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The beginning of AI-driven welfare? An inquiry into how public sector AI experiments shape the Danish welfare state
No full textThis study investigates the nascent stages of an AI-driven welfare state by focusing on 40 signature projects initiated by the Danish Government between 2020 and 2022. These projects represent a paradigmatic case of AI experimentation within the welfare state. We adopt an empirical approach based on mixed methods to explore the objectives, organizational actors, goals, and outcomes of the projects, shedding light on the evolving landscape of AI-driven welfare from 2020 to 2023. The chapter highlights the variance between welfare domains in the objectives set for AI models and the differences between municipal and regional experimentation with welfare, focusing on how they differ with respect to implementation and research goals. While only some of the projects to date have been operationally implemented, several live on as prototypes or partial systems with the potential to transform the welfare state beyond their formal termination
#SAT-Algorithms for Classes of Threshold Circuits Based on Probabilistic Rank
No full textThere is a large body of work that shows how to leverage lower bound techniques for circuit classes to obtain satisfiability algorithms that run in better than brute-force time [24, 38]. For circuits with threshold gates, there are several such algorithms based on either Probabilistic Representations by low-degree polynomials, which allow for the use of fast polynomial evaluation algorithms, or Low rank, which allows for an efficient reduction to rectangular matrix multiplication. In this paper, we use a related notion of probabilistic rank to obtain satisfiability algorithms for circuit classes contained in ACC0 ◦ 3-PTF, i.e. constant-depth circuits with modular counting gates and a single layer of degree-3 polynomial threshold functions. Even for the special case of a single 3-PTF, it is not clear how to use either of the above two strategies to get a non-trivial satisfiability algorithm. The best known algorithm in this case previously was based on memoization and yields worse guarantees than our algorithm
Is it getting harder to make a hit? Evidence from 65 years of US music chart history
No full textAbstract Since the creation of the Billboard Hot 100 music chart in 1958, the chart has been a window into the music consumption of Americans. Since its introduction, the chart has documented music consumption through eras of globalization, economic growth, and the emergence of new technologies for music listening. In recent years, artists have voiced their worry that the music world is changing: Many claim that it is getting harder to make a hit. Until now, however, the claims have not been backed using chart data. Here we show that the dynamics of the Billboard Hot 100 chart have changed significantly since the chart’s founding in 1958, and, in particular, in the past 15 years. Whereas most songs spend less time on the chart now than songs did in the past, we show that top-1 songs have tripled their chart lifetime since the 1960s, and the highest-ranked songs maintain their positions for far longer than previously. At the same time, churn has increased drastically, and the lowest-ranked songs are replaced more frequently than ever. Together, these observations support two competing and seemingly contradictory theories of digital markets: The Winner-takes-all theory and the Long Tail theory. Who occupies the chart has also changed over the years: In recent years, fewer new artists make it into the chart and more positions are occupied by established hit makers. Finally, investigating how song chart trajectories have changed over time, we show that historical song trajectories cluster into clear trajectory archetypes characteristic of the time period they were part of. Our results are interesting in the context of collective attention: Whereas recent studies have documented that other cultural products such as books, news, and movies fade in popularity quicker in recent years, music hits seem to last longer now that in the past.Since the creation of the Billboard Hot 100 music chart in 1958, the chart has been a window into the music consumption of Americans. Since its introduction, the chart has documented music consumption through eras of globalization, economic growth, and the emergence of new technologies for music listening. In recent years, artists have voiced their worry that the music world is changing: Many claim that it is getting harder to make a hit. Until now, however, the claims have not been backed using chart data. Here we show that the dynamics of the Billboard Hot 100 chart have changed significantly since the chart’s founding in 1958, and, in particular, in the past 15 years. Whereas most songs spend less time on the chart now than songs did in the past, we show that top-1 songs have tripled their chart lifetime since the 1960s, and the highest-ranked songs maintain their positions for far longer than previously. At the same time, churn has increased drastically, and the lowest-ranked songs are replaced more frequently than ever. Together, these observations support two competing and seemingly contradictory theories of digital markets: The Winner-takes-all theory and the Long Tail theory. Who occupies the chart has also changed over the years: In recent years, fewer new artists make it into the chart and more positions are occupied by established hit makers. Finally, investigating how song chart trajectories have changed over time, we show that historical song trajectories cluster into clear trajectory archetypes characteristic of the time period they were part of. Our results are interesting in the context of collective attention: Whereas recent studies have documented that other cultural products such as books, news, and movies fade in popularity quicker in recent years, music hits seem to last longer now that in the past
Three-dimensional directivity measurement of acoustic diffusers using regularized holography and sound field separation
No full textCharacterizing acoustic diffusers poses significant challenges due to the complex space-time dependence of the scattered sound fields they generate. This study proposes using regularized plane wave expansion to measure real-sized rigid diffusers in free-field. The formulated inverse problem enables the separation of incident and scattered sound fields in the wave-number domain. Furthermore, a tailored wave-number grid facilitates the computation of three-dimensional directivities directly identified in the wave-number spectrum. The proposed technique is validated through measurements conducted in the near field of three diffusers with distinct geometries. The results obtained from regularized plane wave expansion were compared against reference simulations using the Boundary Element Method. The unique directivity characteristics of different diffusers are accurately characterized, and good agreement is found with the reference simulations. The use of finer spatial resolution and -octave band averaging further improves the reliability of directivity and diffusion coefficient estimates. Thus, the method provides an alternative path for a robust and more practical characterization of acoustic diffusers
Sonic Agency: A Group Autoethnography of Technology-mediated Performance Practice by Deaf and Hard of Hearing Musicians
No full textWhile many assistive devices and accessible technologies aim to increase access and participation of d/Deaf and Hard of Hearing (DHH) audiences, DHH people’s active participation in music performance remains limited. Through a 15-month-long group autoethnography, we present a case study of a Deaf-owned music and culture institution in a mixed-hearing, technology-mediated performance practice. We unpack how technologies are adapted and developed by DHH musicians and highlight the existing and emerging challenges of mixed hearing creative spaces. We use retrospective accounts, field notes, and performance artifacts to reveal the methods we developed for more inclusive music co-creativity. We lastly present three essential strategies that support DHH individuals’ sonic agency and access, with or without prior music knowledge, applicable by different actors who partake in the DHH or mixed hearing music performance ecosystem
Model Checking Reachability Properties for Quantum Markov Chains
Get PDFWe propose a discrete time quantum Markov chain (QMC) related to previous proposals but yet with a different semantics. A probability measure is defined similar to as for DTMCs in contrast to e.g.\ the super operator valued measure. Our work is based on a simple imperative quantum pseudo programming language and its denotational semantics which naturally leads to a definition of a QMC. As a novelty we demonstrate how reachability events of a QMC expressed in a simple temporal logic like notation may be checked similar to as for DTMCs as transient state probabilities and how probability intervals of such events may be computed by smallest fixed-point solutions to linear equations
Local Density and Its Distributed Approximation.
No full textThe densest subgraph problem is a classic problem in combinatorial optimisation. Graphs with low maximum subgraph density are often called “uniformly sparse”, leading to algorithms parameterised by this density. However, in reality, the sparsity of a graph is not necessarily uniform. This calls for a formally well-defined, fine-grained notion of density. Danisch, Chan, and Sozio propose a definition for local density that assigns to each vertex v a value ρ*(v). This local density is a generalisation of the maximum subgraph density of a graph. I.e., if ρ(G) is the subgraph density of a finite graph G, then ρ(G) equals the maximum local density ρ*(v) over vertices v in G. They present a Frank-Wolfe-based algorithm to approximate the local density of each vertex with no theoretical (asymptotic) guarantees. We provide an extensive study of this local density measure. Just as with (global) maximum subgraph density, we show that there is a dual relation between the local out-degrees and the minimum out-degree orientations of the graph. We introduce the definition of the local out-degree g*(v) of a vertex v, and show it to be equal to the local density ρ*(v). We consider the local out-degree to be conceptually simpler, shorter to define, and easier to compute. Using the local out-degree we show a previously unknown fact: that existing algorithms already dynamically approximate the local density for each vertex with polylogarithmic update time. Next, we provide the first distributed algorithms that compute the local density with provable guarantees: given any ε such that ε−1 ∈ O(poly n), we show a deterministic distributed algorithm in the LOCAL model where, after O(ε−2 log2 n) rounds, every vertex v outputs a (1 + ε)-approximation of their local density ρ*(v). In CONGEST, we show a deterministic distributed algorithm that requires poly(log n, ε−1) · 2O(√log n) rounds, which is sublinear in n. As a corollary, we obtain the first deterministic algorithm running in a sublinear number of rounds for (1 + ε)-approximate densest subgraph detection in the CONGEST model
Crossing Domains without Labels: Distant Supervision for Term Extraction
No full textAbstractAutomatic Term Extraction (ATE) is a critical component in downstream NLP tasks such as document tagging, ontology construction and patent analysis. Current state-of-the-art methods require expensive human annotation and struggle with domain transfer, limiting their practical deployment. This highlights the need for more robust, scalable solutions and realistic evaluation settings. To address this, we introduce a comprehensive benchmark spanning seven diverse domains, enabling performance evaluation at both the document- and corpus-levels. Furthermore, we propose a robust LLM-based model that outperforms both supervised cross-domain encoder models and few-shot learning baselines and performs competitively with its GPT-4o teacher on this benchmark.The first step of our approach is generating psuedo-labels with this black-box LLM on general and scientific domains to ensure generalizability. Building on this data, we fine-tune the first LLMs for ATE. To further enhance document-level consistency, oftentimes needed for downstream tasks, we introduce lightweight post-hoc heuristics. Our approach exceeds previous approaches on 5/7 domains with an average improvement of 10 percentage points. We release our dataset and fine-tuned models to support future research in this area
Decision factors for the selection of AI-based decision support systems—The case of task delegation in prognostics
No full textDecision support systems (DSS) integrating artificial intelligence (AI) hold the potential to significantly enhance organizational decision-making performance and speed in areas such as prognostics in machine maintenance. A key issue for organizations aiming to leverage this potential is to select an appropriate AI-based DSS. In this paper, we develop a delegation perspective to identify decision factors and underlying AI system characteristics that affect the selection of AI-based DSS. Utilizing the analytical hierarchy process method, we derive decision weights for these characteristics and apply them to three archetypes of AI-based DSS designed for prognostics. Additionally, we explore how users’ expertise levels impact their preferences for specific AI system characteristics. The results confirm that Performance is the most important decision factor, followed by Effort and Transparency. In line with these results, we find that the archetypes of prognostics systems using Direct Remaining Useful Life estimation and Similarity-based Matching best fit user preferences. Moreover, we find that novices and experts strongly prefer visual over structural explanations, while users with moderate expertise also value structural explanations to develop their skills further