Manipulating the transmission matrix of scattering media for nonlinear imaging beyond the memory effect

The measurement of the Transmission Matrix (TM) of a scattering medium is of great interest for imaging. It can be acquired directly by interferometry using an internal reference wavefront. Unfortunately, internal reference fields are scattered by the medium which results in a speckle that makes the TM measurement heterogeneous across the output field of view. We demonstrate how to correct for this effect using the intrinsic properties of the TM. For thin scattering media, we exploit the memory effect of the medium and the reference speckle to create a corrected TM. For highly scattering media where the memory effect is negligible, we use complementary reference speckles to compose a new TM, not compromised by the speckled reference anymore. Using this correction, we demonstrate large field of view second harmonic generation imaging through thick biological media

I know it is not real (and that matters):Media awareness vs. presence shape the VR experience

Inspired by the widely recognized idea that in VR/XR, not only presence but also encountered plausibility is relevant (Slater, 2009), we propose a general psychological parallel processing account to explain users' VR and XR experience. The model adopts a broad psychological view by building on interdisciplinary literature on the dualistic nature of perceiving and experiencing (mediated) representations. It proposes that perceptual sensations like presence are paralleled by users' belief that "this is not really happening", which we refer to as media awareness. We review the developmental underpinnings of basic media awareness, and argue that it is triggered in users’ conscious exposure to VR/XR. During exposure the salience of media awareness can vary dynamically due to factors like encountered sensory and semantic (in)consistencies. Our account sketches media awareness and presence as two parallel processes that together define a situation as a media exposure situation. We also review potential joint effects on subsequent psychological and behavioral responses that characterize the user experience in VR/XR. We conclude the article with a programmatic outlook on testable assumptions and open questions for future research

Online social engagement, depression, and anxiety among older adults

As opportunities for social interactions proliferate online, questions arise as to how engagement in such activities may relate to mental health. Given older adults’ shrinking networks and increasing use of information and communication technologies (ICTs), online interactions could offer alternatives for connections that could ultimately benefit older users’ mental health. This article examines associations between older adults’ online social engagement and their mental health. Using data from an online survey of older adults ages 60+, we find positive and negative associations between different forms of online social engagement and anxiety. In terms of depression, two forms of online social engagement showed positive associations with this mental health indicator. Our results can help explain inconclusive findings of previous research on ICT use and mental health by looking at how specific online social activities relate to mental health

Linguistic Overhypotheses in Category Learning:Explaining the Label Advantage Effect

When learning to partition the world into categories, peoplerely on a set of assumptions (overhypotheses) about possi-ble category structures. Here we propose that the nature ofthese overhypotheses depends on the presence of a verbal la-bel associated with a given category. We describe a computa-tional model that demonstrates how labels can either acceler-ate or hinder category learning, depending on whether or notthe prior beliefs imposed by their presence align with the truecategory structure. This account provides an explanation forthe phenomena described in prior experimental work (Lupyan,Rakison, & McClelland, 2007; Brojde, Porter, & Colunga,2011) that have remained unexplained by other models. Basedon these results, we argue that the overhypothesis theory of la-bel effects provides a way to formalize and quantify the effectof language on category learning and to develop a more precisedelineation between linguistic and non-linguistic thought

Reasoning about Measures of Unmeasurable Sets

International audienceIn a variety of reasoning tasks, one estimates the likelihood of events by means of volumes of sets they define. Such sets need to be measurable, which is usually achieved by putting bounds, sometimes ad hoc, on them. We address the question how unbounded or unmeasurable sets can be measured nonetheless. Intuitively, we want to know how likely a randomly chosen point is to be in a given set, even in the absence of a uniform distribution over the entire space. To address this, we follow a recently proposed approach of taking intersection of a set with balls of increasing radius, and defining the measure by means of the asymptotic behavior of the proportion of such balls taken by the set. We show that this approach works for every set definable in first-order logic with the usual arithmetic over the reals (addition, multiplication, exponentiation, etc.), and every uniform measure over the space, of which the usual Lebesgue measure (area, volume, etc.) is an example. In fact we establish a correspondence between the good asymptotic behavior and the finiteness of the VC dimension of definable families of sets. Towards computing the measure thus defined, we show how to avoid the asymptotics and characterize it via a specific subset of the unit sphere. Using definability of this set, and known techniques for sampling from the unit sphere, we give two algorithms for estimating our measure of unbounded unmeasurable sets, with deterministic and probabilistic guarantees, the latter being more efficient. Finally we show that a discrete analog of this measure exists and is similarly well-behaved