Mixed Micro/Macro Cache for Device-to-Device Caching Systems in Multi-Operator Environments.

In a device-to-device (D2D) caching system that utilizes a device's available storage space as a content cache, a device called a helper can provide content requested by neighboring devices, thereby reducing the burden on the wireless network. To enhance the efficiency of a limited-size cache, one can consider not only macro caching, which is content-based caching based on content popularity, but also micro caching, which is chunk-based sequential prefetching and stores content chunks slightly behind the one that a nearby device is currently viewing. If the content in a cache can be updated intermittently even during peak hours, the helper can improve the hit ratio by performing micro caching, which stores chunks that are expected to be requested by nearby devices in the near future. In this paper, we discuss the performance and effectiveness of micro D2D caching when there are multiple operators, the helpers can communicate with the devices of other operators, and the operators are under a low load independently of each other. We also discuss the ratio of micro caching in the cache area when the cache space is divided into macro and micro cache areas. Good performance can be achieved by using micro D2D caching in conjunction with macro D2D caching when macro caching alone does not provide sufficient performance, when users are likely to continue viewing the content they are currently viewing, when the content update cycle for the cache is short and a sufficient number of chunks can be updated for micro caching, and when there are multiple operators in the region.

Gambling Behavior of Excluded Gamblers in a Multi-venue Exclusion System: Results from a Three-Wave Survey Conducted in Switzerland.

Gamblers enrolled in the Swiss Multi-Venue Exclusion Program completed a written questionnaire three times, at six-month intervals. In addition to sociodemographic information, they provided details of their gambling behavior, and completed the South Oaks Gambling Screen-Revised (SOGS-R). The excluded gamblers were compared to a control group of non-excluded gamblers who also completed the questionnaire. The baseline survey demonstrated that there was a significant association between gamblers status (excluded n = 87 and non-excluded n = 259) and income (p = .039), as well as debt situation (p < .001) and SOGS-R score classification (p < .001). Over the course of three surveys, 242 gamblers participated. Of these, 133 respondents were not excluded from casinos at any time, 33 were excluded at the time of the first survey wave and remained so, while the exclusion status of 76 respondents changed over time, thus they were excluded for a minimum of one wave. Overall, 12.1% of excluded individuals stopped gambling altogether. Although exclusion is circumvented by some gamblers, it is associated with significant reductions in frequency, duration, and expenditure, as well as severity of problem gambling. The effects were more significant among gamblers who were excluded from casinos during the entire survey period. The results suggest that the duration of an exclusion should be at least six months instead of the current three months. 62.6% of the excluded gamblers had at least one exclusion lifted during the survey period. Further research is needed to investigate the implications of repeated exclusions for gambling-specific problems.

Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps.

Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain-computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.

Collaborative Resource Management for Negotiable Multi-Operator Small Cell Networks.

The ubiquitous coverage/connectivity requirement of wireless cellular networks has shifted mobile network operators' (MNOs) interest toward dense deployment of small cells with coverage areas that are much smaller as compared to macrocell base stations (MBSs). Multi-operator small cells could provide virtualization of network resources (infrastructure and spectrum) and enable its efficient utilization, i.e., uninterrupted coverage and connectivity to subscribers, and an opportunity to avoid under-utilization of the network resources. However, a MNO with exclusive ownership to network resources would have little incentive to utilize its precious resources to serve users of other MNOs, since MNOs differentiate among others based on their ownership of the licensed spectrum. Thus, considering network resources scarcity and under-utilization, this paper proposes a mechanism for multi-operator small cells collaboration through negotiation that establishes a mutual agreement acceptable to all involved parties, i.e., a win-win situation for the collaborating MNOs. It enables subscribers of a MNO to utilize other MNOs' network resources, and allows MNOs to offer small cells "as a service" to users with ubiquitous access to wireless coverage/connectivity, maximize the use of an existing network resources by serving additional users from a market share, and enhance per-user data rate. We validated and evaluated the proposed mechanism through simulations considering various performance metrics.

PROSE: Predicting Multiple Operators and Symbolic Expressions using multimodal transformers.

Approximating nonlinear differential equations using a neural network provides a robust and efficient tool for various scientific computing tasks, including real-time predictions, inverse problems, optimal controls, and surrogate modeling. Previous works have focused on embedding dynamical systems into networks through two approaches: learning a single operator (i.e., the mapping from input parameterised functions to solutions) or learning the governing system of equations (i.e., the constitutive model relative to the state variables). Both of these approaches yield different representations for the same underlying data or function. Observing that families of differential equations often share key characteristics, we seek one network representation across a wide range of equations. Our multimodality approach, called Predicting Multiple Operators and Symbolic Expressions (PROSE), is capable of constructing multi-operators and governing equations simultaneously through a novel fusion structure. In particular, PROSE solves differential equations, predicts future states, and generates the underlying equations of motion by incorporating symbolic "words" through a language model. Experiments with 25600 distinct equations show that PROSE benefits from its multimodal nature, resulting in robust generalization (e.g. noisy observations, equation misspecification, and data imbalance) supported by comparison and ablation studies. PROSE provides a new operator learning framework that incorporates multimodal input/output and language models for solving forward and inverse problems related to differential equations.