This research explores a relatively unexplored area of predicting
cryptocurrency staking rewards, offering potential insights to researchers and
investors. We investigate two predictive methodologies: a) a straightforward
sliding-window average, and b) linear regression models predicated on
historical data. The findings reveal that ETH staking rewards can be forecasted
with an RMSE within 0.7% and 1.1% of the mean value for 1-day and 7-day
look-aheads respectively, using a 7-day sliding-window average approach.
Additionally, we discern diverse prediction accuracies across various
cryptocurrencies, including SOL, XTZ, ATOM, and MATIC. Linear regression is
identified as superior to the moving-window average for perdicting in the short
term for XTZ and ATOM. The results underscore the generally stable and
predictable nature of staking rewards for most assets, with MATIC presenting a
noteworthy exception.

探究预测加密货币质押奖励的一个相对未被研究的领域，结果显示可使用滑动窗口平均值方法，在 1 天和 7 天前瞻中将 ETH 质押奖励预测误差控制在均值的 0.7% 和 1.1% 内。此外，我们发现不同加密货币（包括 SOL、XTZ、ATOM 和 MATIC）的预测准确性存在差异，对于 XTZ 和 ATOM，在短期预测中，线性回归模型优于滑动窗口平均值。研究结果强调了大多数资产质押奖励的稳定性和可预测性，但 MATIC 则是一个显著的例外。

加密货币质押奖励预测

Forecasting Cryptocurrency Staking Rewards

Neural architecture search (NAS) has shown promising results discovering
models that are both accurate and fast. For NAS, training a one-shot model has
become a popular strategy to rank the relative quality of different
architectures (child models) using a single set of shared weights. However,
while one-shot model weights can effectively rank different network
architectures, the absolute accuracies from these shared weights are typically
far below those obtained from stand-alone training. To compensate, existing
methods assume that the weights must be retrained, finetuned, or otherwise
post-processed after the search is completed. These steps significantly
increase the compute requirements and complexity of the architecture search and
model deployment. In this work, we propose BigNAS, an approach that challenges
the conventional wisdom that post-processing of the weights is necessary to get
good prediction accuracies. Without extra retraining or post-processing steps,
we are able to train a single set of shared weights on ImageNet and use these
weights to obtain child models whose sizes range from 200 to 1000 MFLOPs. Our
discovered model family, BigNASModels, achieve top-1 accuracies ranging from
76.5% to 80.9%, surpassing state-of-the-art models in this range including
EfficientNets and Once-for-All networks without extra retraining or
post-processing. We present ablative study and analysis to further understand
the proposed BigNASModels.

本文提出一种新方法 BigNAS，使用单个共享权重集合无需进行额外的训练或后处理步骤，就可以用于神经结构搜索，该方法的预测准确率优于现有方法。

BigNAS：利用大型单级模型扩展神经架构搜索

BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage  Models

Social media is increasingly used for large-scale population predictions,
such as estimating community health statistics. However, social media users are
not typically a representative sample of the intended population -- a
"selection bias". Within the social sciences, such a bias is typically
addressed with restratification techniques, where observations are reweighted
according to how under- or over-sampled their socio-demographic groups are.
Yet, restratifaction is rarely evaluated for improving prediction. In this
two-part study, we first evaluate standard, "out-of-the-box" restratification
techniques, finding they provide no improvement and often even degraded
prediction accuracies across four tasks of esimating U.S. county population
health statistics from Twitter. The core reasons for degraded performance seem
to be tied to their reliance on either sparse or shrunken estimates of each
population's socio-demographics. In the second part of our study, we develop
and evaluate Robust Poststratification, which consists of three methods to
address these problems: (1) estimator redistribution to account for shrinking,
as well as (2) adaptive binning and (3) informed smoothing to handle sparse
socio-demographic estimates. We show that each of these methods leads to
significant improvement in prediction accuracies over the standard
restratification approaches. Taken together, Robust Poststratification enables
state-of-the-art prediction accuracies, yielding a 53.0% increase in variance
explained (R^2) in the case of surveyed life satisfaction, and a 17.8% average
increase across all tasks.

研究评估了标准的限制分层技术，发现它们不能提高对美国县级人口健康状况统计数据的预测准确性；该研究提出了三种方法组成的鲁棒后分层技术，显著提高了预测精度。

纠正社会人口选择偏误进行社交媒体人口预测

Correcting Sociodemographic Selection Biases for Population Prediction  from Social Media

Nowcasting based on social media text promises to provide unobtrusive and
near real-time predictions of community-level outcomes. These outcomes are
typically regarding people, but the data is often aggregated without regard to
users in the Twitter populations of each community. This paper describes a
simple yet effective method for building community-level models using Twitter
language aggregated by user. Results on four different U.S. county-level tasks,
spanning demographic, health, and psychological outcomes show large and
consistent improvements in prediction accuracies (e.g. from Pearson r=.73 to
.82 for median income prediction or r=.37 to .47 for life satisfaction
prediction) over the standard approach of aggregating all tweets. We make our
aggregated and anonymized community-level data, derived from 37 billion tweets
-- over 1 billion of which were mapped to counties, available for research.

该论文介绍了一个利用 Twitter 用户聚合的方法来构建社区级别模型，可以有效的准确预测人口相关，包括人口统计、健康和心理预测等四个不同领域的结果（例如，可以将用于预测收入中位数的皮尔逊相关系数从 0.73 提升到 0.82）。