Hedy Lamarr

Presently I wish to discuss hindrances that ladies novices face in working in science and innovation. Lamarr is an incredible case of how a beginner can both survive and be halted by hindrances. Only a little foundation information, Hedy Lamarr was an Austrian â€American entertainer who was additionally known to be the most lovely lady in Hollywood in her time. You may figure what does she have to do with what have been said before this. Indeed, she was the minds who fundamentally began everything, the spread-range innovation which empowered Wi-Fi and cell systems to be accessible to us today!Lamarr thought about a genuine issue. It was during World War 2 when she figured, by what method can one securely control a torpedo with a radio sign? This was significant, since torpedoes were not extremely exact and the capacity to remotely control them could be massively important. The trouble in utilizing a radio sign to control a torpedo is basically the issue of sticking. On the off cha nce that you attempted to control your torpedo by a sign, in the end the adversary will discover the recurrence you are using.Once this is realized they could stick your control signal by putting out a solid commotion signal on the given recurrence. Lamarr had an answer. Her splendid thought was to utilize recurrence hoppingâ€her innovation. Lamarr likewise found a co-creator, George Antheil, who was additionally a cutting edge author, who spread out a framework dependent on 88 frequencies, comparing to the quantity of keys on a piano, utilizing punctured paper moves which would turn in a state of harmony with each other, transmitting and accepting ever-evolving frequencies, forestalling interference and jamming.They then presented the recurrence jumping gadget to the national innovators chamber where they proceeded to document a patent application. Shockingly, she didn't prevail to discharge this plan to help during the war. There were different needs looked by the US military, alongside the diminishing number of assets that were being utilized to make different types of gear and nuclear bombs. Likewise, she isnt your typical innovator. Who might accept a Hollywood entertainer could help create something valuable for the war? Moreover, she was in front of technology.Spread-range requires a genuinely incredible advanced computational capacity. The innovation that was accessible in 1940? s was rough, and all things considered, it was basically difficult to make her thoughts work. Be that as it may, twenty years after its conceptualization, during the Cuban rocket emergency, the principal occurrence of enormous scope military arrangement of Lamarr and Antheil's recurrence bouncing innovation was implemented†not for the remote-controlled direction of torpedoes, however to give secure interchanges among the boats associated with the maritime blockade.Lamarr’s splendid thought is utilized today in remote correspondence. Not actually as she imagined i n her unique licensed work, however all things considered in manners that are unmistakably recognizable to her thoughts. Lamarr in the long run got the acknowledgment she merited however 3 years before her passing. She and her co-innovator Antheil won the 1997 Electronic Frontier Foundation Pioneer Award. She likewise won the BULBIE that is known as the â€Å"Oscar† of creating. Hedy Lamarr had demonstrated to being something other than a â€Å"pretty face†. My assets: http://rjlipton. wordpress. com/2010/07/25/hedy-lamarr-the-innovator/

A Job Well Done Essay Example | Topics and Well Written Essays - 500 words

A Job Well Done - Essay Example A Job Well Done She didn't advise me to do either; she simply let me get things done all alone. Perhaps the hardest activity was getting up right on time to plan breakfast. My more youthful kin needed to manage with oats and milk for breakfast since I am not all that open to working at the kitchen. I carried my mom something to eat in her room, a sandwich and some warm milk, returned to the kitchen since I additionally needed to clean the table a while later and put everything in the dishwasher. That time I had an inclination that it was the longest morning in my life. I became acclimated to leaving my room when breakfast is prepared at that point return to my room in the wake of eating. In the wake of doing the morning tasks, I checked our cooler to perceive what I can get ready for lunch. I had no clue about what to cook so I just gazed vacantly on the vegetables and meat that was in the cooler. I continued reasoning I need to set up a good dinner for my mother so she can recover her quality and show signs of improvement immediately. It’s something worth being thankful for that we are so outfitted with innovation that the web can give all of you the little subtleties in cooking †simply like a guide for fakers. I wound up making some pasta for me and my kin and a soup for my mom. It was my first an ideal opportunity to cook pasta just without anyone else. Regardless of whether I had the comfort of canned spaghetti sauce and prepared to cook pasta, it was as yet an extreme activity to do on the grounds that I needed to ensure that I will have the option to cook something worth eating.

Data mining titanic dataset Essays

Information mining titanic dataset Essays Information mining titanic dataset Paper Information mining titanic dataset Paper Titanic dataset Submitted by: Submission date 8/1/2013 Declaration Author: Contents Dated: 29/12/2012 The database compares to the sinking of the titanic on April the fifteenth 1912. It is a piece of a database containing the travelers and team who were on board the boat, and different credits connecting to them. The motivation behind this undertaking is to apply the procedure of CRISP-DMS and follow the stages and errands of this model. Utilizing the order technique in fast digger and both the choice tree and INN calculations, I will make a preparation model and attempt apply the class endure or didnt endure. On the off chance that I apply a choice tree to the dataset all things considered, I get a forecast pace of 78%. I will attempt different procedures all through this report to expand the general forecast rate. Information mining goals: I might want to investigate the pre considered thoughts I have about the sinking of the titanic, and demonstrate in the event that they are right. Was there a dominant part of third class travelers who kicked the bucket? What was the proportion of travelers who kicked the bucket, male or female? Did the area of lodges have any kind of effect with regards to who endure? Did valor ring through and did Women and kids first really occur? Information Understanding: Describe the information: Figure Class mark: Survive (1 or O) 1 = endure, passed on. Type = Binomial. Absolute: 891. Endure: 342, Died: 549 Attributes: 10 qualities 891 columns The dataset have fundamentally a straight out kind of property so there is uninformed substance. This may show a choice tree would be a fitting model to utilize. I can see that the quantity of lines in the dataset is in fact 10 to multiple times the quantity of sections, so the quantity of occasions is satisfactory. There doesnt appear to be any inconsistencys in the information. Pappas: first, second, or third class. Type: polynomial. Unmitigated, third class: 491, second class: 216, first class: 184 0 missing Name: Name of Sex: Male, female. Type: binomial. Male: 577, Female: 314 0 missing Age: from 0. 420 to 80. Normal age: 29, standard deviation of 14+-, Max was 80. 177 missing Sibs (Siblings ready): Type: whole number. Normal under 1, most elevated 8. This proposed an exception, however on review the names where there were 8 kin related. (The name was wise, third class travelers, all kicked the bucket. ) O missing Parch: number of guardians, youngsters installed. Type: number. Normal: 0. 3, deviation 0. 8. Max was 6. O missing Ticket: ticket number. Type: polynomial. To me these ticket numbers appear to be very irregular and my first tendency is to dispose of them. O missing Fare: Cost of ticket. Type: genuine. Normal: 32, deviation +-49. Most extreme 512. There is by all accounts a serious dissimilarity in the scope of qualities here. Three tickets cost 512, exceptions? O missing Cabin: lodge numbers. Type: polynomial. 687 missing From taking a gander at this information I want to limit one of my underlying inquiries regarding lodge numbers. On the off chance that there was more information it may be a fascinating component as respects lodge areas and endurance. As it stands the nature of the information isn't acceptable, there are Just o many missing passages. I. E. More prominent than 40%. So I will erase (sift through) the lodge characteristic from the dataset. The age quality could cause an issue with the measure of fields missing. There are beyond any reasonable amount to erase. I may utilize the normal of any age to fill in the spaces. Investigate the information: From an underlying investigation of the information, I had the option to take a gander at different plots and discovered some intriguing outcomes. I have attempted to hold my discoveries to my underlying inquiries that I needed replied. Was there a larger part of third class travelers who kicked the bucket? You can tell from Figure 2 this was valid. This chart Just shows endurance by class, third class fairing the most noticeably terrible. Again this is appeared with a dissipate plot however with the additional quality sex. You can see on the female side of the five star travelers, just a couple kicked the bucket. Strikingly it shows that it was for the most part male third class travelers who died, and it is exhibited that more guys then females kicked the bucket. There is an unmistakable division in classes illustrated. This diagram responds to my other inquiry. What was the proportion of travelers who passed on, male or female? From this we can see that chiefly guys didn't endure. In spite of the fact that there were more guys ready (577), around 460 died. From the females (314), around 235 endure. Another characteristic that needs consideration is the age class. I needed to see whether the ladies and youngsters first arrangement was clung to, however there are 177 missing age esteems. This will confuse my outcomes on this. From leaving the 177 as they seem to be, I get this diagram: however this isn't convincing in Figure 5. I believed that the charge cost may show a childrens cost and subsequently permit me to fill during a time, however the admission cost doesnt appear to have a lot of example. Another thought I thought may help is take a gander at the names of travelers, I. . Miss may imply a lower age. (In 1912 the normal period of marriage was 22, so anybody with title miss could have an age under 22. ) Names which incorporate ace may demonstrate a youthful age also. Figure 5 likewise demonstrates potential anomalies on the correct hand side. From this chart I could without much of a stretch see the breakdown of the diverse class of traveler and where they set out from. Clearly Southampton had the biggest number of travelers jump aboard. Question had the most noteworthy extent of third class travelers contrasted with second and first class at that port, and its additionally fascinating o note this was an Irish port. This diagram further investigates the port of bank and shows the endurance rate from each, just as the various classes. To me it appears that most of third class travelers were lost who originated from Southampton port, despite the fact that they had the most elevated measure of third class travelers. A more intensive glance at Southampton port. The dominant part who didnt endure were third class (blue), additionally noted is the bunch of first class travelers (green) who kicked the bucket, yet Southampton had the most noteworthy number of first class travelers to board. See figure 6. Check information quality There were various missing qualities in the dataset. The most elevated measure of missing information originated from the lodge trait. As it is higher than 45% (687 missing) I chose to sift through this section. There are additionally 177 missing qualities from the age characteristic. This measure of missing information is again too enormous a rate to overlook and should be filled in. I can see that the dataset contains under 1000 columns, so I believe that testing won't need to be performed. There doesnt appear to be any inconsistencys in the information. There are as yet 2 missing snippets of data from the dike characteristic. I see that they are first class travelers so from my diagram on dike I want to put her bank from Churchgoer. The other traveler is a George Nelson, which I will add to Southampton. I chose to sift through names too. I dont perceive how it can help in the dataset. It might have assisted with age, by taking a gander at the title as I stated, yet for this I Just utilized the normal age to supplant the missing qualities. Another way to deal with filling in the missing age fields may be straight relapse. Evacuate potential exceptions? I can see that there might be a few exceptions. For example in the tolls trait, there re three tickets which cost 512 when the normal is 32. They were top of the line tickets, however the thing that matters is gigantic. Information Preparation: Here is the aftereffect of utilizing x approval on the dataset before any information arrangement has occurred. I will presently sift through the issue of 667 lodge numbers missing. With it being higher than 40%, Vive chose to erase the trait completely. Vive additionally erased the name characteristic, as I dont perceive how it will help. By erasing lodge, name and ticket, here is the outcome I get: I supplanted the missing age fields with the normal of ages, this expanded the precision daintily and gave these outcomes with x approval: I utilized distinguish exceptions and picked the main ten and afterward sifted them through. This gave this outcome: The class review for endure has not improved a lot. Expanding the quantity of neighbors in the recognize anomalies administrator improved things, additionally restricting the channel to erasing 5 improved a precision. I chose to utilize determined binning for the ages and broke the ages into three receptacles. For youngsters matured up to 13, moderately aged from 13 to 45, and more established from 45 to 80. I attempted distinctive age ranges and found that these reaches yielded the best outcomes. It increased the precision. I additionally utilized binning for the passages, parting them into low, mid, and high which likewise improved outcomes on the disarray grid. I utilized identify anomaly to locate the ten most clear exceptions, and afterward utilized a channel to dispose of them. I have chosen to expel lodge from the dataset, and furthermore there are 177 missing age esteems which I have attempted different methodologies in evolving. I changed the ages to the normal age, yet this gives a spike in the quantity of ages 29. 7. Case of normal age issue: Modeling: I attempted to actualize both the choice tree and hotel calculations, seeing as the dataset as fundamentally absolute. I found that motel yielded the best outcomes with respect to precision. This was set at k=l . The exactness was not incredible at 73%. The parameter of K is excessively little and might be affected by commotion. Motel: 5 worked the best at 82. 38%. This is by all accounts the ideal incentive for k, and the separation is fixed. Class accuracy is about even on each class. Choice tree: The choice tree calculation didnt give me as much exactness, and I found that killing pre pruning gave me a superior precision. From the choice tree, the age binning appeared to anticipate moderately aged guys (13 to 45) with a low charge well. The class review for survi

The Elusive Term :: Essays Papers

The Elusive Term Ring-supplier is dangerous in light of the fact that it doesn't adhere to its intelligent importance. Once explored and characterized, it is realized that a ring-supplier is a ruler or overlord. Not only a ruler can be known as a ring-provider. An individual in a regulatory position can be alluded to as a ring-supplier. The thanes who got this honor of arm-ring or neck-ring demonstrated their fight value and were held in high regard by their family. In this manner the warriors kept close watch of their prizes. After research, one additionally finds the expression ring-supplier is a kenning; or Anglo - Saxon analogy. Ring-provider is a tricky word. Independently, ring is characterized as a decorative roundabout band worn on a finger, and provider is characterized as to hand over something. Assembling those two definitions, ring-supplier infers the demonstration of giving rings. Be that as it may, this isn't at all the case. A ring-supplier is a lord or overlord. Somebody in power is likewise alluded to just like the ring-supplier. In the Norton Anthology we read in brave verse in any event, a chief type of cash was the gold arm ring; which had the upside of being important, versatile and pompous; subsequently the ruler continually being alluded to as 'ring-provider.' (28). Despite the fact that it was normally the ruler who was given the name ring-provider, anybody in power, could be given the title. In the Grendel, Beowulf's most popular adversary site, we see another event of a ring-supplier being conceived: When the retainers [soldiers] came back from fight, they were relied upon to give their abundance to their boss, who might then redistribute it as indicated by the presentation of every retainer during fight. Accordingly, we regularly discover the dryhten [overlord] being alluded to as the 'gold-provider' or 'ring-supplier.' Usually the ruler had the ability to circulate abundance or prizes, however here we see that anybody in the authority position may have been given the renowned title of ring-supplier. The lord or overlord gave out arm-rings or neck-rings to advance valor and battling for land and ruler. The person who got the ring was regarded by his individual officers. These rings worn on either arm or neck, gave the wearer acknowledgment and moment envy in others. They were images of solidarity and fortitude; there more likely than not been numerous endeavors to take such objects of fiscal and social worth. In any case, it is far fetched that the valiant men who got such honors at any point allowed the chance to lose their trophies.

The Main Walk COLUMBIA UNIVERSITY - SIPA Admissions Blog

The Main Walk COLUMBIA UNIVERSITY - SIPA Admissions Blog Each day as I exit and enter our building on my bike commute to and from work I pass the main walk.   This is a pedestrian pathway through the central part of the University what would be 116th street.   The path always seems to be filled with pedestrians no matter the time of day and this time of year it is illuminated with lights. Lenny Pridatko is a current SIPA student that takes a great interest in photography.   He shared the following photograph with our office.   It has not snowed a great deal this year, but this photo from last year captures the essence of a snowy night. I have spent the last few days submitting an application of my own to SIPA so I know exactly what you as an applicant are seeing as you submit the application.   I will be using my research to put together an entry that explains how you can track your application and what you will see on our site as we process your application.   As the days pass I will also provide updates on what is going on in our office and I will continue to answer common questions so stay tuned.