Aid in the Age of Uncertainty January 23, 2009 Joe Russo Very difficult economy

Aid in the Age of Uncertainty January 23, 2009 Joe Russo Very difficult economy www.phwiki.com

Aid in the Age of Uncertainty January 23, 2009 Joe Russo Very difficult economy

McBride, Sarah, Digital Entertainment Distribution Reporter has reference to this Academic Journal, PHwiki organized this Journal Aid in the Age of Uncertainty January 23, 2009 Joe Russo Very difficult economy Wall Street in addition to Main Street Unemployment, lay offs, in addition to asset losses Bail outs in addition to demise of employers Role of media: hyperbole in addition to misin as long as mation Concerns evident at college Possible cost related enrollment shifts Mid-year tuition increases in addition to budget reductions Hiring in addition to construction freezes Endowment values One more dynamic: Accountability Reauthorization dem in addition to s as long as transparency Outcomes Indicators of per as long as mance

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Refrain from public announcements Put any such announcements in perspective Avoid knee-jerk reactions Different solutions as long as different settings (reduced endowment versus large Pell enrollment) Is policy sustainable in addition to fiscally responsible Manage future expectations Incoming versus returning students Reputation of institution Time in addition to compassion Special communications regarding af as long as dability in addition to compassion Low/no interest institutional loan program Retention ef as long as ts supporting on-time graduation

What opportunities present themselves as long as Students in addition to families: lifestyle; plan in addition to save Institutions: review policies Payment Institutional loans Financial aid packaging Everyone: invest/support 529 programs Government Employers Institutions (I-529) Family including relatives The media “Colleges Struggle to Preserve Financial Aid” New York Times, November 11, 2008 “Private Colleges Worry About a Dip in Enrollment” New York Times, December 21, 2008 “Things Bad Enough without Media Making them Worse” The Washington Post, December 31, 2008 “Moody’s Forecasts Stiff Challenges, Especially as long as Private Colleges, in the Next Year” The Chronicle of Higher Education, January 16, 2009 How can we get the media to help Underst in addition to Journalism 101 Stop using only extreme negative stories Sad stories about individuals Student loan crisis Loan default Drop out due to cost Change dreams Tuition announcements Endowment Budget freezes No more capacity Rankings

How can we get the media to help (continued) Publish good-news stories Much more representative Importance of an educated citizenry as long as individual in addition to society Investment not purchase Global competitiveness Why not on-time graduation: another view of cost of college Remediation Lack of academic support Capacity Cost of college as long as not finishing on time Stay focused Mission Long term Keep perspective Look as long as efficiencies Fitter in addition to trimmer Time often heals much “What we have be as long as e us are some breathtaking opportunities disguised as insoluble problems” John William Gardner

McBride, Sarah Wall Street Journal - Los Angeles Bureau Digital Entertainment Distribution Reporter www.phwiki.com

McBride, Sarah Digital Entertainment Distribution Reporter

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When Atoms Meet Bonding Forces Electron – electron repulsive as long as ces Nucleus – nu

When Atoms Meet Bonding Forces Electron – electron repulsive as long as ces Nucleus – nu www.phwiki.com

When Atoms Meet Bonding Forces Electron – electron repulsive as long as ces Nucleus – nu

Rico, Fred, Interim Program Director has reference to this Academic Journal, PHwiki organized this Journal When Atoms Meet Bonding Forces Electron – electron repulsive as long as ces Nucleus – nucleus repulsive as long as ces Electron – necleus attractive as long as ces Bonds Forces that hold groups of atoms together in addition to make them function as a unit. Metals in addition to Nonmetals

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Types of Chemical Bonding 1. Metal with nonmetal: electron transfer in addition to ionic bonding Three models of chemical bonding Electron transfer Ionic Types of Chemical Bonding 1. Metal with nonmetal: electron transfer in addition to ionic bonding 2. Nonmetal with nonmetal: electron sharing in addition to covalent bonding

Three models of chemical bonding Electron transfer Electron sharing Ionic Covalent Types of Chemical Bonding 1. Metal with nonmetal: electron transfer in addition to ionic bonding 2. Nonmetal with nonmetal: electron sharing in addition to covalent bonding 3. Metal with metal: electron pooling in addition to metallic bonding Three models of chemical bonding Electron transfer Electron sharing Electron pooling Ionic Covalent Metallic

9.1 The outer shell electrons of an atom Participate in chemical bonding Valence Electrons G. N. Lewis Developed the idea in 1902. Lewis Structures Nitrogen, N, is in Group 5A in addition to there as long as e has 5 valence electrons. Lewis Dot Symbols

Lewis Dot Symbols The Octet Rule Chemical compounds tend to as long as m so that each atom, by gaining, losing, or sharing electrons, has eight electrons in its highest occupied energy level. The same number of electrons as in the nearest noble gas The first exception to this is hydrogen, which follows the duet rule. The second exception is helium which does not as long as m bonds because it is already “full” with its two electrons Ionic Bond 1s22s1 1s22s22p5 1s2 1s22s22p6 [He] [Ne]

Lattice energy (E) increases as Q increases in addition to /or as r decreases. r F < r Cl Electrostatic (Lattice) Energy Q+ is the charge on the cation Q- is the charge on the anion r is the distance between the ions Lattice energy (E) is the energy required to completely separate one mole of a solid ionic compound into gaseous ions. A chemical bond in which two or more electrons are shared by two atoms. Lewis structure of F2 Covalent Bond Distribution of electron density of H2 H H #NAME? Electronegativities (EN) The ability of an atom in a molecule to attract shared electrons to itself Classification of Bonds Difference in EN Bond Type 0 Covalent 2 Ionic 0 < in addition to <2 Polar Covalent Cs – 0.7 Cl – 3.0 3.0 – 0.7 = 2.3 Ionic H – 2.1 S – 2.5 2.5 – 2.1 = 0.4 Polar Covalent N – 3.0 N – 3.0 3.0 – 3.0 = 0 Covalent Classification of Bonds Rico, Fred KWFM-AM Interim Program Director www.phwiki.com

Draw skeletal structure of compound showing what atoms are bonded to each other. Put least electronegative element in the center. Count total number of valence e-. Add 1 as long as each negative charge. Subtract 1 as long as each positive charge. Use one pair of electrons to as long as m a bond (a single line) between each pair of atoms. Arrange the remaining electrons to satisfy an octet as long as all atoms (duet as long as H), starting from outer atoms. If a central atom does not have an octet, move in lone pairs to as long as m double or triple bonds on the central atom as needed. Rules as long as Writing Lewis Structures Step 1 – N is less electronegative than F, put N in center Step 2 – Count valence electrons N – 5 (2s22p3) in addition to F – 7 (2s22p5) 5 + (3 x 7) = 26 valence electrons Step 3 – Draw single bonds between N in addition to F atoms. Step 4 – Arrange remaining 20 electrons to complete octets Step 1 – C is less electronegative than O, put C in center Step 2 – Count valence electrons C – 4 (2s22p2) in addition to O – 6 (2s22p4) -2 charge – 2e- 4 + (3 x 6) + 2 = 24 valence electrons Step 3 – Draw single bonds between C in addition to O atoms Step 4 – Arrange remaining 18 electrons to complete octets Step 5 – The central C has only 6 electrons. Form a double bond.

More than one valid Lewis structures can be written as long as a particular molecule The actual structure of the carbonate ion is an average of the three resonance structures Resonance Exceptions to the Octet Rule The Incomplete Octet BeH2 BF3 Exceptions to the Octet Rule Odd-Electron Molecules NO The Exp in addition to ed Octet (central atom with principal quantum number n > 2) SF6

Lab 1 Acknowledgment Some images, animation, in addition to material have been taken from the following sources: Chemistry, Zumdahl, Steven S.; Zumdahl, Susan A.; Houghton Mifflin Co., 6th Ed., 2003; supplements as long as the instructor General Chemistry: The Essential Concepts, Chang, Raymon; McGraw-Hill Co. Inc., 4th Ed., 2005; supplements as long as the instructor Principles of General Chemistry, Silberberg, Martin; McGraw-Hill Co. Inc., 1st Ed., 2006; supplements as long as the instructor NIST WebBook: http://webbook.nist.gov/ http://www.lsbu.ac.uk/water/vibrat.html http://en.wikipedia.org/wiki/Caffeine http://www.wilsonhs.com/SCIENCE/CHEMISTRY/MRWILSON/Unit%204%20Chemical%2 0Bonding%20Powerpoint1.ppt

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Language Technologies Institute, School of Computer Science, Carnegie Mellon University Yajie Miao Hao Zhang Florian Metze Distributed Learning of Multilingual DNN Feature Extractors using GPUs

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Language Technologies Institute, School of Computer Science, Carnegie Mellon University Yajie Miao Hao Zhang Florian Metze Distributed Learning of Multilingual DNN Feature Extractors using GPUs

Christopher Newport University, VA has reference to this Academic Journal, Distributed Learning of Multilingual DNN Feature Extractors using GPUs Yajie Miao Hao Zhang Florian Metze Language Technologies Institute, School of Computer Science, Carnegie Mellon University ymiao, haoz1, fmetze@cs.cmu Introduction As the state of the art in consideration of speech recognition, DNNs are particularly suitable in consideration of multi-lingual in addition to cross-lingual ASR. A multilingual DNN is trained over a group of languages, alongside hidden layers shared across languages. Given a new language, the shared hidden layers act as a deep feature extractor. Goal. With multiple GPUs available, we aim so that parallelize the learning of the feature extractor over large amounts of multilingual training data. Highlight. We study how parallelization affects the quality of feature extractors. Feature extractor learning is robust so that infrequent thread synchronization. Thus, time-synchronous model averaging achieves good speed-up. 1. Two evaluation conditions on the BABEL corpus DistLang: Distribution by Languages Preliminary Evaluation Source DistModel: Distribution by Model 1. Basic Idea 2. Two Methods in consideration of Feature Fusion With larger averaging interval, we obtain monotonically better speed-up 2000 seems so that be a good tradeoff point Applied so that monolingual DNN on Tagalog FullLP. The enlarged WER degradation shows that DistModel is particularly useful in consideration of multilingual DNN training Larger-Scale Evaluation Acknowledgements This work was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Defense U.S. Army Research Laboratory (DoD / ARL) contract number W911NF-12-C-0015. The U.S. Government is authorized so that reproduce in addition to distribute reprints in consideration of Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views in addition to conclusions contained herein are those of the authors in addition to should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoD/ARL, or the U.S. Government. Training data of each language is partitioned evenly across the GPUs. After a specified number of mini-batches (averaging interval), feature extractors from the individual GPUs are averaged into a unified model. The averaged parameters are sent back so that each GPU as the new starting model in consideration of the subsequent training. This is a time-synchronous method. However, on this particular feature learning task, DistModel is robust so that large averaging interval up so that 2000 mini-batches. Each GPU trains the DNN model as a language-specific feature extractor. On the target language, each speech frame is fed into these separate extractors. The feature vectors are fused into a single feature representation. FeatConcat: concatenate outputs from the language-specific feature extractors into a single vector Datasets in addition to Experimental Setup Source Languages Target Language FeatMix: fuse the feature vectors via a linear weighted combination. The combined feature vector can be computed as an weights in consideration of features from the n-th extractor b bias (vector) 3. Metrics WERs(%) of the hybrid DNN model on a 2-hour testing set of the target lang Speed-up: the ratio of the training time taken using a single GPU so that the time using multiple GPUs 3. Pros & Cons No communication cost ? perfect speed-up Inclusion of new source languages is easy ? no need so that retrain from scratch The number of GPUs is hardcoded by the number of source languages WER% in addition to Speed-up of DistModel as averaging interval increases WER% of DistLang alongside the two feature fusion methods Always gives ~3.0 speed-up Worse than DistModel partly because of language dependence FeatConcat is slightly better than FeatMix WER% in addition to Speed-up of DistModel as # of GPUs increases Consistent acceleration, although the improvement is not linear Pooling more GPUs degrades WERs on the target language This degradation might be mitigated by further optimization averaging interval on 3 GPUs Lang 1 Lang 2 Lang 3 Lang 1 Lang 2 Lang 3 GPU #1 Extractor 1 Averaged Extractor 90 Hours 90 Hours 90 Hours Lang 1 Lang 2 Lang 3 GPU #2 Extractor 2 Lang 1 Lang 2 Lang 3 GPU #3 Extractor 3 averaging interval 30 Hrs 30 Hrs 30 Hrs Lang 1 input Lang 2 input Lang 3 input Lang 1 softmax Lang 2 softmax Lang 3 softmax ? Target Target Lang ? ? Hybrid DNN Feature Extractor LANG #1 LANG #1 GPU #1 GPU #2 LANG #1 LANG #1 + Target DNN Tagalog – IARPA-babel106-v0.2f Cantonese – IARPA-babel101-v0.4c Turkish – IARPA-babel105b-v0.4 Pashto – IARPA-babel104b-v0.4aY Vietnamese – IARPA-babel101-v0.4c Bengali – IARPA-babel103b-v0.4b ?. ?. 2. Protocol. We measure the WERs on the target language, alongside the identical DNN architecture in consideration of various feature extractors.

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CPP Fall 2014 IRB Training Session eIRB Instructions Communicating With Your Assigned Reviewer Viewing in addition to Responding so that Comments Approved Protocol Thank you!

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Previous Research Introduction Automaticity of Musical Processing

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Previous Research Introduction Automaticity of Musical Processing

Christopher Newport University, US has reference to this Academic Journal, Automaticity of Musical ProcessingDanielle DeVincentis, Melissa Poole, in addition to Zach ReedHanover CollegeIntroductionThe Stroop Effect (Stroop, 1938)Automaticity of Movement (Solso, 2008)Similar effect when playing instrumentCan musical literacy become automatic?Previous ResearchIrrelevant musical notation causes Stroop effect (Stewart, 2005)

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Previous Research, Cont.Incongruence between auditory in addition to visual stimuli leads so that inaccuracy (W”llner, Halfpenny, Ho, & Kurosawa, 2003).HypothesisWhen presented alongside incongruent visual in addition to auditory stimuli, participants who were music-literate would: have slower reaction times. be less accurate than participants who could not read music.MethodParticipantsN=3817 male, 21 female18-22 years old, alongside one 53 year old outlier38 Caucasian25 could read music, 13 could not.

Stimuli6 simple tunes?Mary Had A Little Lamb?, ?Frere Jacques?, ?Jesus Loves Me?, ?Ode so that Joy?, ?Yankee Doodle?, or ?Twinkle, Twinkle, Little Star.?Musical staff alongside congruent or incongruent notesEquipmentGateway Model FPD1565 LCD MonitorGateway Model E4300 ComputerHeadphonesDemographic SurveyJava Program (Krantz, 2010)Procedure Within-Subjects design25 trials per conditionAfter participants completed one condition, researchers prepared the computer in consideration of the next conditionIndicate tune being playedRecorded average reaction time in addition to accuracy in consideration of each condition

. . . . . . . .

ResultsDiscussionToo much error so that make conclusionsNot a problem in traditional Stroop.Possible ExplanationContinuum of Automaticity (MacLeod & Dunbar, 1988)Variance in level of training or ability causes varying levels of automaticity.

Future DirectionsFaster reaction taskInteractive visual stimuli (Stewart, Walsh, & Frith, 2004; Stewart, 2005)Association between musical notation in addition to physical actions (Stewart, Walsh &, Frith, 2004)Phonological loop in consideration of music reading?Questions?

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