IMDEA Networks

Background/Objectives: The treatment of dual disorders (DDs) must be comprehensive and multidisciplinary. Evidence supports the effectiveness of psychotherapy in treating DDs. The second part of this consensus synthesizes the available evidence on psychological treatment for specific DDs. Methods: Two consensus methods were sequentially implemented: the nominal group technique and the Delphi method. Results: This consensus review encompassed a compilation of recommendations for the psychological treatment of neurodevelopmental, anxiety, post-traumatic stress, somatic symptom, eating, and personality disorders. Finally, recommendations for the future research agenda on the psychological treatment of DD were included. Conclusions: (1) Psychological treatment, particularly integrated treatment, is effective. (2) In the case of dual autism, interventions for substance use disorders should be adapted to this population's characteristics. (3) More research is needed on dual social anxiety, panic, generalized anxiety, somatic symptom, and eating disorders, for which Cognitive Behavioral Therapy (CBT) is the most commonly used treatment. (4) For dual attention deficit hyperactivity disorder, multicomponent treatment is recommended (psychoeducation, CBT, and peer or family support). (5) For dual anxiety disorders, CBT is the first-line treatment. (6) For dual post-traumatic stress disorder, CBT (cognitive processing therapy and prolonged exposure therapy), acceptance and commitment therapy, stress inoculation training, and Eye Movement Desensitization and Reprocessing (EMDR) are effective. (7) For dual personality disorders, evidence is scarce. (8) For borderline personality disorder, dialectical behavior therapy, dynamic deconstructive psychotherapy, and dual-focus schema therapy show promise. (9) For antisocial personality disorder, CBT, contingency management, and counseling on impulsive lifestyles may be useful. (10) Much more evidence is needed from studies that overcome the methodological limitations of existing ones.

Abstract Recruiting passive candidates, i.e. individuals not actively seeking jobs but open to compelling opportunities, remains one of the hardest challenges in digital recruitment. Motivated by a real collaboration with an industry partner, we introduce the Independent Halting Cascade (IHC) model: a simple but rich agent-based framework that couples network diffusion with the possibility of halting through job applications. Agents can either recommend vacancies to peers or apply themselves, and incentives increase the likelihood of recommendation, mobilizing otherwise passive candidates. The IHC bridges research on social network diffusion, coordinated task completion, and labor economics by modeling heterogeneous skills, job specificities, and network structures, including homophily. We derive analytical boundaries that characterize diffusion and failure regimes, and we show through simulations that the IHC reproduces the empirical chain-length distributions of Travers and Milgram and Dodds with only coarse calibration. Across synthetic (ER, BA, homophilic) and real networks (SMS, e-mail, Twitter), the IHC achieves comparable or higher success rates than direct-recommendation baselines, while requiring fewer applicants. Our findings suggest that the IHC captures core mechanisms of coordinated task completion, offering both a theoretical contribution and a practical foundation for recruitment systems designed to reach and engage passive candidates.

Reconfigurable intelligent surfaces (RIS) have emerged as a promising approach to enhance the secrecy performance of wireless networks from a physical layer security perspective. However, its effectiveness critically depends on the availability of accurate instantaneous channel state information (CSI) at the RIS controller. This work evaluates the impact of outdated CSI on the secrecy performance of a multiple RIS-aided wireless network. To this end, a closed-form expression for the secrecy outage probability (SOP) is derived assuming the availability of an outdated CSI at the RIS controller. The obtained results demonstrate the network’s secrecy degradation level due to the CSI outdatedness, while the increase in the number of RISs can compensate for such a secrecy loss. For instance, incorporating three RISs improves the SOP by two orders of magnitude with respect to a single RIS for 25% of CSI outdatedness. Furthermore, the results reveal that, under specific scenarios of several channel outdatedness, a baseline single RIS with a perfect CSI can outperform multiple RISs with outdated CSI. These findings highlight a critical limitation in incorporating multiple RISs in a wireless network with severe channel aging, requiring the increase of the RISs size or the number of RISs to combat its effect.

Integrating machine learning (ML) models directly in the network user plane enables inference on data traffic at line rate, and can dramatically reduce the latency and improve the scalability of key functionalities like traffic classification or intrusion detection. Yet, the hardware that can be used for this purpose, in particular programmable switches, present stringent constraints in terms of limited memory and little support for mathematical operations or data types that render ML model deployment a substantial technical challenge. In this paper, we make a step forward in user-plane ML by introducing <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flowrest</monospace>, a solution that redefines the state of the art in flow-level inference for programmable switches. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flowrest</monospace> allows implementing general-purpose Random Forest (RF) models in industry-grade switches by (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {i}$</tex-math> </inline-formula>) suitably handling stateful flow-level (FL) features in the switch ASIC, (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {ii}$</tex-math> </inline-formula>) achieving low-collision flow management, and (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {iii}$</tex-math> </inline-formula>) customizing RF models right from the design phase for in-switch operation. We develop <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flowrest</monospace> as an open-source software using the P4 language and evaluate its performance in an experimental testbed with Intel Tofino switches. Experiments with inference tasks of varying complexity prove that our solution improves accuracy by over 10 percent points on average with respect to the second-best competitor out of five recent approaches for RF-based in-switch inference, while maintaining sub-microsecond latency.

The Service-Based Architecture (SBA) has been successfully employed in the 5G Core since Release 15. Its modular and loosely coupled approach to network function interaction, based on the producer/consumer paradigm, perfectly pairs with the softwarization trend mobile networks have undergone over the last decade. Still, the Radio Access Network (RAN) did not leverage this approach, retaining the traditional Protocol-Based Architecture (PBA). In this paper, we make the case for adopting the SBA in the RAN, a transition we argue is both advantageous and feasible. We first present a comprehensive analysis of the benefits, such as enhanced flexibility and innovation, while also considering potential implementation challenges. Then, we detail how the SBA can be used to provide two exemplary procedures currently performed using the PBA, showing the feasibility of a fully service-based 5G network architecture.

Vaccination against the SARS-CoV-2 disease has significantly reduced its mortality rate and spread. However, despite its availability, a considerable proportion of the public has either refused or delayed getting vaccinated. This reluctance is known as vaccine hesitancy. The aim of this paper is to present a mathematical model to investigate how social interaction can impact vaccine hesitancy. The model describes the temporal transitions between different vaccination classes of the population (those vaccinated, those who are not yet vaccinated but agree to be vaccinated, and those who refuse). We apply the model to state and national survey data from the USA to estimate model parameters that quantify the rates at which public opinion on vaccination changes. Moreover, we investigate how political trends and demographic factors, such as age and education, impact these parameters. Our results show that state-level political affiliation, age, and educational level shape opinions on vaccination and have a strong influence on the temporal dynamics of attitude changes.

The rising popularity of Vehicle-to-Network (V2N) applications is driven by the Ultra-Reliable Low-Latency Communications (URLLC) service offered by 5G. Distributed resources can help manage heavy traffic from these applications, but complicate traffic routing under URLLCfs strict delay requirements. In this paper, we introduce the V2N Computation Offloading and CPU Activation (V2N-COCA) problem, aiming at the monetary/energetic cost minimization via computation offloading and edge/cloud CPU activation decisions, under stringent latency constraints. Some challenges are the proven nonmonotonicity of the objective function and the no-existence of closed-formulas for the sojourn time of tasks. We present a provably tight approximation for the latter, and we design BiQui, a provably asymptotically optimal and computationally efficient algorithm for the V2N-COCA problem. We then study dynamic scenarios, introducing the Swap-Prevention problem, to account for changes in the traffic load and minimize the switching on/off of CPUs without incurring into overcosts.We prove the problemfs structural properties and exploit them to design Min-Swap, a provably correct and computationally effective algorithm for the Swap-Prevention Problem. We assess both BiQui and Min-Swap over real-world vehicular traffic traces, performing a sensitivity analysis and a stress-test. Results show that (i) BiQui is nearoptimal and significantly outperforms existing solutions; and (ii) Min-Swap reduces by a ≥90% the CPU swapping incurring into just ≤0.14% extra cost.