The unit selection problem defined by Li and Pearl identifies individuals who
have desired counterfactual behavior patterns, for example, individuals who
would respond positively if encouraged and would not otherwise. Li and Pearl
showed by example that their unit selection model is beyond the A/B test
heuristics. In this paper, we reveal the essence of the A/B test heuristics,
which are exceptional cases of the benefit function defined by Li and Pearl.
Furthermore, We provided more simulated use cases of Li-Pearl's unit selection
model to help decision-makers apply their model correctly, explaining that A/B
test heuristics are generally problematic.

该研究介绍了 Li 和 Pearl 所定义的单元选择问题，针对有所需反事实行为模式的人进行筛选，并且比较了该模型与 A/B 测试的区别和优越性，同时提供了更多模拟用例，并指出 A/B 测试普遍存在问题。

单元选择：案例研究及与 A/B 测试启发式算法的比较

Unit Selection: Case Study and Comparison with A/B Test Heuristic

Automatic speech recognition (ASR) in Sanskrit is interesting, owing to the
various linguistic peculiarities present in the language. The Sanskrit language
is lexically productive, undergoes euphonic assimilation of phones at the word
boundaries and exhibits variations in spelling conventions and in
pronunciations. In this work, we propose the first large scale study of
automatic speech recognition (ASR) in Sanskrit, with an emphasis on the impact
of unit selection in Sanskrit ASR. In this work, we release a 78 hour ASR
dataset for Sanskrit, which faithfully captures several of the linguistic
characteristics expressed by the language. We investigate the role of different
acoustic model and language model units in ASR systems for Sanskrit. We also
propose a new modelling unit, inspired by the syllable level unit selection,
that captures character sequences from one vowel in the word to the next vowel.
We also highlight the importance of choosing graphemic representations for
Sanskrit and show the impact of this choice on word error rates (WER). Finally,
we extend these insights from Sanskrit ASR for building ASR systems in two
other Indic languages, Gujarati and Telugu. For both these languages, our
experimental results show that the use of phonetic based graphemic
representations in ASR results in performance improvements as compared to ASR
systems that use native scripts.

本文对梵语 ASR 进行了首次大规模研究，研究了单元选择对梵语 ASR 的影响，并发布了 78 小时的梵语 ASR 数据集，研究不同声学模型和语言模型单元在 ASR 系统中的角色，提出了一个新的模型单元，并强调选择语文文字表示对词错误率的影响。

梵语自动语音识别：新的语音语料库和建模见解

Automatic Speech Recognition in Sanskrit: A New Speech Corpus and  Modelling Insights

Several methods exist for a computer to generate music based on data
including Markov chains, recurrent neural networks, recombinancy, and grammars.
We explore the use of unit selection and concatenation as a means of generating
music using a procedure based on ranking, where, we consider a unit to be a
variable length number of measures of music. We first examine whether a unit
selection method, that is restricted to a finite size unit library, can be
sufficient for encompassing a wide spectrum of music. We do this by developing
a deep autoencoder that encodes a musical input and reconstructs the input by
selecting from the library. We then describe a generative model that combines a
deep structured semantic model (DSSM) with an LSTM to predict the next unit,
where units consist of four, two, and one measures of music. We evaluate the
generative model using objective metrics including mean rank and accuracy and
with a subjective listening test in which expert musicians are asked to
complete a forced-choiced ranking task. We compare our model to a note-level
generative baseline that consists of a stacked LSTM trained to predict forward
by one note.

该研究探索了利用单位选择和连接作为生成音乐的手段，结合深度结构语义模型和 LSTM 预测下一个单位的生成模型，使用客观指标和专业音乐人听辨测试进行评估。