Excessive noise as a test for many-body localization

Recent experimental reports suggested the existence of a finite-temperature insulator in the vicinity of the superconductor-insulator transition. The rapid decay of conductivity over a narrow temperature range was theoretically linked to both a finite-temperature transition to a many-body-localized state, and to a charge-Berezinskii-Kosterlitz-Thouless transition. Here we report of low-frequency noise measurements of such insulators to test for many-body localization. We observed a huge enhancement of the low-temperatures noise when exceeding a threshold voltage for nonlinear conductivity and discuss our results in light of the theoretical models

Evidence for a Finite Temperature Insulator

In superconductors the zero-resistance current-flow is protected from dissipation at finite temperatures (T) by virtue of the short-circuit condition maintained by the electrons that remain in the condensed state. The recently suggested finite-T insulator and the "superinsulating" phase are different because any residual mechanism of conduction will eventually become dominant as the finite-T insulator sets-in. If the residual conduction is small it may be possible to observe the transition to these intriguing states. We show that the conductivity of the high magnetic-field insulator terminating superconductivity in amorphous indium-oxide exhibits an abrupt drop, and seem to approach a zero conductance at T<0.04 K. We discuss our results in the light of theories that lead to a finite-T insulator

Emotion Goals: What do Sexual Offenders Want to Feel?

Sexual offenders typically experience more negative emotions and greater difficulties in regulating emotions than non-offenders. However, limited data exist on what sexual offenders want to feel (i.e., their emotion goals). Notably, emotion goals play a key role in emotion regulation and contribute to emotional experience. The present study tested whether sexual offenders (N = 31) reported higher scores for negative emotion goals and lower scores for positive emotion goals, compared with general offenders (N = 26) and non-offenders (N = 26). In addition, we tested whether sexual offenders differed from the other two groups in their perceived pleasantness and perceived utility of emotions. Sexual offenders reported greater scores for the emotion goal of sadness, and lower scores for the emotion goal of excitement, compared with both general offenders and non-offenders. State and trait levels of these emotions could not fully account for these differences. Furthermore, sexual offenders reported lower perceived pleasantness for sadness than general offenders and lower perceived pleasantness for excitement compared with both other groups. Finally, sexual offenders reported greater perceived utility of sadness than non-offenders. These novel findings and their implications for research and interventions are discussed in the context of sexual offenders' emotional dysfunction

Grover's Quantum Search Algorithm and Diophantine Approximation

In a fundamental paper [Phys. Rev. Lett. 78, 325 (1997)] Grover showed how a quantum computer can find a single marked object in a database of size N by using only O(N^{1/2}) queries of the oracle that identifies the object. His result was generalized to the case of finding one object in a subset of marked elements. We consider the following computational problem: A subset of marked elements is given whose number of elements is either M or K, M<K, our task is to determine which is the case. We show how to solve this problem with a high probability of success using only iterations of Grover's basic step (and no other algorithm). Let m be the required number of iterations; we prove that under certain restrictions on the sizes of M and K the estimation m < (2N^{1/2})/(K^{1/2}-M^{1/2}) obtains. This bound sharpens previous results and is known to be optimal up to a constant factor. Our method involves simultaneous Diophantine approximations, so that Grover's algorithm is conceptualized as an orbit of an ergodic automorphism of the torus. We comment on situations where the algorithm may be slow, and note the similarity between these cases and the problem of small divisors in classical mechanics.Comment: 8 pages, revtex, Title change

Mask exposure during COVID-19 changes emotional face processing

Faces are one of the key ways that we obtain social information about others. They allow people to identify individuals, understand conversational cues, and make judgements about others’ mental states. When the COVID-19 pandemic hit the United States, widespread mask-wearing practices were implemented, causing a shift in the way Americans typically interact. This introduction of masks into social exchanges posed a potential challenge—how would people make these important inferences about others when a large source of information was no longer available? We conducted two studies that investigated the impact of mask exposure on emotion perception. In particular, we measured how participants used facial landmarks (visual cues) and the expressed valence and arousal (affective cues), to make similarity judgements about pairs of emotion faces. Study 1 found that in August 2020, participants with higher levels of mask exposure used cues from the eyes to a greater extent when judging emotion similarity than participants with less mask exposure. Study 2 measured participants’ emotion perception in both April and September 2020 –before and after widespread mask adoption—in the same group of participants to examine changes in the use of facial cues over time. Results revealed an overall increase in the use of visual cues from April to September. Further, as mask exposure increased, people with the most social interaction showed the largest increase in the use of visual facial cues. These results provide evidence that a shift has occurred in how people process faces such that the more people are interacting with others that are wearing masks, the more they have learned to focus on visual cues from the eye area of the face

Conditional Score-Based Reconstructions for Multi-contrast MRI

Magnetic resonance imaging (MRI) exam protocols consist of multiple contrast-weighted images of the same anatomy to emphasize different tissue properties. Due to the long acquisition times required to collect fully sampled k-space measurements, it is common to only collect a fraction of k-space for some, or all, of the scans and subsequently solve an inverse problem for each contrast to recover the desired image from sub-sampled measurements. Recently, there has been a push to further accelerate MRI exams using data-driven priors, and generative models in particular, to regularize the ill-posed inverse problem of image reconstruction. These methods have shown promising improvements over classical methods. However, many of the approaches neglect the multi-contrast nature of clinical MRI exams and treat each scan as an independent reconstruction. In this work we show that by learning a joint Bayesian prior over multi-contrast data with a score-based generative model we are able to leverage the underlying structure between multi-contrast images and thus improve image reconstruction fidelity over generative models that only reconstruct images of a single contrast

Accelerated Motion Correction with Deep Generative Diffusion Models

Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality, but unfortunately suffers from long scan times which, aside from increasing operational costs, can lead to image artifacts due to patient motion. Motion during the acquisition leads to inconsistencies in measured data that manifest as blurring and ghosting if unaccounted for in the image reconstruction process. Various deep learning based reconstruction techniques have been proposed which decrease scan time by reducing the number of measurements needed for a high fidelity reconstructed image. Additionally, deep learning has been used to correct motion using end-to-end techniques. This, however, increases susceptibility to distribution shifts at test time (sampling pattern, motion level). In this work we propose a framework for jointly reconstructing highly sub-sampled MRI data while estimating patient motion using diffusion based generative models. Our method does not make specific assumptions on the sampling trajectory or motion pattern at training time and thus can be flexibly applied to various types of measurement models and patient motion. We demonstrate our framework on retrospectively accelerated 2D brain MRI corrupted by rigid motion