Monitoring air pollution is of vital importance to the overall health of the
population. Unfortunately, devices that can measure air quality can be
expensive, and many cities in low and middle-income countries have to rely on a
sparse allocation of them. In this paper, we investigate the use of Gaussian
Processes for both nowcasting the current air-pollution in places where there
are no sensors and forecasting the air-pollution in the future at the sensor
locations. In particular, we focus on the city of Kampala in Uganda, using data
from AirQo's network of sensors. We demonstrate the advantage of removing
outliers, compare different kernel functions and additional inputs. We also
compare two sparse approximations to allow for the large amounts of temporal
data in the dataset.

利用高斯过程预测空气污染现状以及将来的趋势，并从设备、数据处理方法等方面进行了探索与比较。

坎帕拉空气质量监测的高斯过程

Gaussian Processes for Monitoring Air-Quality in Kampala

Consider a setting in which devices and a server share a pre-trained model.
The server wishes to make an inference on a new input given the model. Devices
have access to data, previously not used for training, and can communicate to
the server over a common wireless channel. If the devices have no access to the
new input, can communication from devices to the server enhance the quality of
the inference decision at the server? Recent work has introduced federated
conformal prediction (CP), which leverages devices-to-server communication to
improve the reliability of the server's decision. With federated CP, devices
communicate to the server information about the loss accrued by the shared
pre-trained model on the local data, and the server leverages this information
to calibrate a decision interval, or set, so that it is guaranteed to contain
the correct answer with a pre-defined target reliability level. Previous work
assumed noise-free communication, whereby devices can communicate a single real
number to the server. In this paper, we study for the first time federated CP
in a wireless setting. We introduce a novel protocol, termed wireless federated
conformal prediction (WFCP), which builds on type-based multiple access (TBMA)
and on a novel quantile correction strategy. WFCP is proved to provide formal
reliability guarantees in terms of coverage of the predicted set produced by
the server. Using numerical results, we demonstrate the significant advantages
of WFCP against digital implementations of existing federated CP schemes,
especially in regimes with limited communication resources and/or large number
of devices.

通过无线通信的设备到服务器的信息交流，提高服务器对新输入进行推断决策的准确性，在有限的通信资源和 / 或大量设备的环境下，无线联合共测性预测 (WFCP) 相对于现有的数字实现具有显著优势。

通过符合预测，在无线信道上进行可靠的不确定性量化的联邦推理

Federated Inference with Reliable Uncertainty Quantification over  Wireless Channels via Conformal Prediction

Blood pressure is a vital sign that offers important insights into overall
health, particularly cardiovascular well-being. It plays a critical role in
medical settings and homes for disease prevention, diagnosis, treatment, and
management. Physicians heavily rely on blood pressure values for making crucial
decisions. Most commercial devices utilize cuffs for blood pressure
measurement, and automatic devices have gained popularity due to the high
prevalence of hypertension. Self-measurement and home monitoring of blood
pressure are also recommended. However, concerns arise regarding the accuracy
of blood pressure measurement technologies and the alignment of reported values
with actual values. People often adjust their medication based on these
reported values, making accuracy vital. This study focuses on the concept of
``bias'' to highlight potential discrepancies between reported and actual blood
pressure values. Previous research has identified biases originating from three
categories: (1) blood pressure measurement devices, (2) subject-specific
factors, and (3) measurement sessions. Specifically, this study examines biases
associated with cuff-based blood pressure technologies due to their widespread
use in medical applications and the growing trend of home monitoring.
Identifying and addressing the primary sources of biases is crucial to prevent
their propagation and mitigate potential consequences. Additionally, the study
explores the future prospects of blood pressure monitoring using machine
learning methods.

本研究探讨了血压测量中的偏差，主要包括血压测量设备、主体因素和测量周期，并探讨如何使用机器学习技术来监测和预测血压的变化和可能存在的偏移。

血压测量技术调查：解决潜在偏差来源

A Survey on Blood Pressure Measurement Technologies: Addressing  Potential Sources of Bias

According to Dennett, the same system may be described using a `physical'
(mechanical) explanatory stance, or using an `intentional' (belief- and
goal-based) explanatory stance. Humans tend to find the physical stance more
helpful for certain systems, such as planets orbiting a star, and the
intentional stance for others, such as living animals. We define a formal
counterpart of physical and intentional stances within computational theory: a
description of a system as either a device, or an agent, with the key
difference being that `devices' are directly described in terms of an
input-output mapping, while `agents' are described in terms of the function
they optimise. Bayes' rule can then be applied to calculate the subjective
probability of a system being a device or an agent, based only on its
behaviour. We illustrate this using the trajectories of an object in a toy
grid-world domain.

该论文讨论了以物理立场和意图立场描述系统的形式对应于计算理论中设备和代理的概念，并通过贝叶斯规则计算系统是设备还是代理的主观概率。