One of the common hazards and issues in meteorology and agriculture is the
problem of frost, chilling or freezing. This event occurs when the minimum
ambient temperature falls below a certain value. This phenomenon causes a lot
of damage to the country, especially Fars province. Solving this problem
requires that, in addition to predicting the minimum temperature, we can
provide enough time to implement the necessary measures. Empirical methods have
been provided by the Food and Agriculture Organization (FAO), which can predict
the minimum temperature, but not in time. In addition to this, we can use
machine learning methods to model the minimum temperature. In this study, we
have used three methods Gated Recurrent Unit (GRU), Temporal Convolutional
Network (TCN) as deep learning methods, and Gradient Boosting (XGBoost). A
customized loss function designed for methods based on deep learning, which can
be effective in reducing prediction errors. With methods based on deep learning
models, not only do we observe a reduction in RMSE error compared to empirical
methods but also have more time to predict minimum temperature. Thus, we can
model the minimum temperature for the next 24 hours by having the current 24
hours. With the gradient boosting model (XGBoost) we can keep the prediction
time as deep learning and RMSE error reduced. Finally, we experimentally
concluded that machine learning methods work better than empirical methods and
XGBoost model can have better performance in this problem among other
implemented.

气象和农业中常见的危害和问题之一是霜冻、寒冷或冻结的问题。本研究使用了三种方法，包括门控循环单元（GRU）、时间卷积网络（TCN）和梯度提升（XGBoost），证明基于机器学习方法的最低温度建模比经验方法具有更好的性能。

利用机器学习方法预测法尔斯省的霜冻

Frost Prediction Using Machine Learning Methods in Fars Province

Efficient and accurate BRDF acquisition of real world materials is a
challenging research problem that requires sampling millions of incident light
and viewing directions. To accelerate the acquisition process, one needs to
find a minimal set of sampling directions such that the recovery of the full
BRDF is accurate and robust given such samples. In this paper, we formulate
BRDF acquisition as a compressed sensing problem, where the sensing operator is
one that performs sub-sampling of the BRDF signal according to a set of optimal
sample directions. To solve this problem, we propose the Fast and Robust
Optimal Sampling Technique (FROST) for designing a provably optimal
sub-sampling operator that places light-view samples such that the recovery
error is minimized. FROST casts the problem of designing an optimal
sub-sampling operator for compressed sensing into a sparse representation
formulation under the Multiple Measurement Vector (MMV) signal model. The
proposed reformulation is exact, i.e. without any approximations, hence it
converts an intractable combinatorial problem into one that can be solved with
standard optimization techniques. As a result, FROST is accompanied by strong
theoretical guarantees from the field of compressed sensing. We perform a
thorough analysis of FROST-BRDF using a 10-fold cross-validation with publicly
available BRDF datasets and show significant advantages compared to the
state-of-the-art with respect to reconstruction quality. Finally, FROST is
simple, both conceptually and in terms of implementation, it produces
consistent results at each run, and it is at least two orders of magnitude
faster than the prior art.

通过压缩感知技术中的稀疏表示方法，本论文提出了 Fast and Robust Optimal Sampling Technique (FROST) 用于设计一种能够以最小化恢复误差的方式放置采样方向的压缩采样算子，从而加速 BRDF 材料的获取，具有从压缩感知领域得到的强理论保证和与现有方法相比的显著优势。

FROST-BRDF: 快速且稳健的 BRDF 采样技术

FROST-BRDF: A Fast and Robust Optimal Sampling Technique for BRDF  Acquisition

The Open Radio Access Network (O-RAN) is a burgeoning market with projected
growth in the upcoming years. RAN has the highest CAPEX impact on the network
and, most importantly, consumes 73% of its total energy. That makes it an ideal
target for optimisation through the integration of Machine Learning (ML).
However, the energy consumption of ML is frequently overlooked in such
ecosystems. Our work addresses this critical aspect by presenting FROST -
Flexible Reconfiguration method with Online System Tuning - a solution for
energy-aware ML pipelines that adhere to O-RAN's specifications and principles.
FROST is capable of profiling the energy consumption of an ML pipeline and
optimising the hardware accordingly, thereby limiting the power draw. Our
findings indicate that FROST can achieve energy savings of up to 26.4% without
compromising the model's accuracy or introducing significant time delays.

通过 FROST（Flexible Reconfiguration method with Online System Tuning），我们提出了一种能源感知的机器学习管道解决方案，可根据 O-RAN 的规范和原则对能耗进行优化，从而实现高达 26.4% 的能源节省而不降低模型的准确性或引入显著的时间延迟。