instruction:
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
question:
Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification. This new method has been tested on text-dependent speaker verification tasks, and improvement was reported when combined with the conventional i-vector method. This paper extends the d-vector approach to semi textindependent speaker verification tasks, i.e., the text of the speech is in a limited set of short phrases. We explore various settings of the DNN structure used for d-vector extraction, and present a phone-dependent training which employs the posterior features obtained from an ASR system. The experimental results show that it is possible to apply d-vectors on semi text-independent speaker recognition, and the phone-dependent training improves system performance.
answer:
Deep Speaker Vectors for Semi Text-independent Speaker Verification


question:
The Complete Tang Poems (CTP) is the most important source to study Tang poems. We look into CTP with computational tools from specific linguistic perspectives, including distributional semantics and collocational analysis. From such quantitative viewpoints, we compare the usage of “wind” and “moon” in the poems of Li Bai (李白) and Du Fu (杜甫). Colors in poems function like sounds in movies, and play a crucial role in the imageries of poems. Thus, words for colors are studied, and “白” (bai2, white) is the main focus because it is the most frequent color in CTP. We also explore some cases of using colored words in antithesis(對仗) pairs that were central for fostering the imageries of the poems. CTP also contains useful historical information, and we extract person names in CTP to study the social networks of the Tang poets. Such information can then be integrated with the China Biographical Database of Harvard University.
answer:
Color Aesthetics and Social Networks in Complete Tang Poems: Explorations and Discoveries


question:
<lb>We study the best arm identification (Best-1-Arm) problem, which is defined as follows.<lb>We are given n stochastic bandit arms. The ith arm has a reward distribution<lb>Di with an<lb>unknown mean μi. Upon each play of the ith arm, we can get a reward, sampled i.i.d. from<lb>Di. We would like to identify the arm with the largest mean with probability at least 1 − δ,<lb>using as few samples as possible. We provide a nontrivial algorithm for Best-1-Arm, which<lb>improves upon several prior upper bounds on the same problem. We also study an important<lb>special case where there are only two arms, which we call the Sign-ξ problem. We provide a<lb>new lower bound of Sign-ξ, simplifying and significantly extending a classical result by Farrell<lb>in 1964, with a completely new proof. Using the new lower bound for Sign-ξ, we obtain the<lb>first lower bound for Best-1-Arm that goes beyond the classic Mannor-Tsitsiklis lower bound,<lb>by an interesting reduction from Sign-ξ to Best-1-Arm. We propose an interesting conjecture<lb>concerning the optimal sample complexity of Best-1-Arm from the perspective of instance-wise<lb>optimality.
answer:

On the Optimal Sample Complexity for Best Arm Identification