We review how nuclear forces emerge from low-energy QCD via chiral effective field theory. The presentation is accessible to the non-specialist. At the same time, we also provide considerable detailed information (mostly in appendices) for the benefit of researchers who wish to start working in this field.
We consider a communication system in which status updates arrive at a source node, and should be transmitted through a network to the intended destination node. The status updates are samples of a random process under observation, transmitted as packets, which also contain the time stamp to identify when the sample was generated. The age of the information available to the destination node is the time elapsed, since the last received update was generated. In this paper, we model the source-destination link using the queuing theory, and we assume that the time it takes to successfully transmit a packet to the destination is an exponentially distributed service time. We analyze the age of information in the case that the source node has the capability to manage the arriving samples, possibly discarding packets in order to avoid wasting network resources with the transmission of stale information. In addition to characterizing the average age, we propose a new metric, called peak age, which provides information about the maximum value of the age, achieved immediately before receiving an update.
A. P. Alodjants, D. V. Tsarev, P. V. Zakharenko
et al.
Abstract We propose a quantum-inspired framework for modeling open distributed intelligence systems (DISs) comprising natural intelligence agents (NIAs) and artificial intelligence agents (AIAs) that interact with each other. Each NIA – AIA pair represents a user and their digital assistant – an avatar implemented as an agent based on a large language model (LLM). The AIAs are interconnected through a complex, scale-free network and communicate with users and one another in real time. We focus on the social impact and evolution of users’ emotional states, which we model as simple, two-level cognitive systems shaped by interactions with AIAs and external information sources. Within this framework, the AIAs adiabatically follow the NIAs, mediating emotional influence by disseminating information and propagating user emotions throughout the system. Building on Mehrabian’s Pleasure–Arousal–Dominance (PAD) model and Wundt’s three-dimensional theory of emotions, we put forward a quantum-like representation of affective states on an emotional sphere. We demonstrate that the arousal component is governed by the interplay between external informational inputs and individual personality traits. This leads to the emergence of limiting cycles in emotional dynamics. Assuming weak AIA – AIA coupling, we identify two distinct regimes of affective behavior. In the first regime, coherent NIA – AIA interaction supports emotional heterogeneity and individual differentiation across the network. In the second regime, shared exposure to external information drives synchronized emotional responses, resulting in a macroscopic affective field that captures collective emotional dynamics. Furthermore, we demonstrate that the network’s structural properties, particularly node degree correlations, play a role analogous to quantum correlations in ensembles of two-level physical systems; a quantum-like superradiant state corresponds to the network-induced collective emotional activation of NIAs within a DIS. These findings advance our understanding of affective dynamics and emergent social phenomena in hybrid human–AI ecosystems.
Does semantic communication require a semantic information theory parallel to Shannon's information theory, or can Shannon's work be generalized for semantic communication? This paper advocates for the latter and introduces a semantic generalization of Shannon's information theory (G theory for short). The core idea is to replace the distortion constraint with the semantic constraint, achieved by utilizing a set of truth functions as a semantic channel. These truth functions enable the expressions of semantic distortion, semantic information measures, and semantic information loss. Notably, the maximum semantic information criterion is equivalent to the maximum likelihood criterion and similar to the Regularized Least Squares criterion. This paper shows G theory's applications to daily and electronic semantic communication, machine learning, constraint control, Bayesian confirmation, portfolio theory, and information value. The improvements in machine learning methods involve multilabel learning and classification, maximum mutual information classification, mixture models, and solving latent variables. Furthermore, insights from statistical physics are discussed: Shannon information is similar to free energy; semantic information to free energy in local equilibrium systems; and information efficiency to the efficiency of free energy in performing work. The paper also proposes refining Friston's minimum free energy principle into the maximum information efficiency principle. Lastly, it compares G theory with other semantic information theories and discusses its limitation in representing the semantics of complex data.
Melanie Jane Nicholls, Alexandra Almeida, Justin Castello
et al.
BackgroundMedications for opioid use disorder (MOUD), such as methadone and buprenorphine, are the gold standard for opioid use disorder (OUD) treatment. Owing to various barriers, MOUD access and retention are low in the United States. The internet presents a digital solution to mitigate barriers, but a comprehensive and reliable resource is lacking. We present a user-friendly, web-based resource, the MOUD Hub, that provides reliable information on MOUD.
ObjectiveThis study aims to assess the safety, acceptability, feasibility of dissemination, and reach of the MOUD Hub using focus groups and advertising on 1 key search engine and 1 social media platform.
MethodsThis protocol describes the development of the MOUD Hub and the descriptive observational feasibility study that will be undertaken. The MOUD Hub uses motivational interviewing principles to guide users through the stages of change. The website provides evidence-based information from national health and substance use agencies, harm reduction organizations, and peer-reviewed literature. First, pilot focus groups with 10 graduate students who have lived experience with OUD will be conducted to provide feedback on safety concerns. Then, focus groups with 20-30 potential MOUD Hub users (eg, people with OUD with and without MOUD experience, friends and family, and health care providers) will be conducted to assess safety, acceptability, reach, and usability. Data will be analyzed using inductive thematic analysis. The website will be advertised on Google and MOUD-specific Reddit forums to assess dissemination, reach, and user acceptability based on the total user volume, sociodemographic characteristics, pop-up survey responses, and 1-year engagement patterns. This information will be collected through Google Analytics. Potential differences between users from Google and Reddit will be assessed.
ResultsThe MOUD Hub will be launched in January 2025. Data collected from 5 focus groups (approximately 30-40 participants) will be used to improve the website before launching it. There is no target sample size for the second stage of the study as it aims to assess dissemination feasibility and reach. Data will be collected for a year, analyzed every 3 months, and used to improve the website.
ConclusionsThe MOUD Hub offers an innovative theory-based approach, tailored to people with OUD and their family and friends, to increase access to and retention in MOUD treatment in the United States and provides broader harm reduction resources for those not currently in a position to receive treatment or those at risk of resuming illicit opioid use. Findings from this feasibility phase will serve to better tailor the MOUD Hub. After modifying the website based on our findings, we will use a randomized controlled trial to assess its efficacy in increasing MOUD access and retention, contributing to growing research on web-based interventions for OUD.
International Registered Report Identifier (IRRID)PRR1-10.2196/57065
Medicine, Computer applications to medicine. Medical informatics