220 research outputs found
-theoretic counterexamples to Ravenel's telescope conjecture
At each prime and height , we prove that the telescopic and
chromatic localizations of spectra differ. Specifically, for
acting by Adams operations on , we prove that the
-localized algebraic -theory of is not -local. We also show that Galois
hyperdescent, -invariance, and nil-invariance fail for the
-localized algebraic -theory of -local
-rings. In the case and we make complete
computations of , for certain finite Galois extensions
of the -local sphere. We show for that the algebraic -theory
of the -local sphere is asymptotically -local.Comment: 100 pages. Comments very welcom
A Dance of Neural Rhythms: Coupling of Slow Oscillations and Spindle Activity During Sleep, in Relation to Memory and Ageing
Sleep undergoes both quantitative and qualitative changes across the lifespan, which accompanies age-related declines in memory. Contemporary research suggests that synchronized cross-frequency coupling (CFC) of slow oscillations (SO) and spindles during sleep is a neural mechanism of overnight memory consolidation, and it has been hypothesized that an age-related “de-coupling” may contribute to memory impairments in older adults. However, evidence for this ageing hypothesis is currently lacking, as most available studies of SO-spindle CFC and memory focus on young adults, with few studies available to directly compare younger and older groups.
The goal of my dissertation is to enhance current thinking about SO-spindle CFC and its relations with ageing and overnight memory consolidation. This work includes two interrelated studies, both examining SO-spindle CFC during sleep in relation to performance on a sleep-dependent declarative memory task. The first study, with 25 healthy seniors, showed that SO-spindle CFC was stable across two recording nights, and that a slow spindle coupling phase closer to the SO up-state predicted better memory consolidation. An exploratory analysis also highlighted potential interactive effects between distinct CFC measures in predicting memory. My second study builds on the analyses from Study 1 with a new sample of 16 older adults compared to 16 young adults, all who completed an expanded 3-night protocol that included a similar memory task and a cognitive control task. This second study showcased age-group differences in coupling during sleep but did not find similarly strong evidence for relations between CFC and memory.
Overall, this work demonstrates 1) a preserved association between certain measures of CFC during sleep and memory in older adults; 2) that coupling dynamics may differ between younger and older adults, but the overall magnitude of coupling may not; and 3) how relations between CFC, ageing, and memory can vary across distinct analytic contexts. Results are discussed in reference to the impact of ageing on brain oscillations and CFC, distinctions in the methods between my two studies as compared to the extant literature, and the impact of even small changes in signal processing decisions on coupling metrics and their association with memory and ageing
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
Large Partition Functions of 3d Holographic SCFTs
We study the topologically twisted index and
the squashed sphere partition function of various 3d
holographic superconformal field theories arising from M2-branes. Employing
numerical techniques in combination with well-motivated conjectures we provide
compact closed-form expressions valid to all orders in the perturbative
expansion for these observables. We also discuss the holographic implications
of our results for the topologically twisted index for the dual M-theory
Euclidean path integral around asymptotically AdS solutions of 11d
supergravity. In Lorentzian signature this leads to a prediction for the
corrections to the Bekenstein-Hawking entropy of a class of static
asymptotically AdS BPS black holes.Comment: v1: p.57; v2: minor revision
Discrete time models for bid-ask pricing under Dempster-Shafer uncertainty
As is well-known, real financial markets depart from simplifying hypotheses of classical no-arbitrage pricing theory. In particular, they show the presence of frictions in the form of bid-ask spread. For this reason, the aim of the thesis is to provide a model able to manage these situations, relying on a non-linear pricing rule defined as (discounted) Choquet integral with respect to a belief function. Under the partially resolving uncertainty principle, we generalize the first fundamental theorem of asset pricing in the context of belief functions. Furthermore, we show that a generalized arbitrage-free lower pricing rule can be characterized as a (discounted) Choquet expectation with respect to an equivalent inner approximating (one-step) Choquet martingale belief function. Then, we generalize the Choquet pricing rule dinamically: we characterize a reference belief function such that a multiplicative binomial process satisfies a suitable version of time-homogeneity and Markov properties and we derive the induced conditional Choquet expectation operator. In a multi-period market with a risky asset admitting bid-ask spread, we assume that its lower price process is modeled by the proposed time-homogeneous Markov multiplicative binomial process. Here, we generalize the theorem of change of measure, proving the existence of an equivalent one-step Choquet martingale belief function. Then, we prove that the (discounted) lower price process of a European derivative is a one-step Choquet martingale and a k-step Choquet super-martingale, for k ≥ 2
Statistical Data Modeling and Machine Learning with Applications
The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties
Globalization? Trade war? A counterbalance perspective
The embrace of globalization and protectionism among economies has ebbed and flowed over the past few decades. These fluctuations call for quantitative analytics to help countries improve their trade policies. Changing attitudes about globalization also imply that the best trade policies may vary over time and be country-specific. We argue that the imports and exports of all economies constitute a counterbalanced network where conflict and cooperation are two sides of the same coin. Quantitative competitiveness is then formulated for each country using a network counterbalance equilibrium. A country could improve its relative strength in the network by embracing globalization, protectionism, trade collaboration, or conflict. This paper presents the necessary conditions for globalization and trade wars, evaluates their side effects, derives national bargaining powers, identifies appropriate targets for conflict or collaboration, and recommends fair resolutions for trade conflicts. Data and events from the past twenty years support these conditions
Proceedings of the Conference on Production Systems and Logistics: CPSL 2022
[no abstract available
Partial OFDM Symbol Recovery to Improve Interfering Wireless Networks Operation in Collision Environments
The uplink data rate region for interfering transmissions in wireless networks has been characterised and proven, yet its underlying model assumes a complete temporal overlap. Practical unplanned networks, however, adopt packetized transmissions and eschew tight inter-network coordination, resulting in packet collisions that often partially overlap, but rarely ever completely overlap. In this work, we report a new design called (), that specifically targets the parts of data symbols that experience no interference during a packet collision. bootstraps a successive interference cancellation (SIC) like decoder from these strong signals, thus improving performance over techniques oblivious to such partial packet overlaps. We have implemented on the WARP software-defined radio platform and in trace-based simulation. Our performance evaluation presents experimental results from this implementation operating in a 12node software network testbed, spread over two rooms in a nonlineofsight indoor office environment. Experimental results confirm that our proposal decoder is capable of decoding up to 60 % of collided frames depending on the type of data and modulation used. This consistently leads to throughput enhancement over conventional WiFi under different scenarios and for the various data types tested, namely downlink bulk TCP, downlink videoondemand, and uplink UDP
Advances in Data Mining Knowledge Discovery and Applications
Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications
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