Evaluation of Switching Technologies for Reflective and Transmissive RISs at Sub-THz Frequencies

For the upcoming 6G wireless networks, reconfigurable intelligent surfaces are an essential technology, enabling dynamic beamforming and signal manipulation in both reflective and transmissive modes. It is expected to utilize frequency bands in the millimeter-wave and THz, which presents unique opportunities but also significant challenges. The selection of switching technologies that can support high-frequency operation with minimal loss and high efficiency is particularly complex. In this work, we demonstrate the potential of advanced components such as Schottky diodes, memristor switches, liquid metal-based switches, phase change materials, and RF-SOI technology in RIS designs as an alternative to overcome limitations inherent in traditional technologies in D-band (110-170 GHz).

A churn prediction dataset from the telecom sector: a new benchmark for uplift modeling

Uplift modeling, also known as individual treatment effect (ITE) estimation, is an important approach for data-driven decision making that aims to identify the causal impact of an intervention on individuals. This paper introduces a new benchmark dataset for uplift modeling focused on churn prediction, coming from a telecom company in Belgium, Orange Belgium. Churn, in this context, refers to customers terminating their subscription to the telecom service. This is the first publicly available dataset offering the possibility to evaluate the efficiency of uplift modeling on the churn prediction problem. Moreover, its unique characteristics make it more challenging than the few other public uplift datasets.

Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment

Counterfactuals are central in causal human reasoning and the scientific discovery process. The uplift, also called conditional average treatment effect, measures the causal effect of some action, or treatment, on the outcome of an individual. This paper discusses how it is possible to derive bounds on the probability of counterfactual statements based on uplift terms. First, we derive some original bounds on the probability of counterfactuals and we show that tightness of such bounds depends on the information of the feature set on the uplift term. Then, we propose a point estimator based on the assumption of conditional independence between the counterfactual outcomes. The quality of the bounds and the point estimators are assessed on synthetic data and a large real-world customer data set provided by a telecom company, showing significant improvement over the state of the art.