a "periodic component" and a "smooth component", which brings a simple and computationally efficient answer to this problem. We discuss the interest of such a decomposition on several applications. In this paper, we proposed a simple and efficient way to deal with periodization artifacts arising from the use of the Discrete Fourier Transform on 2-D images. It consists in the decomposition of the discrete image into a periodic component, that gathers most of the image information but avoids periodization artifacts, and a smooth (harmonic) component that presents very slow variations inside the image domain. In contrast with the PHLST/PHLST5 method [22, 28] (that could be used for the same purpose, though it was primarily intended for local decomposition), the "periodic component" we propose matches more closely the original image, since it is not constrained to be null at the image frame border. We derived explicit formulas in Fourier and image domains, and showed in particular that the associated linear u → per(u) operator is robust to noise and that it could be iterated to yield a projector forcing an exact match of image borders. Applications of the proposed decomposition were discussed, including spectrum analysis, Shannon interpolation, and recent works related to image quality assessment. We believe that the periodic plus smooth decomposition opens interesting perspectives concerning the systematic use of Shannon (sinc) interpolation, whose main drawback (edge effects) can be avoided by considering the periodic component. The present work could be generalized to color images (either by processing the channels independently or by using a more sophisticated distance on colors), and the extension to volumic (3-dimensional or more) images is straightforward. Variants of this work could also be easily investigated by considering other variational formulations, involving for example non-L 2 norms or a more complex neighborhood structure.

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Learning from labeled features using generalized expectation criteria It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domain knowledge in the form of affinities between input features and classes. For example, in a baseball vs. hockey text classification problem, even without any labeled data, we know that the presence of the word puck is a strong indicator of hockey. We refer to this type of domain knowledge as a labeled feature. In this paper, we propose a method for training discriminative probabilistic models with labeled features and unlabeled instances. Unlike previous approaches that use labeled features to create labeled pseudo-instances, we use labeled features directly to constrain the model's predictions on unlabeled instances. We express these soft constraints using generalized expectation (GE) criteriaterms in a parameter estimation objective function that express preferences on values of a model expectation. In this paper we train multinomial logistic regression models using GE criteria, but the method we develop is applicable to other discriminative probabilistic models. The complete objective function also includes a Gaussian prior on parameters, which encourages generalization by spreading parameter weight to unlabeled features. Experimental results on text classification data sets show that this method outperforms heuristic approaches to training classifiers with labeled features. Experiments with human annotators show that it is more beneficial to spend limited annotation time labeling features rather than labeling instances. For example, after only one minute of labeling features, we can achieve 80% accuracy on the ibm vs. mac text classification problem using GE-FL, whereas ten minutes labeling documents results in an accuracy of only 77% Supervised machine learning methods require costly labeled problem instances, and this limits the applicability of learning to new domains. Semi-supervised learning methods [21] , which aim to leverage available unlabeled instances, are an appealing solution for reducing labeling effort. However, despite recent interest in this problem, real applications of semi-supervised learning remain rare. Reasons for this may include the time and space complexity and reliance on sensitive hyperparameters of semi-supervised methods. Additionally, many methods make strong assumptions that may hold in small, synthetic data sets, but tend to be violated in real-world data.. Instead, we want a simple, robust method that facilitates training models for new domains and requires minimal annotation effort. One potential solution involves incorporating existing domain knowledge into learning. There has been much recent interest in this idea [20, 19, 9, 15, 17, 7] . In this paper, we propose a discriminative semi-supervised learning method that incorporates into training one particular type of domain knowledge: affinities between features and classes. For example, in a baseball vs. hockey text classification problem, even without any labeled data, we know that the presence of the word puck is a strong indicator of hockey. We refer to this type of domain knowledge as a labeled feature. Unlike previous heuristic approaches that use labeled features for feature selection or to create labeled pseudo-instances [6, 14, 17, 19, 20] , we use labeled features directly to constrain the model's predictions on unlabeled instances. We specify these soft-constraints using generalized expectation (GE) criteria.. A GE criterion [16] is a term in a parameter estimation objective function that express preferences on values of a model expectation. GE is similar to the method of moments for parameter estimation, but allows us to express arbitrary scalar preferences on expectations of arbitrary functions, rather than requiring equality between sample and model moments. We also note three important differences from traditional training objective functions for undirected graphical models. (1) A one-to-one relationship between GE terms and model factors is not required. For example, a GE term may score expectations on sets of variables that form a subset of parameterized model factors, or on sets of variables larger than model factors. (2) Model expectations in different GE terms can be conditioned on different data sets. ( 3) The score function can be arbitrary. Examples of possible score functions include the distance to some target expectation or a smooth hinge-loss.. In this paper, we use leverage property (3) to specify an objective function that penalizes parameter settings if the resulting model predictions do not conform to prior expectations. We use property (1) to express constraints only on the subsets of variables for which prior information is available. Specifically, for each labeled feature, there is a corresponding GE term that scores the model's predicted class distribution conditioned on the presence of that feature. The score function penalizes these distributions according to their KLdivergence from reference distributions estimated using the labeled features. We derive a specific objective function for a multinomial logistic regression classifier, but the idea is applicable to other discriminative probabilistic models. We refer to this method as Generalized Expectation with Feature Labels, or GE-FL.. We evaluate GE-FL on six text classification data sets. First, we show that GE-FL outperforms several baseline methods that use labeled features. Next, we compare with three previous methods that incorporate labeled features into learning [19, 20, 17] , and show that GE-FL attains comparable or better performance despite using no labeled documents. Finally, we conduct human annotation experiments in which we compare the performance over time of (a) a system that trains a classifier with labeled features using GE-FL and (b) a system that uses semi-supervised training with labeled documents. The results show that given limited annotation time, it is more beneficial to spend that time labeling features rather than labeling instances. For example, after only one minute of labeling features, we can achieve 80% accuracy on the ibm vs. mac text classification problem using GE-FL, whereas ten minutes labeling documents results in an accuracy of only 77%. Given equal labeling time, the accuracy difference is often much more pronounced, with absolute accuracy improvements as high as 40%. In our experiments, labeling features is on average 3.7 times faster than labeling documents, a result that supports similar findings in previous work [18] . In this paper, we have contributed GE-FL, a method for learning discriminative probabilistic models from labeled features and unlabeled documents. In experiments on text classification data sets this method outperforms heuristic methods that leverage labeled features. A preliminary user study supports the claim made in previous work [18] that it is much faster to label a feature than an instance. Consequently, GE-FL can provide dramatic decreases in the amount of time needed to train a classifier for a new domain.. In ongoing research, we are applying GE to models for structured output spaces and to the problems of active learning and domain adaptation. We are also interested in incorporating domain knowledge from ontologies and existing resources, and encoding task-specific structural constraints on the learning problem. The methods described in this paper are semi-supervised [21] . However, the supervision comes in the form of labeled features, or more generally arbitrary expectations from domain knowledge, rather than labeled instances. We suggest that this approach is beneficial because it avoids some of the common assumptions of semi-supervised learning methods. For example, unlike discriminative semi-supervised learning methods such as Transductive Support Vector Machines [12] and Entropy Regularization [8] , we do not assume low-density regions between classes.. There has been much recent interest in incorporating domain knowledge into learning, including several methods that use labels or relevance judgments on features. Nearly all of these methods convert labeled features into labeled instances, and apply a standard learning algorithm. Liu, et al. [14] use human annotators to label features that are highly predictive of unsupervised instance clustering assignments. The unlabeled instances are soft-labeled according to their cosine similarity with pseudo instances that only contain labeled features, and this soft-labeled data is used as an initialization point for the expectation maximization (EM) algorithm. Schapire, Rochery, and Gupta [19] use handcrafted rules based on relevant features to label instances, and modify AdaBoost to choose weak learners that both fit the labeled training data and the soft-labeled data. Wu and Srihari [20] use labeled features to assign labels and confidence scores to unlabeled instances, which are then used in conjunction with labeled data during training. We compare with the methods of Schapire, Rochery, and Gupta [19] and Wu and Srihari [20] in Sections 5.2 and 5.3, respectively. Dayanik, et al. [4] propose several methods that use labeled features to specify prior distributions on the parameters of a logistic regression model.. There is also recent work in the natural language processing community with similar goals. Chang, Ratinov, and Roth [2] propose an EM-like algorithm that incorporates prior constraints into semi-supervised training of structured output models. In the E-step, the inference procedure produces an N-best list of outputs ranked according to the sum of the output's score under the model and a penalty term for violated constraints. In the M-step, the N-best list is used to re-estimate the model parameters. Haghighi and Klein [9] use prototypes, which are analogous to what we refer to as labeled features, to learn log-linear models for structured output spaces. The prototypes are used to hypothesize additional soft prototypes for features that are syntactically similar. All prototypes are then used as features during maximum likelihood training on limited labeled data.. Other types of domain knowledge have also been incorporated into learning. Jin and Liu [11] and Mann and McCallum [15] provide methods for incorporating prior information about the class distribution into discriminative training. Huang and Mitchell [10] propose a new generative clustering model and provide methods for the user to exert influence over the learned clusters. For example, the user can specify that a feature indicates a cluster, an instance belongs to a cluster, or that a cluster should be deleted.. Many of the above methods convert domain knowledge into labeled instances. In this paper, we take an alternative approach in which domain knowledge is used to constrain model predictions. Graça, Ganchev, and Taskar [7] provide a related method that incorporates prior constraints into the EM algorithm. Specifically, the E-step is modified so that the expectation over output variables is the closest distribution (in terms of KL-divergence) to the model prediction that respects a specified set of constraints. In the M-step, the model parameters are re-estimated using this modified expectation. We note several differences between this method and GE-FL. First, the constraints in constrained EM are per-instance, whereas in this paper we use global constraints over entire data sets. Next, Graça et al. use a generative model, whereas here we use direct maximization in a discriminative model. Finally, Graça et al. put constraints only on the output variables, whereas here the constraints additionally consider input variables.. Work in active learning is also relevant. In active learning, the learner can choose the particular instances to be labeled. In pool-based active learning [3] , the learner has access to a set of unlabeled instances, and can choose the instance that has the highest expected utility according to some metric. A standard pool-based active learning method is uncertainty sampling [13] , in which the instance chosen is the one for which the model predictions are most uncertain. Although in theory this method is problematic because it ignores the distribution over instances [5] , in practice it often works well, and is easy to implement. We use uncertainty sampling as a baseline in our user experiments.. Some recent work has addressed active learning by labeling features. Raghavan, Madani, and Jones [18] interleave feedback on instances and features in an algorithm called tandem learning. They show that incorporating feedback on features can significantly accelerate active learning. Experiments also demonstrate that humans can provide accurate information about features, and that it takes five times as long to label instances as to label features. Raghavan and Allan [17] provide additional methods for training SVMs with labeled features, including scaling the parameters of labeled features, creating specially-weighted pseudo-instances containing only labeled features, and soft-labeling unlabeled instances. We compare with tandem learning in Section 5.4. Godbole et al. [6] describe software for interactive classification that uses both feature and instance active learning. Similarly to Raghavan and Allan [17] , Godbole et al. incorporate information about features into training by creating pseudo-instances containing only labeled features. We evaluate the effectiveness of GE-FL on six text classification data sets. For all data sets, instances correspond to documents and features are word counts. For the tasks in which a single instance can be assigned multiple labels, we split the task into L one vs. all binary learning tasks, where L is the number of labels. For other data sets, we use multi-class classification. We describe the data sets below.. • reuters21578: 1 A standard text categorization data set in which task is to assign categories to news articles. We use the ModApte split and evaluate on the top 10 most frequent classes, as in [20] (9603 training instances, 3299 testing instances).. • 20 newsgroups 2 The task is to classify messages according to the newsgroup to which they were posted. We use both the entire data set (20 classes, 20,000 instances) and binary subsets (2,000 instances).. • movie 3 The Polarity v2.0 data set, in which the task is to classify the sentiment of movie reviews as positive or negative (2,000 instances).. • sraa 2 The task is to classify messages about real and model automobiles and aviation with the appropriate newsgroup (4 classes, 73,218 instances).. • webkb 4 The task is to classify university webpages as student, course, faculty, or project (4,199 instances).. • industry sector 2 The task is to classify webpages according to a hierarchy of industrial sectors (4,582 instances). We use binary subsets, and the top level categories (7 classes).. For data sets without a standard test/train split, we randomly split the data such that 75% is used as training data, and the remaining 25% is reserved for testing. For the experiments in sections 5.1, 5.2, and 5.4 we use 10 such random splits and report the mean of the results. For experiments that do not use labeled instances we simulate unlabeled data by hiding labels of all instances. Experiments with GE-FL never include labeled instances.

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