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1. Toward Personalizing Students' Education with Crowdsourced Tutoring

   https://doi.org/10.1145/3430895.3460130  

   Prihar, Ethan; Patikorn, Thanaporn; Botelho, Anthony; Sales, Adam; Heffernan, Neil (June 2021, Lerning @ Scale 2021)

   null (Ed.)

   A s m or e e d u c at or s i nt e gr at e t h eir c urri c ul a wit h o nli n e l e ar ni n g, it i s e a si er t o cr o w d s o ur c e c o nt e nt fr o m t h e m. Cr o w ds o ur c e d t ut ori n g h a s b e e n pr o v e n t o r eli a bl y i n cr e a s e st u d e nt s’ n e xt pr o bl e m c orr e ct n e s s. I n t hi s w or k, w e c o n fir m e d t h e fi n di n g s of a pr e vi o u s st u d y i n t hi s ar e a, wit h str o n g er c o n fi d e n c e m ar gi n s t h a n pr e vi o u sl y, a n d r e v e al e d t h at o nl y a p orti o n of cr o w d s o ur c e d c o nt e nt cr e at or s h a d a r eli a bl e b e n e fit t o st ud e nt s. F urt h er m or e, t hi s w or k pr o vi d e s a m et h o d t o r a n k c o nt e nt cr e at or s r el ati v e t o e a c h ot h er, w hi c h w a s u s e d t o d et er mi n e w hi c h c o nt e nt cr e at or s w er e m o st eff e cti v e o v er all, a n d w hi c h c o nt e nt cr e at or s w er e m o st eff e cti v e f or s p e ci fi c gr o u p s of st u d e nt s. W h e n e x pl ori n g d at a fr o m Te a c h er A SSI S T, a f e at ur e wit hi n t h e A S SI S T m e nt s l e ar ni n g pl atf or m t h at cr o w d s o ur c e s t ut ori n g fr o m t e a c h er s, w e f o u n d t h at w hil e o v erall t hi s pr o gr a m pr o vi d e s a b e n e fit t o st u d e nt s, s o m e t e a c h er s cr e at e d m or e eff e cti v e c o nt e nt t h a n ot h er s. D e s pit e t hi s fi n di n g, w e di d n ot fi n d e vi d e n c e t h at t h e eff e cti v e n e s s of c o nt e nt r eli a bl y v ari e d b y st u d e nt k n o wl e d g e-l e v el, s u g g e sti n g t h at t h e c o nt e nt i s u nli k el y s uit a bl e f or p er s o n ali zi n g i n str u cti o n b a s e d o n st u d e nt k n o wl e d g e al o n e. T h e s e fi n di n g s ar e pr o mi si n g f or t h e f ut ur e of cr o w d s o ur c e d t ut ori n g a s t h e y h el p pr o vi d e a f o u n d ati o n f or a s s e s si n g t h e q u alit y of cr o w d s o ur c e d c o nt e nt a n d i n v e sti g ati n g c o nt e nt f or o p p ort u niti e s t o p er s o n ali z e st u d e nt s’ e d u c ati o n. 

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2. Energy Harvesting Performance of a Flapping Foil with Leading Edge Motion

   Prier, M. (April 2019, Dissertation)

   F or c e d at a f or a fl a p pi n g f oil e n er g y h ar v e st er wit h a cti v e l e a di n g e d g e m oti o n o p er ati n g i n t h e l o w r e d u c e d fr e q u e n c y r a n g e i s c oll e ct e d t o d et er mi n e h o w l e a di n g e d g e m oti o n aff e ct s e n er g y h ar v e sti n g p erf or m a n c e. T h e f oil pi v ot s a b o ut t h e mi dc h or d a n d o p er at e s i n t h e l o w r e d u c e d fr e q u e n c y r a n g e of 𝑓𝑓 𝑓𝑓 / 𝑈𝑈 ∞ = 0. 0 6 , 0. 0 8, a n d 0. 1 0 wit h 𝑅𝑅 𝑅𝑅 = 2 0 ,0 0 0 − 3 0 ,0 0 0 , wit h a pit c hi n g a m plit u d e of 𝜃𝜃 0 = 7 0 ∘ , a n d a h e a vi n g a m plit u d e of ℎ 0 = 0. 5 𝑓𝑓 . It i s f o u n d t h at l e a di n g e d g e m oti o n s t h at r e d u c e t h e eff e cti v e a n gl e of att a c k e arl y t h e str o k e w or k t o b ot h i n cr e a s e t h e lift f or c e s a s w ell a s s hift t h e p e a k lift f or c e l at er i n t h e fl a p pi n g str o k e. L e a di n g e d g e m oti o n s i n w hi c h t h e eff e cti v e a n gl e of att a c k i s i n cr e a s e d e arl y i n t h e str o k e s h o w d e cr e a s e d p erf or m a n c e. I n a d diti o n a di s cr et e v ort e x m o d el wit h v ort e x s h e d di n g at t h e l e a di n g e d g e i s i m pl e m e nt f or t h e m oti o n s st u di e d; it i s f o u n d t h at t h e m e c h a ni s m f or s h e d di n g at t h e l e a di n g e d g e i s n ot a d e q u at e f or t hi s p ar a m et er r a n g e a n d t h e m o d el c o n si st e ntl y o v er pr e di ct s t h e a er o d y n a mi c f or c e s. 

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3. Individual differences and long-term consequences of tDCS augmented cognitive training

   Katz, B.; Au, J.; Buschkuehl, M.; Abagis, T.; Zabel, C.; Jaeggi, S.; Jonides, J. (October 2017, Journal of cognitive neuroscience)

   A gr e at d e al of i nt er e st s urr o u n d s t h e u s e of tr a n s cr a ni al dir e ct c urr e nt sti m ul ati o n (t D C S) t o a u g m e nt c o g niti v e tr ai ni n g. H o w e v er, eff e ct s ar e i n c o n si st e nt a cr o s s st u di e s, a n d m et aa n al yti c e vi d e n c e i s mi x e d, e s p e ci all y f o r h e alt h y, y o u n g a d ult s. O n e m aj or s o ur c e of t hi s i n c o n si st e n c y i s i n di vi d u al diff er e n c e s a m o n g t h e p arti ci p a nt s, b ut t h e s e diff er e n c e s ar e r ar el y e x a mi n e d i n t h e c o nt e xt of c o m bi n e d tr ai ni n g/ sti m ul ati o n st u di e s. I n a d diti o n, it i s u n cl e ar h o w l o n g t h e eff e ct s of sti m ul ati o n l a st, e v e n i n s u c c e s sf ul i nt er v e nti o n s. S o m e st u di e s m a k e u s e of f oll o w- u p a s s e s s m e nt s, b ut v er y f e w h a v e m e a s ur e d p erf or m a n c e m or e t h a n a f e w m o nt hs aft er a n i nt er v e nti o n. H er e, w e utili z e d d at a fr o m a pr e vi o u s st u d y of t D C S a n d c o g niti v e tr ai ni n g [ A u, J., K at z, B., B u s c h k u e hl, M., B u n arj o, K., S e n g er, T., Z a b el, C., et al. E n h a n ci n g w or ki n g m e m or y tr ai ni n g wit h tr a n scr a ni al dir e ct c urr e nt sti m ul ati o n. J o u r n al of C o g niti v e N e u r os ci e n c e, 2 8, 1 4 1 9 – 1 4 3 2, 2 0 1 6] i n w hi c h p arti ci p a nts tr ai n e d o n a w or ki n g m e m or y t as k o v er 7 d a y s w hil e r e c ei vi n g a cti v e or s h a m t D C S. A n e w, l o n g er-t er m f oll o w- u p t o a ss es s l at er p erf or m a n c e w a s c o n d u ct e d, a n d a d diti o n al p arti ci p a nt s w er e a d d e d s o t h at t h e s h a m c o n diti o n w a s b ett er p o w er e d. W e a s s e s s e d b a s eli n e c o g niti v e a bilit y, g e n d er, tr ai ni n g sit e, a n d m oti v ati o n l e v el a n d f o u n d si g nifi c a nt i nt er a cti o ns b et w e e n b ot h b as eli n e a bilit y a n d m oti v ati o n wit h c o n diti o n ( a cti v e or s h a m) i n m o d els pr e di cti n g tr ai ni n g g ai n. I n a d diti o n, t h e i m pr o v e m e nt s i n t h e a cti v e c o nditi o n v er s u s s h a m c o n diti o n a p p e ar t o b e st a bl e e v e n a s l o n g a s a y e ar aft er t h e ori gi n al i nt er v e nti o n. ■ 

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4. Instantaneous everywhere-blowup of parabolic SPDEs

   https://doi.org/10.1007/s00440-024-01263-7  

   Foondun, Mohammud; Khoshnevisan, Davar; Nualart, Eulalia (October 2024, Probability Theory and Related Fields)

   Abstract We consider the following stochastic heat equation$$\begin{aligned} \partial _t u(t,x) = \tfrac{1}{2} \partial ^2_x u(t,x) + b(u(t,x)) + \sigma (u(t,x)) {\dot{W}}(t,x), \end{aligned}$$ $\begin{array}{c}{\partial }_{t}u(t,x)=\frac{1}{2}{\partial }_x^{2}u(t,x)+b\left(u(t,x)\right)+\sigma \left(u(t,x)\right)\dot{W}(t,x),\end{array}$defined for$$(t,x)\in (0,\infty )\times {\mathbb {R}}$$ $(t,x)\in (0,\infty )\times R$, where$${\dot{W}}$$ $\dot{W}$denotes space-time white noise. The function$$\sigma $$ $\sigma$is assumed to be positive, bounded, globally Lipschitz, and bounded uniformly away from the origin, and the functionbis assumed to be positive, locally Lipschitz and nondecreasing. We prove that the Osgood condition$$\begin{aligned} \int _1^\infty \frac{\textrm{d}y}{b(y)}<\infty \end{aligned}$$ $\begin{array}{c}{\int }_1^{\infty }\frac{\text{d}y}{b\left(y\right)}<\infty \end{array}$implies that the solution almost surely blows up everywhere and instantaneously, In other words, the Osgood condition ensures that$$\textrm{P}\{ u(t,x)=\infty \quad \hbox { for all } t>0 \hbox { and } x\in {\mathbb {R}}\}=1.$$ $\text{P}\left\{u\right(t,x)=\infty \text{for all}t>0\text{and}x\in R\}=1.$The main ingredients of the proof involve a hitting-time bound for a class of differential inequalities (Remark 3.3), and the study of the spatial growth of stochastic convolutions using techniques from the Malliavin calculus and the Poincaré inequalities that were developed in Chen et al. (Electron J Probab 26:1–37, 2021, J Funct Anal 282(2):109290, 2022). 

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5. The Brown measure of the free multiplicative Brownian motion

   https://doi.org/10.1007/s00440-022-01142-z  

   Driver, Bruce K.; Hall, Brian; Kemp, Todd (October 2022, Probability Theory and Related Fields)

   Abstract The free multiplicative Brownian motion $$b_{t}$$ b t is the large- N limit of the Brownian motion on $$\mathsf {GL}(N;\mathbb {C}),$$ GL ( N ; C ) , in the sense of $$*$$ ∗ -distributions. The natural candidate for the large- N limit of the empirical distribution of eigenvalues is thus the Brown measure of $$b_{t}$$ b t . In previous work, the second and third authors showed that this Brown measure is supported in the closure of a region $$\Sigma _{t}$$ Σ t that appeared in the work of Biane. In the present paper, we compute the Brown measure completely. It has a continuous density $$W_{t}$$ W t on $$\overline{\Sigma }_{t},$$ Σ ¯ t , which is strictly positive and real analytic on $$\Sigma _{t}$$ Σ t . This density has a simple form in polar coordinates: $$\begin{aligned} W_{t}(r,\theta )=\frac{1}{r^{2}}w_{t}(\theta ), \end{aligned}$$ W t ( r , θ ) = 1 r 2 w t ( θ ) , where $$w_{t}$$ w t is an analytic function determined by the geometry of the region $$\Sigma _{t}$$ Σ t . We show also that the spectral measure of free unitary Brownian motion $$u_{t}$$ u t is a “shadow” of the Brown measure of $$b_{t}$$ b t , precisely mirroring the relationship between the circular and semicircular laws. We develop several new methods, based on stochastic differential equations and PDE, to prove these results. 

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