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Looks like a complex system and management program. However, it's a lot easier to manage if we form some small parts into a big group. I also feel that HRM has more influence on a company's performance. I guess this is like using people "efficiently" to get the most out of their work.Given the task definition, example input & output, solve the new input case.
Given a sentence in the Japanese, provide an equivalent translation in Filipino that retains the same meaning through the translation. In translation, keep numbers as it is.
Example: フランスのパリ、パルク・デ・プランスで行われた2007年ラグビーワールドカップのプールCで、イタリアは31対5でポルトガルを下した。
Output: Natalo ng Italya ang Portugal sa puntos na 31-5 sa Grupong C noong 2007 sa Pandaigdigang laro ng Ragbi sa Parc des Princes, Paris, France.
This is a correct and accurate translation from Japanese to Filipino because translated text is just paraphrased of Japanese sentence. Also, it preserves the numbers as it is.

New input case for you: 過去1年間にわたって、さまざまな国が懸念を示すか、もしくはソマリアの海賊対応に海軍を派遣してきた。
Output:
Noong nakaraang ilang mga taon ipinahayag ng maraming bansa ang kanilang pagkabahala at inilagak nila ang kanilang mga hukbong-dagat upang makitungo sa mga pirata ng Somalia.MANDARIN BEGINNER 2: If you know pin yin and have some very rudimentary vocabulary such as describing people and talking about family but still struggle to hold a conversation and you are ready to pick up at the middle of Chit Chat Chinese, this level is for you.
will we learn characters?
Hi Anthony! The group classes of this school are a blend between the formal approach - focus on the tones, the structure of the language and in case of Mandarin, pinyin and simplified characters - and the interactive approach, that promotes situations with verbal interaction, which is the focus of our classes. That said, the school do not prioritize written skills and do not practice it in class. Students can do so at home.
Question from Teresa D.
There is any Chinese class on Friday, Saturday or Sunday?
Hi Teresa! I hope these two links would help. https://coursehorse.com/nyc/classes/language/chinese-mandarin/basic-chinese-mandarin/mandarin-chinese-total-beginner1 & https://coursehorse.com/nyc/classes/language/chinese-mandarin/basic-chinese-mandarin/all-about-mandarin-an-intro-workshop.
will we learn any characters?
.
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Nexus Startup wins at the Regional Global Student Entrepreneur Awards!.Despite what most folks think, the science of robotics is not based on mobility and motion alone. Say the word “robot” to most anyone on the street and they will assimilate the word robot to a mobile humanoid-like mechanism that has superhuman intelligence and strength. In reality, Earth-bound robots have the ability to weld, cut, saw, drill, wash dishes, wash your underwear, wash your car, and cook your dinner...
\section{Introduction}\label{intro}}

The projection predictive variable selection (\protect\hyperlink{ref-catalina_projection_2022}{Catalina et al., 2022}; \protect\hyperlink{ref-piironen_projective_2020}{Piironen et al., 2020}) is a special predictive model selection method (\protect\hyperlink{ref-vehtari_survey_2012}{Vehtari and Ojanen, 2012}) for Bayesian regression models that comes with valid post-selection inference and has been shown to perform better---in general---than alternative methods (\protect\hyperlink{ref-piironen_comparison_2017}{Piironen and Vehtari, 2017b}).
So far, the implementation of the projection predictive variable selection in the R (\protect\hyperlink{ref-r_core_team_r_2022}{R Core Team, 2022}) package \textbf{projpred} (\protect\hyperlink{ref-piironen_projpred_2022}{Piironen et al., 2022}) has been restricted to the Gaussian, the binomial, and the Poisson response families.
Recently, the \emph{latent projection} (\protect\hyperlink{ref-catalina_latent_2021}{Catalina et al., 2021}) has extended the range of possible response families considerably, for example, to the ordinal family underlying \texttt{MASS::polr()} (\protect\hyperlink{ref-venables_modern_2002}{Venables and Ripley, 2002}).
However, the latent projection is an approximate approach as it replaces the original projection problem with a latent projection problem.
Here (section~\ref{math}), we present the exact solution to the original projection problem for discrete finite-support response families and call the corresponding procedure the \emph{augmented-data projection}.

For investigating the performance of the augmented-data projection (section~\ref{sim}), we confine ourselves to a simulation study comparing the augmented-data projection to the latent projection because the generally superior performance of the projection predictive variable selection based on the traditional projection and based on the latent projection has already been demonstrated by Piironen and Vehtari (\protect\hyperlink{ref-piironen_comparison_2017}{2017b}) and Catalina et al. (\protect\hyperlink{ref-catalina_latent_2021}{2021}), respectively.

Finally, our work is discussed in section~\ref{disc}, where we also mention possible modifications of the augmented-data projection to extend it to more response families in the future.

\hypertarget{math}{%
\section{Augmented-data projection}\label{math}}

\hypertarget{math-notation}{%
\subsection{Notation}\label{math-notation}}

For the following mathematical presentation of the augmented-data projection (a special case of the general approach that is presented first), we assume the availability of a dataset with \(N\) observations.
The observed response vector will be denoted by \(\boldsymbol{y} = (y_1, \dotsc, y_N)^{\scriptscriptstyle \mathsf{T}} \in \mathcal{Y}^{N} \subseteq \mathbb{R}^{N}\).
We do not introduce any notation for the corresponding predictor data as we will always be conditioning implicitly on it.
By \(\boldsymbol{\tilde{y}} = (\tilde{y}_1, \dotsc, \tilde{y}_N)^{\scriptscriptstyle \mathsf{T}}\), we will denote \emph{unobserved} response values at the same observed predictor values, with realizations in \(\mathcal{Y}^{N}\).

A crucial part (\protect\hyperlink{ref-pavone_using_2022}{Pavone et al., 2022}; \protect\hyperlink{ref-piironen_projective_2020}{Piironen et al., 2020}) for the superior performance of the projection predictive variable selection is the \emph{reference model}, which is the best possible model (in terms of predictive performance) one can construct.
For \textbf{projpred}, the reference model is usually fitted within \textbf{rstanarm} (\protect\hyperlink{ref-goodrich_rstanarm_2022}{Goodrich et al., 2022}) or \textbf{brms} (\protect\hyperlink{ref-burkner_brms_2017}{Bürkner, 2017}, \protect\hyperlink{ref-burkner_advanced_2018}{2018}) which both rely on Stan (\protect\hyperlink{ref-carpenter_stan_2017}{Carpenter et al., 2017}; \protect\hyperlink{ref-stan_development_team_stan_2022}{Stan Development Team, 2022b}), a probabilistic programming language and software that is mainly used for its dynamic Hamiltonian Monte Carlo (HMC) algorithm, a modern Markov chain Monte Carlo (MCMC) sampler.
However, the methodology behind \textbf{projpred} is more general and does not require the reference model to be fitted within \textbf{rstanarm} or \textbf{br