Functional evolution of quantum cylindrical waves

Kucha{\v{r}} showed that the quantum dynamics of (1 polarization) cylindrical wave solutions to vacuum general relativity is determined by that of a free axially-symmetric scalar field along arbitrary axially-symmetric foliations of a fixed flat 2+1 dimensional spacetime. We investigate if such a dynamics can be defined {\em unitarily} within the standard Fock space quantization of the scalar field. Evolution between two arbitrary slices of an arbitrary foliation of the flat spacetime can be built out of a restricted class of evolutions (and their inverses). The restricted evolution is from an initial flat slice to an arbitrary (in general, curved) slice of the flat spacetime and can be decomposed into (i) `time' evolution in which the spatial Minkowskian coordinates serve as spatial coordinates on the initial and the final slice, followed by (ii) the action of a spatial diffeomorphism of the final slice on the data obtained from (i). We show that although the functional evolution of (i) is unitarily implemented in the quantum theory, generic spatial diffeomorphisms of (ii) are not. Our results imply that a Tomanaga-Schwinger type functional evolution of quantum cylindrical waves is not a viable concept even though, remarkably, the more limited notion of functional evolution in Kucha{\v{r}}'s `half parametrized formalism' is well-defined.Comment: Replaced with published versio

Airy functions over local fields

Airy integrals are very classical but in recent years they have been generalized to higher dimensions and these generalizations have proved to be very useful in studying the topology of the moduli spaces of curves. We study a natural generalization of these integrals when the ground field is a non-archimedean local field such as the field of p-adic numbers. We prove that the p-adic Airy integrals are locally constant functions of moderate growth and present evidence that the Airy integrals associated to compact p-adic Lie groups also have these properties.Comment: Minor change

Low Latency Prefix Accumulation Driven Compound MAC Unit for Efficient FIR Filter Implementation

135–138This article presents hierarchical single compound adder-based MAC with assertion based error correction for speculation variations in the prefix addition for FIR filter design. The VLSI implementation of approximation in prefix adder results show a significant delay and complexity reductions, all this at the cost of latency measures when speculation fails during carry propagation, which is the main reason preventing the use of speculation in parallel-prefix adders in DSP applications. The speculative adder which is based on Han Carlson parallel prefix adder structure accomplishes better reduction in latency. Introducing a structured and efficient shift-add technique and explore latency reduction by incorporating approximation in addition. The improvements made in terms of reduction in latency and merits in performance by the proposed MAC unit are showed through the synthesis done by FPGA hardware. Results show that proposed method outpaces both formerly projected MAC designs using multiplication methods for attaining high speed

The parameterized complexity of some geometric problems in unbounded dimension

We study the parameterized complexity of the following fundamental geometric problems with respect to the dimension $d$: i) Given $n$ points in \Rd, compute their minimum enclosing cylinder. ii) Given two $n$-point sets in \Rd, decide whether they can be separated by two hyperplanes. iii) Given a system of $n$ linear inequalities with $d$ variables, find a maximum-size feasible subsystem. We show that (the decision versions of) all these problems are W[1]-hard when parameterized by the dimension $d$. %and hence not solvable in ${O}(f(d)n^c)$ time, for any computable function $f$ and constant $c$ %(unless FPT=W[1]). Our reductions also give a $n^{\Omega(d)}$-time lower bound (under the Exponential Time Hypothesis)

Noise-Vocoded Sentence Recognition and the Use of Context in Older and Younger Adult Listeners

Purpose: When listening to speech under adverse conditions, older adults, even with “age-normal” hearing, face challenges that may lead to poorer speech recognition than their younger peers. Older listeners generally demon-strate poorer suprathreshold auditory processing along with aging-related declines in neurocognitive functioning that may impair their ability to compen-sate using “top-down” cognitive–linguistic functions. This study explored top-down processing in older and younger adult listeners, specifically the use of semantic context during noise-vocoded sentence recognition. Method: Eighty-four adults with age-normal hearing (45 young normal-hearing [YNH] and 39 older normal-hearing [ONH] adults) participated. Participants were tested for recognition accuracy for two sets of noise-vocoded sentence mate-rials: one that was semantically meaningful and the other that was syntactically appropriate but semantically anomalous. Participants were also tested for hearing ability and for neurocognitive functioning to assess working memory capac-ity, speed of lexical access, inhibitory control, and nonverbal fluid reasoning, as well as vocabulary knowledge. Results: The ONH and YNH listeners made use of semantic context to a similar extent. Nonverbal reasoning predicted recognition of both meaningful and anomalous sentences, whereas pure-tone average contributed additionally to anomalous sentence recognition. None of the hearing, neurocognitive, or language measures significantly predicted the amount of context gain, computed as the difference score between meaningful and anomalous sentence recogni-tion. However, exploratory cluster analyses demonstrated four listener profiles and suggested that individuals may vary in the strategies used to recognize speech under adverse listening conditions. Conclusions: Older and younger listeners made use of sentence context to similar degrees. Nonverbal reasoning was found to be a contributor to noise-vocoded sentence recognition. However, different listeners may approach the problem of recognizing meaningful speech under adverse conditions using different strategies based on their hearing, neurocognitive, and language profiles. These findings provide support for the complexity of bottom-up and top-down interactions during speech recognition under adverse listening conditions.</p

Noise-Vocoded Sentence Recognition and the Use of Context in Older and Younger Adult Listeners

Purpose: When listening to speech under adverse conditions, older adults, even with “age-normal” hearing, face challenges that may lead to poorer speech recognition than their younger peers. Older listeners generally demon-strate poorer suprathreshold auditory processing along with aging-related declines in neurocognitive functioning that may impair their ability to compen-sate using “top-down” cognitive–linguistic functions. This study explored top-down processing in older and younger adult listeners, specifically the use of semantic context during noise-vocoded sentence recognition. Method: Eighty-four adults with age-normal hearing (45 young normal-hearing [YNH] and 39 older normal-hearing [ONH] adults) participated. Participants were tested for recognition accuracy for two sets of noise-vocoded sentence mate-rials: one that was semantically meaningful and the other that was syntactically appropriate but semantically anomalous. Participants were also tested for hearing ability and for neurocognitive functioning to assess working memory capac-ity, speed of lexical access, inhibitory control, and nonverbal fluid reasoning, as well as vocabulary knowledge. Results: The ONH and YNH listeners made use of semantic context to a similar extent. Nonverbal reasoning predicted recognition of both meaningful and anomalous sentences, whereas pure-tone average contributed additionally to anomalous sentence recognition. None of the hearing, neurocognitive, or language measures significantly predicted the amount of context gain, computed as the difference score between meaningful and anomalous sentence recogni-tion. However, exploratory cluster analyses demonstrated four listener profiles and suggested that individuals may vary in the strategies used to recognize speech under adverse listening conditions. Conclusions: Older and younger listeners made use of sentence context to similar degrees. Nonverbal reasoning was found to be a contributor to noise-vocoded sentence recognition. However, different listeners may approach the problem of recognizing meaningful speech under adverse conditions using different strategies based on their hearing, neurocognitive, and language profiles. These findings provide support for the complexity of bottom-up and top-down interactions during speech recognition under adverse listening conditions.</p

Adaptive Language-based Mental Health Assessment with Item-Response Theory

Mental health issues widely vary across individuals - the manifestations of signs and symptoms can be fairly heterogeneous. Recently, language-based depression and anxiety assessments have shown promise for capturing this heterogeneous nature by evaluating a patient's own language, but such approaches require a large sample of words per person to be accurate. In this work, we introduce adaptive language-based assessment - the task of iteratively estimating an individual's psychological score based on limited language responses to questions that the model also decides to ask. To this end, we explore two statistical learning-based approaches for measurement/scoring: classical test theory (CTT) and item response theory (IRT). We find that using adaptive testing in general can significantly reduce the number of questions required to achieve high validity (r ~ 0.7) with standardized tests, bringing down from 11 total questions down to 3 for depression and 5 for anxiety. Given the combinatorial nature of the problem, we empirically evaluate multiple strategies for both the ordering and scoring objectives, introducing two new methods: a semi-supervised item response theory based method (ALIRT), and a supervised actor-critic based model. While both of the models achieve significant improvements over random and fixed orderings, we find ALIRT to be a scalable model that achieves the highest accuracy with lower numbers of questions (e.g. achieves Pearson r ~ 0.93 after only 3 questions versus asking all 11 questions). Overall, ALIRT allows prompting a reduced number of questions without compromising accuracy or overhead computational costs