Institute for Interdisciplinary Studies of Austrian Universities

Multi-access Edge Computing (MEC) is an essential piece of 5G telecommunication systems to satisfy the challenging low-latency demands of future applications. Our previous publications argue that edge computing should be transparent to clients. We introduced an efficient solution to implement such a transparent approach, leveraging Software-Defined Networking and virtual IP+port addresses for registered edge services. A core component of our architecture is a Patricia Trie, which stores all our virtual IP+port addresses. Unfortunately, most implementations of Patricia Tries are not geared toward use cases with millions of keys where a low memory footprint becomes essential. In this paper, we present TinyTric$\dot{w}$, a space-efficient open-source implementation of a Patricia Trie for keys up to 256 bits. TinyTricia can keep track of up to half a billion (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">29</sup> ) keys $\leq$57 bits and up to a quarter of a billion (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">28</sup> -1) keys $\leq$256 bits with tiny memory requirements. In the latter case, each key can have a data value of any type. Keys $\leq$57 bits require only 8 to 16 bytes per key (i.e., only 0 to 8 bytes overhead per 57-bit key); for keys $\geq$58 bits, add the key size (in whole bytes) to these values. Thus, for 16.78 million (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">24</sup> )57-bit keys, our solution requires between 128 and 256 MiB of memory. Unlike some other spaceefficient implementations, TinyTriia allows adding and removing keys at runtime.

Abstract This article offers our reply to Wolfgang Teubert. 2021. Embodiment is not the answer to meaning: A discussion of the theory underlying the article by Carina Rasse and Raymond W. Gibbs, Jr. in JLS 50(1). Journey of Literary Semantics 50. 89–106. Teurbert’s article examined discussion of our earlier publication in this journal on metaphorical thinking in people’s literary experiences of J.D. Salinger’s novel “The Catcher in the Rye.” Teubert makes several points about our advocacy of an embodied perspective on literary meaning and interpretation. He argues that literary experience is best characterized in terms of people’s verbalized, reflective statements about the meanings of literary texts. Data from cognitive linguistic analyses and behavioral experiments are less compelling, in his view, because these studies examine embodied metaphors from a discourse-external perspective and mostly focus on people’s fast, mostly unconscious processing of verbal metaphors. Our reply highlights the importance of studying linguistic understanding, and literary experience, along varying time-dimensions, the fact that many linguistic and behavioral studies examine embodied metaphorical thinking in more reflective, social circumstances, exactly as Teubert recommends. Finally, we suggest that looking at literary experience from an embodied perspective is tightly associated with a discourse-analytic point of view. Scholars can never dismiss the reality of embodiment in literary experience because it provides a critical, but not exclusive, constraint on how we express ourselves and enable others to create specific patterns of meaning in the words they read.

We analyse the all-pay auction with incomplete information and variance-averse bidders. We characterise the symmetric equilibrium for general distributions of valuations and any number of bidders. Variance aversion is a sufficient assumption to predict that high-valuation bidders increase their bids relative to the risk-neutral case while low types decrease their bid. Considering an asymmetric two-player environment with uniform valuations, we show that a more variance-averse type always bids higher than her less variance-averse counterpart. Utilising our analytical bidding functions we discuss all-pay auctions with variance-averse bidders from a designer’s perspective. We extend our basic model to include noisy signals and allow for the possibility of variance-seeking preferences and type-dependent attitudes towards risk. (JEL C7, D7, D81. Keywords: Auctions, Contests, Mean-Variance preferences.)

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