Descartes’ First Meditation • Four bulldozers of doubt: – I can’t trust my senses – I could be crazy – I could be dreaming – A malicious demon could be out to fool me. • Is there anything you’d stake your life on?
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City Housing Trust Funds Berkeley, California: Housing Trust Fund Cupertino, California: Affordable Housing Fund Los Angeles, California: Housing Trust Fund Menlo Park, California: Below Market Rate Housing Reserve Morgan Hill, California: Senior Housing Trust Fund Palo Alto, California: The Housing Reserve Sacramento, California: Housing Trust Fund San Diego, California: Housing Trust Fund San Francisco, California: Office Affordable Housing Production Program; Hotel Tax Fund; and Bond Housing Program Santa Monica, California: Citywide Housing Trust Fund West Hollywood, California: Affordable Housing Trust Fund Aspen, Colorado: Housing Day Care Fund Boulder, Colorado: Community Housing Assistance Program and Affordable Housing Fund Denver, Colorado: Skyline Housing Fund Longmont, Colorado: Affordable Housing Fund Telluride, Colorado: Housing Trust Fund Tallahassee, Florida: Housing Trust Fund Chicago, Illinois: Low Income Housing Trust Fund Bloomington, Indiana: Housing Trust Fund Fort Wayne, Indiana: Central City Housing Trust Fund Indianapolis, Indiana: Housing Trust Fund Lawrence, Kansas: Housing Trust Fund Boston, Massachusetts: Neighborhood Housing Trust Cambridge, Massachusetts: Housing Trust Fund Ann Arbor, Michigan: Housing Trust Fund St. Paul, Minnesota: STAR Program St. Louis, Missouri: Housing Trust Fund New Jersey: 142 COAH approved developer fee programs Santa Fe, New Mexico: Community Housing Trust Greensboro, North Carolina: VM Nussbaum Housing Partnership Fund Columbus/Franklin County: Affordable Housing Trust Fund Toledo, Ohio: Housing Fund Portland, Oregon: Housing Investment Fund Charleston, South Carolina: Housing Trust Fund Knoxville, Tennessee: Housing Trust Fund Nashville, Tennessee: Nashville Housing Fund, Inc. Austin, Texas: Housing Trust Fund San Antonio, Texas: Housing Trust Salt Lake City, Utah: Housing Trust Fund Burlington, Vermont: Housing Trust Fund Alexandria, Virginia: Housing Trust Fund Manassas, Virginia: Manassas Housing Trust Fund, Inc. Bainbridge Island, Washington: Housing Trust Fund Seattle, Washington: Housing Assistance Funds Washington, D.C.: Housing Production Trust Fund
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So…Where Have You Been? In this assignment, I would like you to help me compile a composite profile of Thinking Geographically students’ geographic experience. Attached are three blank maps: one of Virginia’s localities; one of the United States; and one of the world (with enlarged insets for Europe and the Middle East). On each, shade in all of the localities, states, and countries you have traveled through or visited. You must have been on the ground in each locality, state, or country; airport layovers or airport hotel stays and travel through by train do not count!. Use whatever kind of marker you like (I prefer the medium highlighters with sharp and wide surfaces, but marking pens that won’t bleed through, colored pencils, and even crayons will do), as long as it’s easily seen on the maps. Virginia map – (1) color-in the localities you have been in and/or through. You may need to consult a Virginia highway map to figure out which Commonwealth localities you’ve experienced. For example, if you’ve been from Fairfax County to Longwood via US 15, from north to south, you’ve been through Fairfax, Prince William, Fauquier, Culpeper, Madison, Orange, Louisa, Fluvanna, Buckingham, and Prince Edward Counties. From the City of Richmond to Virginia Beach via I-64, I-664, and I-264/Virginia Beach Expressway, you would have been in Richmond City, Henrico, New Kent, James City, and York Counties, and Newport News, Hampton, Norfolk, and Virginia Beach Cities. All of the places you’ve been in Virginia should be contiguous (strung together) unless you flew/parachuted in, came in by boat, or snuck in through a neighboring state. If you’ve been to all but a handful of localities, you may mark those you have not been to, as long as you make a note of that on the map. (2) count up and record the number of localities you have been to/through, divide that number by 133, multiply by 100, and record the percentage of localities you’ve been to in the space provided (all told, you’ve probably been to more of Virginia than you realize – that’s part of the point of this!); (3) write in what you consider your home locality (probably where you graduated high school) in the space provided and indicate it with a darker color or black on the map (if you’re from out-of-state, just leave it blank); (4) check the appropriate box for urban/suburban/small town/rural (be aware that just because your locality has the work “city” in its title doesn’t necessarily mean it’s urban – which means built-up); and (5) use a line pattern to indicate the locality you most want to begin your teaching career in. US map – (1) color the states you’ve been to/through (remember: airports and train travel don’t count), darken/blacken in your home state; (2) write in your birthplace state (for most of you, that probably will be Virginia) in the space provided and blacken/darken it in on the map; (3) tally and record the number of states you’ve been to/through (including the District of Columbia and your home state), divide by 51, multiply by 100, and that’s the percentage of states you’ve been to and enter that number in the space provided; (4) with a horizontal line pattern for your father and a vertical line pattern for your mother, mark your parents’ birth states on the map (if it’s the same state, you’ll have a crisscrossed pattern) World map – (1) color the countries you’ve been to other than the U.S. (even if you’ve only been to a coastal resort, you’ve been to that country, but again, airport layovers don’t count); (2) tally and record the number of countries other than the U.S. that you’ve been to, divide by 205, multiply by 100, and that’s the percentage of countries other than the US that you’ve visited. Enter that number in the space provided. I’ve provided inset maps for Europe and the Middle East that show more detail if you’ve been to a small country that’s difficult to see. If you’ve been to an island country too small to be seen, list those on the map. You do not need to mark the U.S. on this map. I will tally up the total results and produce maps showing the percentage of students across all three sections who have been to/through particular Virginia localities, U.S. states, and other countries. This will give us an idea of how well-traveled you all are. Value: up to 15 points (12 necessary items, one point each + 3 possible neatness points) Due date: Wednesday, February 10, 2016 DO NOT INCLUDE THIS COVER SHEET WHEN YOU HAND THE MAPS IN! 1
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Descartes’ Second Meditation • • • • • Is there anything I can’t doubt? I can’t doubt that: I doubt. Try it: I doubt that I doubt. OK, but then: I doubt that I doubt. Self-certifying process: Doing it makes it so. • So, “I doubt” is absolutely certain.
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Closed hashing (Open addressing) Keys are stored inside a hash table. Key A FOOL AND h (K ) 1 9 0 1 2 3 4 6 5 6 HIS MONEY ARE SOON PARTED 10 7 11 11 12 7 8 9 10 11 12 A A FOOL A AND FOOL A AND FOOL HIS A AND MONEY FOOL HIS A AND MONEY FOOL HIS ARE A AND MONEY FOOL HIS ARE SOON PARTED A AND MONEY FOOL HIS ARE SOON A. Levitin Copyright © 2007 Pearson Addison-Wesley. All rights reserved. “Introduction to the Design & Analysis of Algorithms,” 2nd ed., Ch. 77-20
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ACM IEEE ASONAM 2015 Accepted Tutorials Keynote Speakers •Bot Detection in Social Media: Networks, Behavior, and Evaluation Interpersonal Trust Dynamics in Online Systems – Models and ApplicationsJ Jaideep Srivastava University of Minnesota, USA Fred Morstatter, Kathleen M. Carley, and Huan Liu and Qatar Foundation, QatarAbstract •Core Decomposition: Algorithms and Applications •Understanding the nature of online interpersonal trust continues to gain importance, especially as we increasingly perform activities Fragkiskos D. Malliaros, Michalis Vazirgiannis, and Apostolos N. Papadopoulos and form relationships online. Trust forms a critical substrate on which activities with economic consequence, e.g. e-commerce •Analysis and mining of multiple social networks Matteo Magnani •Principles, models, and methods for the characterization and analysis of lurkers in online social networks transactions, or relationships with emotional consequence, e.g. friendships and romances, are built. There is a vast literature on Roberto Interdonato and Andrea Tagarelli •Subgroup Discovery and Community Detection on Attributed Graphs M Atzmueller interpersonal trust in the social sciences. However, with the mass adoption of the Internet in our daily lives, and the ability to capture high resolution data on its use, we are at the threshold of a deeper understanding of the dynamics behind interpersonal trust. It is now becoming possible to study the phenomenon of trust dynamics at a much finer granularity than ever before. Online social systems such as Multiplayer Online Games (MOGs) and Virtual Worlds (VWs) have become increasingly popular and have communities comprising tens of millions. They serve as unprecedented tools to theorize and empirically model the trust dynamics of individuals, workshops in conjunction with ACM IEEE ASONAM 2015. groups, and networks within large communities. •Advances of Social Media K-H Networks (KHNetwork 2015) This talk consists of four parts. First, we describe findings from the Virtual World Exploratorium; a multi-institutional, multidisciplinary project which uses data from commercial MMOGs and VWs to study many fields of social science, including sociology, •Organizers: – social psychology, organization theory, group dynamics, macro-economics, etc. Second, describe a model for a multi-relational, Ahmed Abdeen Hamed, University of Vermont, USA [email protected] multi-activity environment, where ‘low familiarity threshold’ activities like chatting, grouping, and transactions form the scaffolding •Multiplex & Attributed Networks Mining (MANEM2015) for the formation of ‘high familiarity threshold’ relationships like trust formation. Third, using this model, we describe our studies on •Organizers: the dynamics of online interpersonal trust, including like trust formation, trust reciprocation, trust revocation, and the nature of trust – Rushed Kanawati, Universite Paris-Nord, France [email protected] – Christine Largeron, Jean Monnet University, Saint-Etienne, France [email protected] – I-Hsien Ting, National University of Kaohsiung, Taiwan [email protected] transitivity and trust cascading. Finally, we describe some applications of this model for tasks like understanding the vulnerabilities of a social network to rumor spreading, and inoculation against it. Communities and privacy in mobile phone social networks Vincent Blondel Ucatholique de Louvain, Belgium •The 6th International Workshop on Mining and Analyzing Social Networks for Decision Support (MSNDS2015) •Organizers: •We describe several recent results on large network analysis with a special emphasis on community detection and on the analysis of mobile phone datasets. In particular, we describe the Louvain method that and can be routinely used for analyzing networks with billions of nodes or links. We analyze communities obtained on a nationwide dataset of criminal records, as well as on a social – I-Hsien Ting, National University of Kaohsiung, Taiwan [email protected] network constructed from mobile phone communications that span periods covering several months. We also describe applications of – Chung-Hong Lee, National Kaohsiung of Technology and Applied Sciences, Taiwan mobile phone dataset analysis for a range of applications such as urban planning, traffic optimization, monitoring of development – Chen-Shu Wang, National Taipei University of Technology, Taiwan •The 5th International Workshop on Social Network Analysis in Applications (SNAA2015) policy, crisis management, and control of epidemics. With these applications in mind we overview results obtained in the ''Data for Development'' (D4D) challenge on the analysis of mobile phone datasets. We analyze the privacy threats of anonymized mobile phone dataset and show that human behavior puts fundamental natural constraints to the privacy of individuals. •Organizers: The Dynamics of Social Influence and Reputation Online Sinan Aral MIT Sloan School of Management, USA – Piotr Brodka, Wroclaw University of Technology, Poland [email protected] – Katarzyna Musial, King’s College London, United Kingdom [email protected] – Marcin Budka, Bournemouth University, United Kingdom Identity and reputation drive some of the most important relational decisions we make online: Who to follow or link to, whose •1st International Workshop on Dynamics in Networks (DyNo2015) information to trust, whose opinion to rely on when choosing a product or service, and whose content to consume and share. Yet, we know very little about the dynamics of social influence and relational reputation and how they affect our decision making. Sinan will describe a series of large scale experiments that explore the behavioral dynamics catalyzed by social influence, identity and
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Sentences in FOL  What does the following FOL sentence mean? x [Person(x)  t (Time(t)  Fool(x,t))]  t [Time(t)  x (Person(x)  Fool(x,t))]  t,x [Time(t)  Person(x)  Fool(x,t)] • Abraham Lincoln: "If you once forfeit the confidence of your fellow citizens, you can never regain their respect and esteem. It is true that you may fool some of the people all of the time; you can even fool all of the people some of the time, but you can’t fool all of the people all of the time."
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Translating English to FOL Every gardener likes the sun. x gardener(x)  likes(x,Sun) You can fool some of the people all of the time. x t person(x) time(t)  can-fool(x,t) You can fool all of the people some of the time. x t (person(x)  time(t) can-fool(x,t)) Equivalent x (person(x)  t (time(t) can-fool(x,t)) All purple mushrooms are poisonous. x (mushroom(x)  purple(x))  poisonous(x) No purple mushroom is poisonous. x purple(x)  mushroom(x)  poisonous(x) Equivalent x (mushroom(x)  purple(x))  poisonous(x) There are exactly two purple mushrooms. x y mushroom(x)  purple(x)  mushroom(y)  purple(y) ^ (x=y)  z (mushroom(z)  purple(z))  ((x=z)  (y=z)) Clinton is not tall. tall(Clinton) X is above Y iff X is on directly on top of Y or there is a pile of one or more other objects directly on top of one another starting with X and ending with Y. x y above(x,y) ↔ (on(x,y)  z (on(x,z)  above(z,y)))
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REFLECTIVE JOURNALING TOOLS Reflective J ournalingTools LEARNING: • How is practice different from theory? Did this exercise help you to understand your theory and the application of theory better? How? Why? • Did you learn anything that helped you to better understand a theory, the use of a test that you were taught in lectures/labs? • What did you learn that were not taught in lectures (e.g. communication with patients), and how did you cope or learn more about this to improve your performance? Or how can this be incorporated into lectures? • Did this exercise help you to remember or recall later other aspects of previous experiences that you have forgotten? • Did this exercise help you identify areas that need to be changed, improved etc. in yourself/peers/staff/clinical training etc. Why and how? • What actions did you take you take and what are the results (what did you learn)? SELF ASSESSMENT: • Did you identify areas/issues that you were unclear of, or disagreed with your supervisors/peers, or different from what you have learned in your past lectures? Justify the actions taken. Did this help you in your learning? How? • Have you been open to share with others and to listen what others have to say? • Have you paid attention to both your strong and weak points? Can you identify them? What are you going to do about them? • How did faculty supervision/RW help you in your clinical experiences in relation to your professional growth? (eg. did it encourage you to be more independent, to become more confident in professional activities and behaviors etc) • What have you noted about yourself, your learning altitude, your relationship with peers/supervisors etc. that has changed from doing this exercise? COMMUNICATION: • What have you learned from interacting with others (peers/supervisors/staff etc)? • Did your peers gain anything from YOUR involvement in this exercise and vice versa? • Did this exercise encourage and facilitate communication? • Did you clarify with your supervisors/peers about problematic issues identified? Why (not)? What are the results? • How could you/your peers/staff help you overcome negative emotions arising from your work? Did your show empathy for your peers? PROFESSIONALISM: • Did you learn that different situations call for different strategies in management? • What are the good and bad practices that you have identified? How would you suggest to handle the bad/poor practices identified (if any)? • Did you learn to accept and use constructive criticism? • Did you accept responsibility for your own actions? • Did you try to maintain high standard of performance? • Did you display a generally positive altitude and demonstrate self-confidence? • Did you demonstrate knowledge of the legal boundaries and ethics of contact lens practice? EMOTION & PERSONAL GROWTH: • Did you reflect on your feelings when dealing with the case/peers/supervisor (eg. frustration, embarrassment, fear) for this exercise? If not, why not? If yes, who should be responsible — you, your patient or your supervisor? Why? • Did you find reflection (as required for this exercise) helpful, challenging, and enjoyable, change the way you learn? How? Why (not)? • How and what did you do to handle negative emotions arising from doing this subject? How could these feelings be minimized? • Did you try to find out if your feelings were different from your peers? Why? What did you do to help your peers? • Did you reflect on your learning altitude? How was it? Is there room for improvement? How? Why (not)? • What did you learn about your relationship with your peers/supervisors? What did you learn about working with others? Ideas for Reflective Journaling Writing Contributor(s): Dr. Michael Ying and Dr. Pauline Cho
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Words from boys in Pontville APOD ‘Even if you make this place heaven it is not enough for us because we feel like we are in a cage. We feel people see us like animals in a cage.’ ‘Four months we have been staying here. Our request is they should get us out of here when we are fit and healthy, not when we are crazy.’ ‘We stay up all night and sleep all day. We don’t want to go to school because it upsets us to see others free.’ ‘At school people stare and laugh at us which makes us feel sad.’ ‘We are getting crazy pressure from our families. At 3am if you come here you will see people walking around like crazy because they can’t sleep. There are going crazy so people cut themselves.’ ‘We are fed up with people telling us we are going to get out. We don’t mind living on the street, we just want to get out.’
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createconfigs script in src/mpp-mpred-3.2.0/p95/mu11 #!/bin/bash for g in .1 .2 .4 .7 .9 do sed -i -e "s/dMNsdsThr=[^ ]*/dMNsdsThr=$g/" t.config for h in .1 .2 .4 .7 .9 do sed -i -e "s/dMNsdsExp=[^ ]*/dMNsdsExp=$h/" t.config cp t.config configs/a$g$h.config submit script run in scr/mpp-mpred-3.2.0 produces subdirs in mpp-mpred-3.2.0 : drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x drwxr-xr-x creates in src.mpp-mpred-3.2.0/p95/mu11/configs: -rw-r--r-- 1 a.1.1.config -rw-r--r-- 1 a.1.2.config -rw-r--r-- 1 a.1.4.config -rw-r--r-- 1 a.1.7.config -rw-r--r-- 1 a.1.9.config -rw-r--r-- 1 a.2.1.config -rw-r--r-- 1 a.2.2.co -rw-r--r-- 1 a.2.4.co submit in src/mpp-mpred-3.2.0 produces here -rw-r--r-- 1 a.2.7.co #!/bin/bash -rw-r--r-- 1 a.2.9.co -rw-r--r-- 1 a.4.1.co for g in .1 .2 .4 .7 .9 do; for h in .1 .2 .4 .7 .9 do -rw-r--r-- 1 a.4.2.co ./mpp-submit -S -i Data/p95test.txt -c p95/mu11/configs -rw-r--r-- 1 a.4.4.co a$g$h.out -t .05 -d ./p95/mu11 -rw-r--r-- 1 a.4.7.co -rw-r--r-- 1 a.4.9.co -rw-r--r-- 1 a.7.1.co .predictions -rw-r--r-- 1 a.7.2.co p95test.txt.rmse 12641: Movie: 12641: -rw-r--r-- 1 a.7.4.co 1.22 0: Ans: 1 Pred: 1.22 Error: -rw-r--r-- 1 a.7.7.co 3.65 0.04840 -rw-r--r-- 1 a.7.9.co 1: Ans: 4 Pred: 3.65 Error: 2.55 -rw-r--r-- 1 a.9.1.co 0.12250 4.04 -rw-r--r-- 1 a.9.2.co 2: Ans: 2 Pred: 2.55 Error: 1.85 -rw-r--r-- 1 a.9.4.co 0.30250 -rw-r--r-- 1 a.9.7.co 3: Ans: 4 Pred: 4.04 Error: -rw-r--r-- 1 a.9.9.co 0.00160 12502: -rw-r--r-- 1 a.1.1.out 4: Ans: 2 Pred: 1.85 Error: 4.71 0.02250 -rw-r--r-- 1 a.1.2.out 3.54 Sum: 0.49750 Total: 5 RMSE: 0.315436 -rw-r--r-- 1 a.1.4.out Running RMSE: 0.315436 / 5 predictions 3.87 -rw-r--r-- 1 a.1.7.out 3.33 12502: -rw-r--r-- 1 a.1.9.out Movie: 2.97 0: Ans: 4 Pred: 4.71 Error: -rw-r--r-- 1 a.2.1.out 0.50410 : -rw-r--r-- 1 a.2.2.out . 1: Ans: 5 Pred: 3.54 Error: -rw-r--r-- 1 a.2.4.out 2.13160 10811: -rw-r--r-- 1 a.2.7.out 2: Ans: 5 Pred: 3.87 Error: 1.2769 4.05 -rw-r--r-- 1 a.2.9.out 3: Ans: 3 Pred: 3.33 Error: 3.49 -rw-r--r-- 1 a.4.1.out 0.10890 3.94 4: Ans: 2 Pred: 2.97 Error: -rw-r--r-- 1 a.4.2.out 3.39 0.94090 -rw-r--r-- 1 a.4.4.out Sum: 4.96240 Total: 5 RMSE: 0.996233 -rw-r--r-- 1 a.4.7.out 12069: -rw-r--r-- 1 a.4.9.out : Running RMSE: .738911 /10 predictions 3.20 -rw-r--r-- 1 a.7.1.out Movie: 10811 3.48 -rw-r--r-- 1 a.7.2.out 0: Ans: 5 Pred: 4.05 Error: -rw-r--r-- 1 a.7.4.out 0.90250 1: Ans: 3 Pred: 3.49 Error: -rw-r--r-- 1 a.7.7.out -rw-r--r-- 1 a.7.9.out 0.24010 is a script, createtablermse: 2: Ans: 4 Pred: 3.94In dotouts Error: -rw-r--r-- 1 a.9.1.out #!/bin/bash 0.00360 -rw-r--r-- 1 a.9.2.out 3: Ans: 3 Pred: 3.39for gError: in .1 .2 .4 .7 .9 do; for h in .1 .2 .4 .7 .9 -rw-r--r-- 1 a.9.4.out 0.15210 grep RMSE:\ a$g$h.out >> rmse -rw-r--r-- 1 a.9.7.out Sum: 1.29830 Total: 4 RMSE: 0.569715 -rw-r--r-- 1 a.9.9.out Running RMSE: 0.964397 / 743 preds I copy to src/mpp-mpred-3.2.0/dotouts. Movie: 12069: do Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: Sum: 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 10:15 10:15 10:15 10:15 10:15 10:15 10:15 10:16 10:16 10:16 10:16 10:16 10:16 10:16 10:17 10:17 10:17 10:17 10:17 10:17 10:17 10:18 10:18 10:18 10:18 a.1.1 a.1.2 a.1.4 a.1.7 a.1.9 a.2.1 a.2.2 a.2.4 a.2.7 a.2.9 a.4.1 a.4.2 a.4.4 a.4.7 a.4.9 a.7.1 a.7.2 a.7.4 a.7.7 a.7.9 a.9.1 a.9.2 a.9.4 a.9.7 a.9.9 692.82510 691.59330 691.90610 691.90610 691.90610 691.84690 690.47330 691.90610 691.90610 691.90610 693.27970 691.90610 691.90610 691.90610 691.90610 691.90610 691.90610 691.90610 691.90610 691.90610 691.90610 691.90610 691.90610 691.90610 691.90610 and e.g., a.9.9 contains: -rw-r--r--rw-r--r--rw-r--r--rw-r--r--rw-r--r--rw-r--r--rw-r--r-- 1 a.9.9.config 1 hi-a.9.9.txt hi-a.9.9.txt.answers lo-a.9.9.txt lo-a.9.9.txt.answers p95test.txt.predictions p95test.txt.rmse dotouts is a script, createtablejob:#!/bin/bash for g in .1 .2 .4 .7 .9 do; for h in .1 .2 .4 .7 .9 do grep Input:\ \ \ lo a$g$h.out >> job Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Total: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: Input: 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 lo-a.1.1.txt lo-a.1.2.txt lo-a.1.4.txt lo-a.1.7.txt lo-a.1.9.txt lo-a.2.1.txt lo-a.2.2.txt lo-a.2.4.txt lo-a.2.7.txt lo-a.2.9.txt lo-a.4.1.txt lo-a.4.2.txt lo-a.4.4.txt lo-a.4.7.txt lo-a.4.9.txt lo-a.7.1.txt lo-a.7.2.txt lo-a.7.4.txt lo-a.7.7.txt lo-a.7.9.txt lo-a.9.1.txt lo-a.9.2.txt lo-a.9.4.txt lo-a.9.7.txt lo-a.9.9.txt RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: RMSE: 0.964348 0.963490 0.963708 0.963708 0.963708 0.963667 0.962710 0.963708 0.963708 0.963708 0.964664 0.963708 0.963708 0.963708 0.963708 0.963708 0.963708 0.963708 0.963708 0.963708 0.963708 0.963708 0.963708 0.963708 0.963708
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Getting Started Welcome to Williams. When you get to campus, you’ll have a list of things to do and, inevitably, many decisions to make about what you’ll buy. This section offers advice about budgeting for some of your bigger expenses: books, computers, and communication. 1) Books 1) If you’re on financial aid your books are paid for by Williams as long as you buy them from Water Street Books (the ONLY book store that the financial aid office acknowledges). Course packets and most art studio fees are also covered by Williams’ book grant. You can’t be reimbursed for books bought from online sources or other bookstores. And you must ONLY buy books for courses that you’re actually enrolled in. About a week prior to the start of classes, you’ll be notified that you may purchase your books at Water Street Books by simply swiping your ID card. Doing so will charge the book purchases to your student term bill, which will then be covered by college grants once your enrollment in each course is verified. The grant applied to your student term bill will cover the TOTAL amount paid for your books and course packets. YOU THEN OWN THESE MATERIALS and are free to sell, donate, or keep these books at the end of the semester. For more information visit: http://finaid.williams.edu/announcements/#book_grant 2) If you’re not on financial aid, you can buy used books instead of new books at Water Street Books. This will cost you less money, and you may even get lucky and inherit a book from someone who was a great highlighter. You can also participate in the book rental system through Water Street Books, which allows you to rent the books for the year, instead of buying them. You may charge your books with your student ID, and the costs will then be added to your term bill and can be paid at a later date. 3) Buy your books online. As long as you don’t mind waiting a few days for shipping, you can save anywhere from 50%-70% per book. On the next page, there is a list of websites that sell textbooks at discount prices.
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