We introduce NotaGen, a symbolic music generation model aiming to explore the potential of producing high-quality classical sheet music. Inspired by the success of Large Language Models (LLMs), NotaGen adopts pre-training, fine-tuning, and reinforcement learning paradigms (henceforth referred to as the LLM training paradigms). It is pre-trained on 1.6M pieces of music in ABC notation, and then fine-tuned on approximately 9K high-quality classical compositions conditioned on "period-composer-instrumentation" prompts. For reinforcement learning, we propose the CLaMP-DPO method, which further enhances generation quality and controllability without requiring human annotations or predefined rewards. Our experiments demonstrate the efficacy of CLaMP-DPO in symbolic music generation models with different architectures and encoding schemes. Furthermore, subjective A/B tests show that NotaGen outperforms baseline models against human compositions, greatly advancing musical aesthetics in symbolic music generation.

Since chronic loneliness is both a painful individual experience and an increasingly serious social problem, robot companions have emerged as a result of robotization of social work to confront this issue. We foresee that social robots will become pervasive in the near future. Thus, it is crucial to pinpoint the relationship between chronic experiences of loneliness (i.e., trait loneliness) and both anthropomorphism and acceptance of such artificial intelligent agents. Previous research demonstrated that experimentally induced state loneliness increases anthropomorphic inferences about nonhuman agents such as pets. However, in the present research we found that trait (vs. state) loneliness - a permanent personality disposition that is not easily relieved (vs. transitory experiences caused by circumstance, and easily relieved) - reduced participants' anthropomorphic tendencies and acceptance of a social robot (regardless of the form: a picture of the robot, an on-site robot, or direct interaction with the robot). In particular, believing that the robot lacks good "unique humanness" traits (i.e., Humble, Thorough, Organized, Broadminded, and Polite) is one reason why dispositionally lonely participants are less likely to anthropomorphize a robot, which further prompts reduced acceptance of it. This finding suggests that unique humanness, exemplifying secondary emotions, is vital, not only in interpersonal contexts, but in establishing connections with social robots.

In this paper, we present a cartoon face animation system for multimedia HCI applications. We animate face cartoons not only from input speech, but also based on emotions derived from speech signal. Using a corpus of over 700 utterances from different speakers, we have trained SVMs (support vector machines) to recognize four categories of emotions: neutral, happiness, anger and sadness. Given each input speech phrase, we identify its emotion content as a mixture of all four emotions, rather than classifying it into a single emotion. Then, facial expressions are= generated from the recovered emotion for each phrase, by morphing different cartoon templates that correspond to various emotions. To ensure smooth transitions in the animation, we apply low-pass filtering to the recovered (and possibly jumpy) emotion sequence. Moreover, lip-syncing is applied to produce the lip movement from speech, by recovering a statistical audio-visual mapping. Experimental results demonstrate that cartoon animation sequences generated by our system are of good and convincing quality.