The Women Of Beowulf

The Women Of Beowulf An epic tale of heroes and monsters, the story of Beowulf is filled with excitement and adventure, However Beowulfs importance goes far beyond that of just an excellent literary piece. It also offers many insights into the world of the seventh-century Anglo-Saxon culture. One of the things that is very prevalent in Beowulf is how women are portrayed and expected to act in this society. Anglo-Saxon women that are peaceful and unassertive are considered to be following their roles in society, by greeting guests and serving mead to the men in the mead hall. One such example of the Anglo-Saxon women following this role is Welthow, the queen of the Danes. Women are also portrayed on the opposite end of the spectrum; a perfect example of this would be Grendels mother. She is a strong and fierce monster whom Beowulf must kill. By reading about these two women in Beowulf, we can understand the different ways women are portrayed in this society. Throughout the story of Beowulf, the author sub tly supports the traditional Anglo-Saxon views of women by praising the actions of Welthow, condemning Grendels mother, and showing the need to stop feminine forces like Wyrd; however, the author also contradicts these views on a few rare occasions by sympathizing with Grendels mother, allowing Welthow to assert herself in support of her family. In the story of Beowulf, Welthow is by far the one that one would think of when they picture a typical Anglo-Saxon women in these times. The instance that best demonstrates this is after they all return to Hrothgar. Then Welthow, Hrothgars gold-ringed queen, greeted the warriors a noble woman who knew what was right, she raised a flowing cup to Hrothgar first, holding it high for the lord of the Danes to drink, wishing him joy in the feast. She thanked god for answering her prayers, for allowing her hands the happy duty of offering mead to the heros. (Raffel 28-29) This passage gives a detailed example of what is expected of women in these times. Even on into later years, Women were still expected and encouraged to serve drinks. The wife grew the grapes, harvested the grapes, made the wine, and sold the wine(Collins 26). Even though she is operating as a typical Anglo-Saxon woman she is still a queen. This is evident in her role from that of a traditional Anglo-Saxon woman to a peace maker when she gives a toast in the meadhall Celebrate his courage, rejoice and be generous while a kingdom sits in your palm, a people and power that death will stealà ¢Ã¢â€š ¬Ã‚ ¦. I know your nephews kindness; I know hell replay in kind the goodness you have shown him. (Raffel 51-52) With everyone gathered for the toast, Hrothulf would have second thoughts about betraying his family and taking the throne. In another similar way, in the poem Les Voeux de Paon, a family is quarreling and with the help of a young woman peacemaker, they put down their disagreements, Elyses, a young woman, goes to each knight asking for them to vow to discharge their obligations to arms(Murphy 6) She continues to gently persuade each of them until they all give in to laying down their weapons. However, on the other end of the spectrum we have Grendels mother. Grendels mother defies the traditional role of an Anglo-Saxon woman by being powerful and aggressive. The main difference between Welthow and Grendels mother is that Welthows influence is much more subtle and nonviolent than that of Grendels mother. Being a monster, Grendels mother possesses great warrior-strength (Raffel 57). Grendels mother attacks Herot. Shed taken Hrothgars closest friend, The man he most loved of all men on earth, The wise old kind, trembled in anger and grief, his dearest friend and adviser dead (Raffel 57). The next morning, Beowulf follows her tracks back to her underwater lair. Beowulf goes into her underwater lair and they fight. However Beowulf did not consider the enormous strength she would have. In order to defeat Grendel, Beowulf grabs one of his arms and rips off; on the other hand, Grendels mother fights with Beowulf and almost defeats him. Beowulf only wins the fight because of divine intervention, The ruler of the world, showed me, hanging shining and beautiful on a wall, a mighty old sword (Raffel 71) When later recounting his battle with Grendels mother, he says she fought with such strength that would surpass any man. Despite Beowulf being the hero and Grendels mother being portrayed as a monster, he creates sympathy for Grendels mother by accepting her motive for vengeance and suggesting a close mother-son bond. When Grendels mother is first introduced, she is depicted as a mother mourning her son and out for vengeance. It shows some reason for her attack instead of just being evil. The author continues to build sympathy for Grendels mother by presenting her as having a clear emotional bond with her son. After her attack on Herot, Grendels mother takes the arm of her slain son. Further evidence of the strong emotional attachment between the two is the fact that Beowulf finds Grendels dead body in his mothers underwater lair. The last we hear of Grendel, he is fleeing from Herot with a mortal wound. One can only assume that Grendels mother was mourning the death of her son and unable to let him go. The continued reinforcement of appropriate female roles by presenting two separate and opposing supernatural forces that strongly influence the plot of Beowulf: a masculine God and a feminine Wyrd, suggesting that feminine forces require suppression. Wyrd is a mysterious force that acts as a fate, bringing the heroes of Beowulf ever closer to agony and death; however, God protects Beowulf and helps him in battle. Wyrd works to bring disorder and doom to Beowulf, just as Grendels mother wages war on Hrothgar and his kingdom. Beowulf is able to kill Grendels mother, ending her influence, however he is unable to do anything about the Wyrd except to look to God for help. The story of Beowulf helps paint a picture of what it must have been like to be an Anglo-Saxon woman in those times. The woman who followed the traditional roles are considered good and those who dont are cast out as monsters. Raffel, Burton. Beowulf. 2nd ed. London: Signet Classic, 2008. Print. Collins, James. French Historical Studies. French Historical Studies. 16.2 Print. Murphy, Michael. English Studies. English Studies. 66.2 105. Print.

I’m Not an Imitation of Someone Else, I’m Latina :: Personal Narrative Writing

I’m Not an Imitation of Someone Else, I’m Latina As I sat at the kitchen table on those chilly winter evenings in Kenner, Louisiana, I could feel my mother staring at me from where she was. I was busy doing my homework, and she was preparing that night's supper. She would always start off by asking me what I was doing and the only thing I would ever answer was, "Oh, nothing. Just homework." Then I would turn away and sort of look in the other direction as if to tell her to leave me alone, because I had a lot to do. At the time I was only eight years old, in my second complete year of schooling in the United States. I had already fully grasped the English language, and it had been a year and a half since I had been removed from the bilingual program. In actuality, I had become Americanized quiet easily. Although this was a process that involvedgive and take, because although I did adapt to my new environment very well, I never let go of what I had already learned in my previous environment. I can recall that at the same time that I was learning to read and write in English, I was also learning to do so in my native tongue, Spanish. In school, as I sat in the small wooden house, which was the bilingual classroom, I could clearly remember wondering why it was that "Spot" was so important. For more than a month we had been learning about this brown dog and about seeing him run. This experience was very strange for me, not only because it was in a totally new language but because I never did really see spot run. I only saw him painted on an oversized illustrated notebook. After a long and confusing day at school, I would come home to do my assignments; alone. It wasn't that my mother did not want to help me, but she couldn't. She knew little about the assignment , and knew even less about the language. At first I didn't mind. The assignments were easy for me to figure out, and if it was really hard I would just tell the teacher the next day that I couldn't figure it out. Sh e would ask me why I didn't ask my mother for help, and I would have to respond to her, "because she didn't know either.

New Hoarding Technique for Handling Disconnection in Mobile

Literature Survey On New Hoarding Technique for Handling Disconnection in Mobile Submitted by Mayur Rajesh Bajaj (IWC2011021) In Partial fulfilment for the award of the degree Of Master of Technology In INFORMATION TECHNOLOGY (Specialization: Wireless Communication and Computing) [pic] Under the Guidance of Dr. Manish Kumar INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD (A University Established under sec. 3 of UGC Act, 1956 vide Notification no. F. 9-4/99-U. 3 Dated 04. 08. 2000 of the Govt. of India) (A Centre of Excellence in Information Technology Established by Govt. of India) Table of Contents [pic] 1.Introduction†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 3 2. Related Work and Motivation 1. Coda: The Pioneering System for Hoarding†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 4 2. Hoarding Based on Data Mining Techniques†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 5 3. Hoarding Techniques Based on Program Trees†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 8 4. Hoarding in a Distributed Environment†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 9 5.Hoarding content for mobile learning†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 10 6. Mobile Clients Through Cooperative Hoarding†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 10 7. Comparative Discussion previous techniques†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 11 3. Problem Definition†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 11 4. New Approach Suggested 1. Zipf’s Law †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 2 2. Object Hotspot Prediction Model†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢ € ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 13 5. Schedule of Work†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 13 6. Conclusion†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 13 References†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 14 . Introduction Mobile devices are the computers which are having wireless communication capabilities to access global data services from any location while roaming. Now a day’s mobile devices are supporting applications such as multimedia, World Wide Web and other high profile applications which demands continuous connections and Mobile devices are lacking here. However, mobile devices with wireless communication are frequently disconnected from the network due to the cost of wireless communication or the unavailability of the wireless network.Disconnection period of mobile device from its network is called as offline period. Such offline periods may appear for different reasons – intentional (e. g. , the available connection is too expensive for the user) or unintentional (e. g. , lack of infrastructure at a given time and location). During offline periods the user can only access materials located on the device’s local memory. Mobile systems typically have a relatively small amount of memory, which is often not enough to store all the needed data for ongoing activities to continue.In such a case, a decision should be taken on which part of the data has to be cached. Often we cannot count on the user’s own judgement of what he/she will need and prefetch. Rather, in our opinion, some sort of automatic prefetching would be desirable. Uninterrupted operation in offline mode will be in high demand and the mobile computer systems should provide support for it. Seamless disconnection can be achieved by loading the files that a user will access in the future from the network to the local storage. This preparation process for disconnected operation is called hoarding.Few of the parameters which complicate the hoarding process are prediction of future access pattern of the user, handling of hoard miss, limited local hoard memory and unpredictable disconnections and reconnection, activities on hoarded object at other clients, the asymmetry of communications bandwidth in downstream and upstream. An important point is to measure the quality of the hoarding and to try to improve it continuously. An often used metric in the evaluation of caching proxies is the hit ratio. Hit ratio is calculated by dividing the number of by the total number of uploaded predictions.It is a good measure for hoarding systems, though a better measure is the miss ratio – a percentage of accesses for which the cache is ineffective. In this work we have given brief overview of the techniques proposed in earlier days and also given the idea for the new hoarding technique. 2. Related Work and Motivation Before the early 1990’s, there was little research on hoarding. Since then, however, interest has increased dramatically among research scientists and professors around the globe and many techniques have been developed. Here we have listed few of the techniques and also will discuss them in brief. Coda: The Pioneering System for Hoarding †¢ Hoarding Based on Data Mining Techniques ? SEER Hoarding System (inspired by clustering technique) ? Association Rule-Based Techniques ? Hoarding Based on Hyper Graph ? Probability Graph Based Technique †¢ Hoarding Techniques Based on Program Trees †¢ Hoarding in a Distributed Environment †¢ Hoarding content for mobile learning †¢ Mobile Clients Through Cooperative Hoarding 2. 1 Coda Coda is a distributed file system based on client–server architecture, where there are many clients and a comparatively smaller number of servers.It is the first system that enabled users to work in disconnected mode. The concept of hoarding was introduced by the Coda group as a means of enabling disconnected operation. Disconnections in Coda are assumed to occur involuntarily due to network failures or voluntarily due to the detachment of a mobile client from the network. Voluntary and involuntary disconnections are handled the same way. The cache manager of Coda, called Venus, is designed to work in disconnected mode by serving client requests from the cache when the mobile client is detached from the network.Requests to the files that are not in the cache during disconnection are reflected to the client as failures. The hoarding system of Coda lets users select the files that they will hopefully need in the future. This information is used to decide what to load to the local storage. For disconnected operation, files are loaded to the client local storage, because the master copies are kept at stationary servers, there is the notion of replication and how to manage locks on the local copies. When the disconnection is voluntary, Coda handles this case by obtaining exclusive locks to files.However in case of involuntary disconnection, the system should defer the conflicting lock requests for an object to the reconnection time, which may not be predictable. The cache management system of Coda, called Venus, diff ers from the previous ones in that it incorporates user profiles in addition to the recent reference history. Each workstation maintains a list of pathnames, called the hoard database. These pathnames specify objects of interest to the user at the workstation that maintains the hoard database. Users can modify the hoard database via scripts, which are called hoard profiles.Multiple hoard profiles can be defined by the same user and a combination of these profiles can be used to modify the hoard database. Venus provides the user with an option to specify two time points during which all file references will be recorded. Due to the limitations of the mobile cache space, users can also specify priorities to provide the hoarding system with hints about the importance of file objects. Precedence is given to high priority objects during hoarding where the priority of an object is a combination of the user specified priority and a parameter indicating how recently it was accessed.Venus per forms a hierarchical cache management, which means that a directory is not purged unless all the subdirectories are already purged. In summary, the Coda hoarding mechanism is based on a least recently used (LRU) policy plus the user specified profiles to update the hoard data-base, which is used for cache management. It relies on user intervention to determine what to hoard in addition to the objects already maintained by the cache management system. In that respect, it can be classified as semi-automated.Researchers developed more advanced techniques with the aim of minimizing the user intervention in determining the set of objects to be hoarded. These techniques will be discussed in the following sections. 2. 2 Hoarding based on Data mining Techniques Knowing the interested pattern from the large collection of data is the basis of data mining. In the earlier history of hoarding related works researchers have applied many different data mining techniques in this arena of mobile hoa rding. Mainly clustering and association rule mining techniques were adopted from data mining domain. . 2. 1 SEER Hoarding System To automate the hoarding process, author developed a hoarding system called SEER that can make hoarding decisions without user intervention. The basic idea in SEER is to organize users’ activities as projects in order to provide more accurate hoarding decisions. A distance measure needs to be defined in order to apply clustering algorithms to group related files. SEER uses the notion of semantic distance based on the file reference behaviour of the files for which semantic distance needs to be calculated.Once the semantic distance between pairs of files are calculated, a standard clustering algorithm is used to partition the files into clusters. The developers of SEER also employ some filters based on the file type and other conventions introduced by the specific file system they assumed. The basic architecture of the SEER predictive hoarding syste m is provided in figure 1. The observer monitors user behaviour (i. e. , which files are accessed at what time) and feeds the cleaned and formatted access paths to the correlator, which then generates the distances among files in terms of user access behaviour.The distances are called the semantic distance and they are fed to the cluster generator that groups the objects with respect to their distances. The aim of clustering is, given a set of objects and a similarity or distance matrix that describes the pairwise distances or similarities among a set of objects, to group the objects that are close to each other or similar to each other. Calculation of the distances between files is done by looking at the high-level file references, such as open or status inquiry, as opposed to individual reads and writes, which are claimed to obscure the process of distance calculation. pic] Figure 1. Architecture of the SEER Predictive Hoarding System The semantic distance between two file referen ces is based on the number of intervening references to other files in between these two file references. This definition is further enhanced by the notion of lifetime semantic distance. Lifetime semantic distance between an open file A and an open file B is the number of intervening file opens (including the open of B). If the file A is closed before B is opened, then the distance is defined to be zero.The lifetime semantic distance relates two references to different files; however it needs to be somehow converted to a distance measure between two files instead of file references. Geometric mean of the file references is calculated to obtain the distance between the two files. Keeping all pairwise distances takes a lot of space. Therefore, only the distances among the closest files are represented (closest is determined by a parameter K, K closest pairs for each file are considered). The developers of SEER used a variation of an agglomerative (i. e. bottom up) clustering algorithm called k nearest neighbour, which has a low time and space complexity. An agglomerative clustering algorithm first considers individual objects as clusters and tries to combine them to form larger clusters until all the objects are grouped into one single cluster. The algorithm they used is based on merging sub clusters into larger clusters if they share at least kn neighbours. If the two files share less than kn close files but more than kf, then the files in the clusters are replicated to form overlapping clusters instead of being merged.SEER works on top of a user level replication system such as Coda and leaves the hoarding process to the underlying file system after providing the hoard database. The files that are in the same project as the file that is currently in use are included to the set of files to be hoarded. During disconnected operation, hoard misses are calculated to give a feedback to the system. 2. 2. 2 Association Rule-Based Techniques Association rule overview: Let I=i1,i2†¦.. im be a set of literals, called items and D be a set of transactions, such that ?T ? D; T? I. A transaction T contains a set of items X if X? T. An association rule is denoted by an implication of the form X ? Y, where X? I, Y ? I, and X ? Y = NULL. A rule X ? Y is said to hold in the transaction set D with confidence c if c% of the transactions in D that contain X also contain Y. The rule X? Y has support sin the transaction set D if s% of transactions in D contains X? Y. The problem of mining association rules is to find all the association rules that have a support and a confidence greater than user-specified thresholds.The thresholds for confidence and support are called minconf and minsup respectively. In Association Rule Based Technique for hoarding, authors described an application independent and generic technique for determining what should be hoarded prior to disconnection. This method utilizes association rules that are extracted by data mining techni ques for determining the set of items that should be hoarded to a mobile computer prior to disconnection. The proposed method was implemented and tested on synthetic data to estimate its effectiveness.The process of automated hoarding via association rules can be summarized as follows: Step 1: Requests of the client in the current session are used through an inferencing mechanism to construct the candidate set prior to disconnection. Step 2: Candidate set is pruned to form the hoard set. Step 3: Hoard set is loaded to the client cache. The need to have separate steps for constructing the candidate set and the hoard set arises from the fact that users also move from one machine to another that may have lower resources.The construction of the hoard set must adapt to such potential changes. Construction of candidate set: An inferencing mechanism is used to construct the candidate set of data items that are of interest to the client to be disconnected. The candidate set of the client is constructed in two steps; 1. The inferencing mechanism finds the association rules whose heads (i. e. , left hand side) match with the client’s requests in the current session, 2. The tails (i. e. , right hand side) of the matching rules are collected into the candidate set.Construction of Hoard set: The client that issued the hoard request has limited re-sources. The storage resource is of particular importance for hoarding since we have a limited space to load the candidate set. Therefore, the candidate set obtained in the first phase of the hoarding set should shrink to the hoard set so that it fits the client cache. Each data item in the candidate set is associated with a priority. These priorities together with various heuristics must be incorporated for determining the hoard set. The data items are used to sort the rules in descending order of priorities.The hoard set is constructed out of the data items with the highest priority in the candidate set just enough to fil l the cache. 3. Hoarding Based on Hyper Graph Hyper graph based approach presents a kind of low-cost automatic data hoarding technology based on rules and hyper graph model. It first uses data mining technology to extract sequence relevance rules of data from the broadcasting history, and then formulates hyper graph model, sorting the data into clusters through hyper graph partitioning methods and sorting them topologically.Finally, according to the data invalid window and the current visit record, data in corresponding clusters will be collected. Hyper graph model: Hyper graph model is defined as H = (V, E) where V={v1 ,v2 ,†¦ ,vn } is the vertices collection of hyper graph, and E={e1 ,e2 ,†¦ ,em } is super-edge collection of hyper graph (there supposed to be m super-edges in total). Hyper graph is an extension of graph, in which each super-edge can be connected with two or more vertices. Super-edge is the collection of a group of vertices in hyper graph, and superedge ei = {vi1, vi2, †¦ inj} in which vi1,vi2 ,†¦ ,vin ? V . In this model, vertices collection V corresponds to the history of broadcast data, in which each point corresponds to a broadcast data item, and each super-edge corresponds to a sequence model. Sequence model shows the orders of data items. A sequence model in size K can be expressed as p = . Use of hyper graph in hoarding are discussed in paper in details. 4. Probability Graph Based Technique This paper proposed a low-cost automated hoarding for mobile computing.Advantage of this approach is it does not explore application specific heuristics, such as the directory structure or file extension. The property of application independence makes this algorithm applicable to any predicative caching system to address data hoarding. The most distinguished feature of this algorithm is that it uses probability graph to represent data relationships and to update it at the same time when user’s request is processed. Before d isconnection, the cluster algorithm divides data into groups.Then, those groups with the highest priority are selected into hoard set until the cache is filled up. Analysis shows that the overhead of this algorithm is much lower than previous algorithms. Probability Graph: An important parameter used to construct probability graph is look-ahead period. It is a fixed number of file references that defines what it means for one file to be opened ‘soon’ after another. In other words, for a specific file reference, only references within the look-ahead period are considered related. In fact, look-ahead period is an approximate method to avoid traversing the whole trace.Unlike constructing probability graph from local file systems, in the context of mobile data access, data set is dynamically collected from remote data requests. Thus, we implemented a variation of algorithm used to construct probability graph, as illustrated in Figure 2. [pic] Figure 2. Constructing the prob ability graph The basic idea is simple: If a reference to data object A follows the reference to data object B within the look-ahead period, then the weight of directed arc from B to A is added by one. The look-ahead period affects absolute weight of arcs.Larger look-ahead period produces more arcs and larger weight. A ’s dependency to B is represented by the ratio of weight of arc from B to A divided by the total weight of arcs leaving B. Clustering: Before constructing the final hoard set, data objects are clustered into groups based on dependency among data objects. The main objective of the clustering phase is to guarantee closely related data objects are partitioned into the same group. In the successive selecting phase, data objects are selected into hoard set at the unit of group. This design provides more continuity in user operation when disconnected.Selecting Groups: The following four kinds of heuristic information are applicable for calculating priority for a grou p: †¢ Total access time of all data objects; †¢ Average access time of data objects; †¢ Access time of the start data object; †¢ Average access time per byte. 2. Hoarding Techniques Based on Program Trees A hoarding tool based on program execution trees was developed by author running under OS/2 operating system. Their method is based on analyzing program executions to construct a profile for each program depending on the files the program accesses.They proposed a solution to the hoarding problem in case of informed disconnections: the user tells the mobile computer that there is an imminent disconnection to fill the cache intelligently so that the files that will be used in the future are already there in the cache when needed. [pic] Figure 3. Sample program Tree This hoarding mechanism lets the user make the hoarding decision. They present the hoarding options to the user through a graphical user interface and working sets of applications are captured automatic ally. The working sets are detected by logging the user file accesses at the background.During hoarding, this log is analyzed and trees that represent the program executions are constructed. A node denotes a file and a link from a parent to one of its child nodes tells us that either the child is opened by the parent or it is executed by the parent. Roots of the trees are the initial processes. Program trees are constructed for each execution of a program, which captures multiple contexts of executions of the same program. This has the advantage that the whole context is captured from different execution times of the program.Finally, hoarding is performed by taking the union of all the execution trees of a running program. A sample program tree is provided in Figure 3. Due to the storage limitations of mobile computers, the number of trees that can be stored for a program is limited to 15 LRU program trees. Hoarding through program trees can be thought of as a generalization of a pr o-gram execution by looking at the past behaviour. The hoarding mechanism is enhanced by letting the user rule out the data files. Data files are automatically detected using three complementary heuristics: 1.Looking at the filename extensions and observing the filename conventions in OS/2, files can be distinguished as executable, batch files, or data files. 2. Directory inferencing is used as a spatial locality heuristic. The files that differ in the top level directory in their pathnames from the running program are assumed to be data files, but the programs in the same top level directory are assumed to be part of the same program. 3. Modification times of the files are used as the final heuristic to deter-mine the type of a file. Data files are assumed to be modified more recently and frequently than the executables.They devised a parametric model for evaluation, which is based on recency and frequency. 3. Hoarding in a Distributed Environment Another hoarding mechanism, which was presented for specific application in distributed system, assumes a specific architecture, such as infostations where mobile users are connected to the network via wireless local area networks (LANs) that offer a high bandwidth, which is a cheaper option compared to wireless wide area networks (WANs). The hoarding process is handed over to the infostations in that model and it is assumed that what the user wants to access is location-dependent.Hoarding is proposed to fill the gap between the capacity and cost trade-off between wireless WANS and wireless LANs. The infestations do the hoarding and when a request is not found in the infostation, then WAN will be used to get the data item. The hoarding decision is based on the user access patterns coupled with that user’s location information. Items frequently accessed by mobile users are recorded together with spatial information (i. e. , where they were accessed). A region is divided into hoarding areas and each infostation is responsible with one hoarding area. 4. Hoarding content for mobile learningHoarding in the learning context is the process for automatically choosing what part of the overall learning content should be prepared and made available for the next offline period of a learner equipped with a mobile device. We can split the hoarding process into few steps that we will discuss further in more details: 1. Predict the entry point of the current user for his/her next offline learning session. We call it the ‘starting point’. 2. Create a ‘candidate for caching’ set. This set should contain related documents (objects) that the user might access from the starting point we have selected. 3.Prune the set – the objects that probably will not be needed by the user should be excluded from the candidate set, thus making it smaller. This should be done based on user behaviour observations and domain knowledge. 4. Find the priority to all objects still in the hoarding set after pruning. Using all the knowledge available about the user and the current learning domain, every object left in the hoarding set should be assigned a priority value. The priority should mean how important the object is for the next user session and should be higher if we suppose that there is a higher probability that an object will be used sooner. . Sort the objects based on their priority, and produce an ordered list of objects. 6. Cache, starting from the beginning of the list (thus putting in the device cache those objects with higher priority) and continue with the ones with smaller weights until available memory is filled in. 5. Mobile Clients Through Cooperative Hoarding Recent research has shown that mobile users often move in groups. Cooperative hoarding takes advantage of the fact that even when disconnected from the network, clients may still be able to communicate with each other in ad-hoc mode.By performing hoarding cooperatively, clients can share their hoar d content during disconnections to achieve higher data accessibility and reduce the risk of critical cache misses. Two cooperative hoarding schemes, GGH and CAP, have been proposed. GGH improves hoard performance by al-lowing clients to take advantage of what their peers have hoarded when making their own hoarding decisions. On the other hand, CAP selects the best client in the group to Hoard each object to maximise the number of unique objects hoarded and minimise access cost. Simulation results show that compare to existing schemes.Details of GGH and CAP are given in paper. 2. 7 Comparative Discussion previous techniques The hoarding techniques discussed above vary depending on the target system and it is difficult to make an objective comparative evaluation of their effectiveness. We can classify the hoarding techniques as being auto-mated or not. In that respect, being the initial hoarding system, Coda is semiautomated and it needs human intervention for the hoarding decision. T he rest of the hoarding techniques discussed are fully automated; how-ever, user supervision is always desirable to give a final touch to the files to be hoarded.Among the automated hoarding techniques, SEER and program tree-based ones assume a specific operating system and use semantic information about the files, such as the naming conventions, or file reference types and so on to construct the hoard set. However, the ones based on association rule mining and infostation environment do not make any operating system specific assumptions. Therefore, they can be used in generic systems. Coda handles both voluntary and involuntary disconnections well.The infostation-based hoarding approach is also inherently designed for involuntary disconnections, because hoarding is done during the user passing in the range of the infostation area. However, the time of disconnection can be predicted with a certain error bound by considering the direction and the speed of the moving client predicting when the user will go out of range. The program tree-based methods are specifically designed for previously informed disconnections. The scenario assumed in the case of infostations is a distributed wire-less infrastructure, which makes it unique among the hoarding mechanisms.This case is especially important in today’s world where peer-to-peer systems are becoming more and more popular. 3. Problem Definition The New Technique that we have planned to design for hoarding will be used on Mobile Network. Goals that we have set are a. Finding a solution having optimal hit ratio in the hoard at local node. b. Technique should not have greater time complexity because we don’t have much time for performing hoarding operation after the knowledge of disconnection. c. Optimal utilization of hoard memory. d. Support for both intentional and unintentional disconnection. e.Proper handling of conflicts in hoarded objects upon reconnection. However, our priority will be for hit rati o than the other goals that we have set. We will take certain assumptions about for other issues if we find any scope of improvement in hit ratio. 4. New Approach 4. 1 Zipf’s Law It is a mathematical tool to describe the relationship between words in a text and their frequencies. Considering a long text and assigning ranks to all words by the frequencies in this text, the occurrence probability P (i) of the word with rank i satisfies the formula below, which is known as Zipf first law, where C is a constant.P (i) = [pic] †¦. (1) This formula is further extended into a more generalized form, known as Zipf-like law. P (i) = [pic]†¦. (2) Obviously, [pic]†¦. (3) Now According to (2) and (3), we have C[pic] [pic] Our work is to dynamically calculate for different streams and then according to above Formula (2) and (4), the hotspot can be predicted based on the ranking of an object. 4. 2 Object Hotspot Prediction Model 4. 2. 1 Hotspot Classification We classify hotsp ot into two categories: â€Å"permanent hotspot† and â€Å"stage hotspot†. Permanent hotspot is an object which is frequently accessed regularly.Stage hotspot can be further divided into two types: â€Å"cyclical hotspot† and â€Å"sudden hotspot†. Cyclical hotspot is an object which becomes popular periodically. If an object is considered as a focus suddenly, it is a sudden hotspot. 4. 2. 2. Hotspot Identification Hotspots in distributed stream-processing storage systems can be identified via a ranking policy (sorted by access frequencies of objects). In our design, the hotspot objects will be inserted into a hotspot queue. The maximum queue length is determined by the cache size and the average size of hotspot Objects.If an object’s rank is smaller than the maximum hotspot queue length (in this case, the rank is high), it will be considered as â€Å"hotspot† in our system. Otherwise it will be considered as â€Å"non hotspot†. And t he objects in the queue will be handled by hotspot cache strategy. 4. 2. 3 Hotspot Prediction This is our main section of interest, here we will try to determine the prediction model for hoard content with optimal hoard hit ratio. 5. Schedule of Work |Work |Scheduled Period |Remarks | |Studying revious work on Hoarding |July – Aug 2012 |Complete | |Identifying Problem |Sept 2012 |Complete | |Innovating New Approach |Oct 2012 |Ongoing | |Integrating with Mobile Arena as solution to Hoarding |Nov- Dec 2012 |- | |Simulation And Testing |Jan 2013 |- | |Optimization |Feb 2013 |- | |Simulation And Testing |Mar 2013 |- | |Writing Thesis Work / Journal Publication |Apr –May 2013 |- | 6. Conclusion In this literature survey we have discussed previous related work on hoarding. We have also given the requirements for the new technique that is planned to be design.Also we are suggesting a new approach that is coming under the category of Hoarding with Data Mining Techniques. Recen t studies have shown that the use of proposed technique i. e. Zipfs-Like law for caching over the web contents have improved the hit ratio to a greater extent. Here with this work we are expecting improvements in hit ratio of the local hoard. References [1]. James J. Kistler and Mahadev Satyanarayanan. Disconnected Operation in the Coda File System. ACM Transactions on Computer Systems, vol. 10, no. 1, pp. 3–25, 1992. [2]. Mahadev Satyanarayanan. The Evolution of Coda. ACM Transactions on Computer Systems, vol. 20, no. 2, pp. 85–124, 2002 [3]. Geoffrey H. Kuenning and Gerald J. Popek. Automated Hoarding for Mobile Computers.In Proceedings of the 16th ACM Symposium on Operating System Principles (SOSP 1997), October 5–8, St. Malo, France, pp. 264–275, 1997. [4]. Yucel Saygin, Ozgur Ulusoy, and Ahmed K. Elmagarmid. Association Rules for Supporting Hoarding in Mobile Computing Environments. In Proceedings of the 10th IEEE Workshop on Research Issues in Data Engineering (RIDE 2000), February 28–29, San Diego, pp. 71–78, 2000. [5]. Rakesh Agrawal and Ramakrishna Srikant, Fast Algorithms for Mining Association Rules. In Proceedings of the 20th International Conference on Very Large Databases, Chile, 1994. [6]. GUO Peng, Hu Hui, Liu Cheng. The Research of Automatic Data Hoarding Technique Based on Hyper Graph.Information Science and Engineering (ICISE), 1st International Conference, 2009. [7]. Huan Zhou, Yulin Feng, Jing Li. Probability graph based data hoarding for mobile environment. Presented at Information & Software Technology, pp. 35-41, 2003. [8]. Carl Tait, Hui Lei, Swarup Acharya, and Henry Chang. Intelligent File Hoarding for Mobile Computers. In Proceedings of the 1st Annual International Conference on Mobile Computing and Networking (MOBICOM’95), Berkeley, CA, 1995. [9]. Anna Trifonova and Marco Ronchetti. Hoarding content for mobile learning. Journal International Journal of Mobile Communications archive V olume 4 Issue 4, Pages 459-476, 2006. [10]. Kwong Yuen Lai, Zahir Tari, Peter Bertok.Improving Data Accessibility for Mobile Clients through Cooperative Hoarding. Data Engineering, ICDE proceedings 21st international Conference 2005. [11]. G. Zipf, Human Behavior and the Principle of Least Effort. Addison-Wesley, 1949. [12]. Chentao Wu, Xubin He, Shenggang Wan, Qiang Cao and Changsheng Xie. Hotspot Prediction and Cache in Distributed Stream-processing Storage Systems. Performance Computing and Communications Conference (IPCCC) IEEE 28th International, 2009. [13]. Lei Shi, Zhimin Gu, Lin Wei and Yun Shi. An Applicative Study of Zipf’s Law on Web Cache International Journal of Information Technology Vol. 12 No. 4 2006. [14]. Web link: http://en. wikipedia. org/wiki/Zipf%27s_law

Mortgage bonds a bond secured by a mortgage - Free Essay Example

Sample details Pages: 14 Words: 4216 Downloads: 4 Date added: 2017/06/26 Category Finance Essay Type Analytical essay Did you like this example? A mortgage bond is a bond secured by a mortgage on one or more assets.  These bonds are typically backed by real estate holdings and/or real property such as equipment. In a default situation, mortgage bondholders have a claim  to the underlying property and could sell it off to compensate for the default. Mortgage bonds offer the investor a great deal of protection in that the principal is secured by a valuable asset that could theoretically be sold off to cover the debt. Don’t waste time! Our writers will create an original "Mortgage bonds a bond secured by a mortgage" essay for you Create order However, because of this inherent safety, the average mortgage bond tends to yield a lower rate of return than traditional corporate bonds that are backed only by the corporations promise and ability to pay. Definition A  mortgage bond  is a  bond  backed by a pool of  mortgages  on a  real estate  asset such as a  house. More generally, bonds which are secured by the pledge of specific assets are called mortgage bonds. Illustrative summary An investor purchases a bond from a financial institution for a fixed amount of money. The financial institution then promises to give the money back years from that day with a small percentage of interest added to the original value. When a person purchases a house, he or she generally must borrow money from a bank or  mortgage  lending company. To borrow this money, the person must sign a  promissory note  stating he or she will pay back the value of the loan, plus a percentage of interest, which is accrued each month. Usually, a  mortgage payment  spans fifteen to thirty years and is paid back in monthly installations. To issues these loans, the mortgage lending company may need to borrow a large sum of cash from a larger financial institution. The mortgage  lender  offers a number of mortgage agreements in one lump-sum package to a financial institution, which issues a mortgage bond in return. With a mortgage bond, t he larger financial institution purchases the mortgage agreement from the mortgage lender and receives the borrowers monthly payment in exchange. The mortgage bond process helps the mortgage lender get the money it needs, while the larger financial institution earns extra money by receiving the monthly payment from the borrower. If the borrower defaults on the  mortgage loan, the loss is passed on to the financial institution that issued the mortgage bond. To regain the money lost from the mortgage bond, the financial institution that issued the mortgage bond can resell the house. This can still result in a loss of money if the mortgage bond is worth more than the home. Related concepts Consolidated Mortgage Bond A bond that consolidates the issues of multiple properties. If the properties covered by the consolidated mortgage bond are already mortgaged, the bond acts as a new mortgage. If the properties do not have outstanding mortgages then the bond is considered the first lien. It can be used as a way to refinance the mortgages on the individual properties. The bond is backed by real estate or physical capital. Consolidated mortgage bonds are used by large companies with many properties, such as railroads, looking to refinance them into one bond to market to investors. It allows companies to set a single coupon rate instead of dealing with several, and makes investors happy because they can purchase a singular bond that covers physical assets of a similar type. Mortgage Subsidy Bond One of the few types of municipal bonds ever issued that may be subject to taxation, provided that the funds raised were used for home mortgages. Mortgage subsidy bonds were issued by cities and other municipalities, and may be either taxable or tax-free. Mortgage subsidy bonds were created by the Mortgage Subsidy Act of 1980. They are issued by either state or local governments and are usually taxable. The  exceptions are a select group of mortgage bonds and veterans bonds. Conclusion In most cases, a mortgage bond is a win-win situation for both financial institutions. The recent increase in the value of homes, however, has caused some difficulty with the mortgage bond arrangement. Because homes were increasing in value, mortgage  lenders  issued loans to people who were not the ideal candidates. As such homeowners default on more loans, and the value of housing levels out, the mortgage bond may be worth more than the value of the house. Debentures Introduction Debenture is a type of fixed-interest  security, issued by companies (as borrowers) in  return  for medium and long-term investment of  funds. A debenture is evidence of the borrowers  debt  to the lender. The word derives from the Latin debeo, meaning I owe. Debentures are issued to the general public through a  prospectus  and are secured by a  trust deed  which spells out the terms and conditions of the fundraising and the rights of the debenture-holders. Typical issuers of debentures are finance companies and large industrial companies. Debenture-holders funds are invested with the borrowing  company  as secured loans, with the security usually in the form of a fixed or  floating charge  over the  assets  of the borrowing company. As secured lenders, debenture-holders  claims  to the companys assets rank ahead of those of ordinary shareholders, should th e company be wound up; also, interest is payable on debentures whether the company makes a  profit  or not. Debentures are issued for fixed periods but if a debenture-holder wants to get his or her  money  back, the securities  can be sold. Definition In the  United States, debenture refers specifically to an  unsecured  corporate bond,  i.e. a bond that does not have a certain line of income or piece of property or equipment to guarantee repayment of  principal  upon the bonds  maturity. Where security is provided for loan stocks or bonds in the US, they are termed mortgage bonds. However, in the  United Kingdom  a debenture is usually secured. In Asia, if repayment is secured by a charge over land, the loan document is called a  mortgage; where repayment is secured by a charge against other assets of the company, the document is called a debenture; and where no security is involved, the document is called a note or unsecured deposit note. A  type of debt instrument that is not secured by physical asset or collateral.  Debentures are backed only by the general  creditworthiness  and  reputation of the issuer.  Both corporations and governments frequently issue this type of bond in order to secure capital.  Like other types of bonds, debentures are documented in an indenture. In law, a  debenture  is a document that either creates a debt or acknowledges it. In  corporate finance, the term is used for a medium- to long-term debt instrument  used by large companies to borrow money. In some countries the term is used interchangeably with  bond,  loan stock  or  note. Illustrative summary Debentures have no collateral.  Bond buyers generally  purchase debentures based on  the belief that the bond issuer is unlikely to default on the repayment.  An example of a government  debenture would be any government-issued  Treasury bond (T-bond) or Treasury bill (T-bill). T-bonds and T-bills  are generally considered risk  free because governments, at worst,  can  print off more money or raise taxes to pay  these types of debts. A  Debenture is a long-term Debt Instrument issued by governments and big institutions for the purpose of raising funds. The Debenture has some similarities with  Bonds  but the terms and conditions of securitization of Debentures are different from that of a Bond. A Debenture is regarded as an unsecured investment  because there are no pledges (guarantee) or liens available on particular assets. Nonetheless, a Debenture is backed by all theƚ  assets which have not been pledged otherwise. Normally, Debentures are referred to as freely negotiable Debt Instruments. The Debenture holder functions as a lender to the issuer of the Debenture. In return, a specific  rate  of interest is paid to the Debenture holder by the Debenture issuer similar to the case of a  loan. In practice, the differentiation between a Debenture and a Bond is not observed every time. In some cases, Bonds are also termed as Debentures and vice-versa. If a  bankruptcy  occurs, Debenture holders are treated as general creditors.  Ãƒâ€š Conclusion The English term debenture has two meanings: 1: a certificate or voucher acknowledging a debt; 2: the ability of a customer to obtain goods or services before payment, based on the trust that payment will be made in the future. The Debenture issuer has a substantial advantage from issuing a Debenture because the particular assets are kept without any encumbrances so that the option is open for issuing them in future for financing  purposes. Subordinated debentures Introduction An unsecured bond with a claim to assets that is subordinate to all existing and future debt. Thus, in the event that the issuer encounters financial difficulties and must be liquidated, all other claims must be satisfied before holders of subordinated debentures can receive a settlement. Frequently, this settlement amounts to relatively little. Because of the risk involved, the issuers have to pay relatively high interest rates in order to sell these securities to investors. Many issues of these debentures include a sweetener such as the right to exchange the securities for shares of common stock. The sweeteners are included so that interest rates on the subordinated debentures can be reduced below the level that would be required without them. Subordinated debentures without the conversion option appeal to risk-oriented investors seeking high current yields. Subordinated debenture  has a lower priority than other bonds of the issuer in case of liquidation during  bankruptcy, below the liquidator, government  tax  authorities and senior debt holders in the hierarchy of creditors. Definition   Subordinated debt  (also known as  subordinated loan,  subordinated bond,  subordinated debenture  or  junior debt) is debt which ranks after other debts should a company fall into  receivership  or be closed. Such debt is referred to as subordinate, because the debt providers (the lenders) have subordinate status in relationship to the normal debt. A typical example for this would be when a promoter of a company invests money in the form of debt, rather than in the form of stock. In the case of liquidation (e.g. the company winds up its affairs and dissolves) the promoter would be paid just before stockholders assuming there are assets to distribute after all other liabilities and debts have been paid. Explanation Subordinated debt has a lower priority than other bonds of the issuer in case of  liquidation  during  bankruptcy, below the  liquidator, government tax authorities and  senior debt holders  in the hierarchy of creditors. Because subordinated debt is repayable after other debts have been paid, they are more risky for the lender of the money. It is unsecured and has lesser priority than that of an additional debt claim on the same asset. Subordinated loans typically have a higher  rate of return  than senior debt due to the increased inherent risk. Accordingly, major  shareholders  and  parent companies  are most likely to provide subordinated loans, as an outside party providing such a loan would normally want compensation for the extra risk. Subordinated bonds usually have a lower credit rating than senior bonds. Subordinate debenture and stocks. When somebody decides to invest in stocks, he or she becomes one of the owners and thus, becomes a shareholder of the good and bad times of the company. The investor faces uncertain fortunes related to the companys  financial  graph. So this explains the amount of risk related to stock-investments. But debentures are more secured investment, as payments with high interest rates are guaranteed. The company is bound to pay interest on the borrowed money, and once the debenture matures, all the borrowed  money  is returned. In other words, the investors gain interest as income from the debentures. Subordinated debenture and bonds. Subordinated debenture and bonds are similar, but  bonds  carry more security than debentures. In both of these investment forms, interest and value is guaranteed, but in case of liquidation, bond holders receive the payment first, followed by the senior bonds, and only after that comes the subordinated debenture holders, who have no collateral which they can claim from the company in case bankruptcy takes place. To compensate for the possibility of such losses,  high interest rates  are paid to the subordinated debenture holders. Examples A particularly important example of subordinated bonds can be found in bonds issued by banks. Subordinated debt is issued periodically by most large banking corporations in the U.S. Subordinated debt can be expected to be especially  risk-sensitive, because subordinated debt holders have claims on bank assets after senior debt holders and they lack the upside gain enjoyed by shareholders. This status of subordinated debt makes it perfect for experimenting with the significance of  market discipline, via the signaling effect of secondary market prices of subordinated debt (and, where relevant, the issue price of these bonds initially in the primary markets). From the perspective of policy-makers and regulators, the potential benefit from having banks issue subordinated debt is that the markets and their information-generating capabilities are enrolled in the supervision of the financial condition of the banks. This hopefully creates both an early-warning system, like t he so-called canary in the mine, and also an incentive for bank management to act prudently, thus helping to offset the  moral hazard  that can otherwise exist, especially if banks have limited equity and deposits are insured. This role of subordinated debt has attracted increasing attention from policy analysts in recent years. For a second example of subordinated debt, consider asset-backed securities. These are often issued in  tranches. The senior tranches get paid back first, the subordinated tranches later. Finally,  mezzanine debt  is another example of subordinated debt. Conclusion Because subordinated debenture is repayable after other debts have been paid, they are more risky for the lender of the money. It is unsecured and has lesser priority than that of an additional debt claim on the same  asset. Subordinated bonds are regularly issued (as mentioned earlier) as part of the securitization of debt, such as  asset-backed securities,  collateralized mortgage obligations  or  collateralized debt obligations. Corporate issuers tend to prefer not to issue subordinated bonds because of the higher interest rate required to compensate for the higher risk, but may be forced to do so if indentures on earlier issues mandate their status as senior bonds. Also, subordinated debt may be combined with  preferred stock  to create so called  monthly income preferred stock, a  hybrid security  paying dividends for the lender and funded as interest expense by the issuer. Investment-grade bonds Introduction A  bond  is considered  investment grade  or  IG  if its credit rating is BBB- or higher by  Standard HYPERLINK https://en.wikipedia.org/wiki/Standard__PoorsHYPERLINK https://en.wikipedia.org/wiki/Standard__Poors Poors  or Baa3 or higher by  Moodys  or BBB (low) or higher by  DBRS. Generally they are bonds that are judged by the rating agency as likely enough to meet payment obligations that banks are allowed to invest in them. Ratings play a critical role in determining how many companies and other entities that issue debt, including sovereign governments; have to pay to access credit markets, i.e., the amount of interest they pay on their issued debt. The threshold between investment-grade and speculative-grade ratings has important market implications for issuers borrowing costs. The risks associated with investment-grade bonds (or investment-grade  corporate debt) are considered noticeably higher than in the case of first-class government bonds. The difference between rates for first-class government bonds and investment-grade bonds is called investment-grade spread. It is an indicator for the markets belief in the stability of the economy. The higher these investment-grade spreads (or  risk premiums) are, the weaker the economy is considered. Until the early 1970s, bond credit ratings agencies were paid for their work by investors who wanted impartial information on the credit worthiness of securities issuers and their particular offerings. Starting in the early 1970s, the Big Three ratings agencies (SP, Moodys, and Fitch) began to receive payment for their work by the securities issuers for whom they issue those ratings, which has led to charges that these ratings agencies can no longer always be impartial when issuing ratings for those securities issuers. Securities issuers have been accused of shopping for the best ratings from these three ratings agenc ies, in order to attract investors, until at least one of the agencies delivers favorable ratings. This arrangement has been cited as one of the primary causes of the  subprime mortgage crisis  (which began in 2007), when some securities, particularly  mortgage backed securities  (MBSs) and collateralized  (CDOs) rated highly by the credit ratings agencies, and thus heavily invested in by many organizations and individuals, were rapidly and vastly devalued due to defaults, and fear of defaults, on some of the individual components of those securities, such as home loans and credit card accounts. Definition Investment grade bonds  are bonds which are rated BBB- or higher by SP and Fitch or Baa3 or higher by Moodys. These ratings are indicators of  default risk  on a particular bond issue with higher rating suggesting lower risk. Bonds which fall below the investment grade threshold are known as  speculative bonds  (also known as  high yield bonds,  non-investment grade  bonds or  junk bonds) The following table lists the ratings which would qualify an issue as  investment grade. Description Moodys SP Fitch Maximum Safety Aaa AAA AAA High grade Aa1 AA+ AA+ High grade Aa2 AA AA High grade Aa3 AA- AA- Higher medium Grade A1 A+ A+ Higher medium Grade A2 A A Higher medium Grade A3 A- A- Lower medium Grade Baa1 BBB+ BBB+ Lower medium Grade Baa2 BBB BBB Lower medium Grade Baa3 BBB- BBB- In vestment grade bonds  are the investment vehicle of choice for many individual and institutional investors. Understanding what an investment grade bond is and what its benefits and risks are will help you make smart choices. Explanation Bonds are rated as to their creditworthiness by the investment ratings agencies, the two primaries of which are Standard Poors and Moodys. Investment grade bonds must be rated BBB- or Baa3, respectively, or higher by these rating agencies. The highest ratings for investment grade bonds are AAA by Standard Poors and Aaa by Moodys. Even the highest-rated investment grade bonds are considered riskier than government-issued bonds. If you take the rate on an investment grade bond and on a government bond, the difference or spread between them is considered a measure of the economys general stability. The lower the spread, the more stable the market views the economy. Conclusion These ratings are important because corporations use bonds as one method of raising funds. Investment grade bonds are considered reliably certain enough to be repaid that banks can invest in them. For this reason, a bond issuer will strive for the highest rating it can get. And, clearly, for the same reason the objectivity and trustworthiness of the ratings agencies is paramount. Junk bonds Introduction High Yield Bonds,  often referred to as junk bonds, are bonds that carry a high risk of default and, as a result, offer a higher yield than investment grade bonds. A high yield bond is classified as having a  credit rating  of BB+ or lower, while bonds with rating of BBB or higher are known as investment grade.  Debt  instruments are the converse of  equity instruments, or stocks, and generally perform better than equities during  economic downturns. This generality holds because debt holders have the first claim on a companys assets. In recessionary periods when cash flows are tight, the companies are required to pay their bond holders before their shareholders receive anything. Junk bonds are the ones that usually pay a high yield because their credit ratings arent stellar. Therefore, in order to borrow money from outside investors, they must pay a higher interest rate in order to attract people to lend them money. This higher i nterest rate reflects the higher chance of default by the company. Bonds rated BBBÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã¢â€ž ¢ and higher are called  investment grade  bonds. Bonds rated lower than investment grade on their date of issue are called speculative grade bonds, derisively referred to as junk bonds. The lower-rated debt typically offers a higher yield, making speculative bonds attractive investment vehicles for certain types of  financial portfolios  and strategies. Many  pension funds  and other investors (banks, insurance companies), however, are prohibited in their  by-laws  from investing in bonds which have ratings below a particular level. As a result, the lower-rated securities have a different investor base than investment-grade bonds. The value of speculative bonds is affected to a higher degree than  investment grade bonds  by the possibility of  default. For example, in a  re cession  interest rates may drop, and the drop in interest rates tends to increase the value of investment grade bonds; however, a recession tends to increase the possibility of default in speculative-grade bonds. Definition A high-risk, high-yield debt security that, if rated at all, is graded less than BBB by Standard Poors or BBB3 by Moodys. These securities are most appropriate for risk-oriented investors. Also called  high-yield bond. Explanation High yield bonds can be bought individually through a broker or in bulk through mutual funds. A high yield mutual fund is a better choice for individual investors because it reduces  risk. This is because the risk is spread over a larger number of contracts, which is known as  diversifying  your credit risk of high yield bonds. That is, while any single bond within the fund may have a relatively high probability of default, when many are grouped together the risk that all, or even most, of the bonds defaulting is much lower. In fact, historically the average rate of  default  between 1971 and 2008 was 3.18%, and even when a high yield bond defaults, bond holders are able to recover on average 44 cents on the dollar.[1]  Therefore, even when high yield bonds default, the investor often does not lose the entire  principal. There are other considerations to take into account besides simply the yield and credit risk. There are two way s high yield bonds enter the market. The first are high yield bonds that are issued by corporations whose credit rating is below investment grade at the time of issue. Because the debt that is being issued is backed by corporations that may a higher chance of being unable to repay, their debt is considered below investment grade and therefore they must pay a higher interest rate. The second way are bonds issued by corporations that were investment grade at the time of issue, but whose credit rating fell below investment grade. For example, suppose Company X currently has a credit rating of AA (investment grade), and issues bonds that expire in 10 years. Two years later, Company Xs performance has fallen off considerably, and its credit rating is now BB+, meaning it is now below investment grade. Therefore, even though the bonds were initially investment grade bonds, it can still fall below investment grade and turn into a high yield bond. These are often referred to as fallen stars. When investment grade companies credit ratings drop to below investment grade, the bond now not only has a higher risk of default, but the price of the bond will fall as well. Therefore, if you plan to sell the bond before maturity, your  holding period return  will suffer with drops in credit ratings. Conversely, if you purchase a high yield bond, and the companys credit rating improves to investment grade, the value of your bond will increase significantly. An investor can view the interest payments as analogous to  dividend  payments made by stocks while changes in credit ratings are somewhat analogous to changes in the bond price. Conclusion The holder of any debt is subject to  interest rate risk  and  credit risk, inflationary risk, currency risk, duration risk,  convexity risk, repayment of principal risk, streaming income risk,  liquidity risk, default risk, maturity risk, reinvestment risk, market risk, political risk, and taxation adjustment risk. Interest rate risk refers to the risk of the market value of a bond changing in value due to changes in the structure or level of interest rates or credit spreads or risk premiums. The credit risk of a high-yield bond refers to the probability and probable loss upon a credit event (i.e., the obligor defaults on scheduled payments or files for bankruptcy, or the bond is restructured), or a credit quality change is issued by a rating agency including Fitch, Moodys, or Standard Poors. A  credit rating agency  attempts to describe the risk with a  credit rating  such as AAA. In  North America, the fiv e major agencies are  Standard and Poors,Moodys,  Fitch Ratings,  Dominion Bond Rating Service  and  A.M. Best. Bonds in other countries may be rated by US rating agencies or by local credit rating agencies. Rating scales vary; the most popular scale uses (in order of increasing risk) ratings of AAA, AA, A, BBB, BB, B, CCC, CC, C, with the additional rating D for debt already in  arrears.  Government bonds  and bonds issued by  government HYPERLINK https://en.wikipedia.org/wiki/Government_sponsored_enterprisesponsored enterprises  (GSEs) are often considered to be in a zero-risk category above AAA; and categories like AA and A may sometimes be split into finer subdivisions like AAÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã¢â€ž ¢ or AA+.