In cross-modal music processing, translation between visual, auditory, and semantic content opens up new possibilities as well as challenges. The construction of such a transformative scheme depends upon a benchmark corpus with a comprehensive data infrastructure. In particular, the assembly of a large-scale cross-modal dataset presents major challenges. In this paper, we present the MOSA (Music mOtion with Semantic Annotation) dataset, which contains high quality 3-D motion capture data, aligned audio recordings, and note-by-note semantic annotations of pitch, beat, phrase, dynamic, articulation, and harmony for 742 professional music performances by 23 professional musicians, comprising more than 30 hours and 570 K notes of data. To our knowledge, this is the largest cross-modal music dataset with note-level annotations to date. To demonstrate the usage of the MOSA dataset, we present several innovative cross-modal music information retrieval (MIR) and musical content generation tasks, including the detection of beats, downbeats, phrases, and expressive contents from audio, video and motion data, and the generation of musicians' body motion from given music audio. The dataset and codes are available alongside this publication (https://github.com/yufenhuang/MOSA-Music-mOtion-and-Semantic-Annotation-dataset).

In a music scenario, both auditory and visual elements are essential to achieve an outstanding performance. Recent research has focused on the generation of body movements or fingering from audio in music performance. The audio-driven face generation technique in music performance is still deficient. In this paper, we compile a violin soundtrack and facial expression dataset (VSFE) for modeling facial expressions in violin performance. To our knowledge, this is the first dataset mapping the relationship between violin performance audio and musicians’ facial expressions. We then propose a 3DCNN network with self-attention and residual blocks for audio-driven facial expression generation. In the experiments, we compare our methods with three baselines on talking face generation. The codes and dataset are available on the Github (https://github.com/kevinlin91/icassp_music2face).

extension to LLVM for solving the blocking between instruction sink and division-modulo combine,

We revisit the satisfiability problem for two-variable logic, denoted by SAT(FO2), which is known to be NEXP-complete. The upper bound is usually derived from its well known exponential size model property. Whether it can be determinized/randomized efficiently is still an open question.In this paper we present a different approach by reducing it to a novel graph-theoretic problem that we call Conditional Independent Set (CIS). We show that CIS is NP-complete and present three simple algorithms for it: Deterministic, randomized with zero error and randomized with small one-sided error, with run time O(1.4423n), O(1.6181n) and O(1.3661n), respectively.We then show that without the equality predicate SAT(FO2) is in fact equivalent to CIS in succinct representation. This yields the same three simple algorithms as above for SAT(FO2) without the the equality predicate with run time O(1.4423(2n)), O(1.6181(2n)) and O(1.3661(2n)), respectively, where n is the number of predicates in the input formula. To the best of our knowledge, these are the first deterministic/randomized algorithms for an NEXP-complete decidable logic with time complexity significantly lower than O(2(2n)). We also identify a few lower complexity fragments of FO2which correspond to the tractable fragments of CIS.For the fragment with the equality predicate, we present a linear time many-one reduction to the fragment without the equality predicate. The reduction yields equi-satisfiable formulas and incurs a small constant blow-up in the number of predicates.

Nanofluid/ultrasonic atomization minimal quantity lubrication (MQL) was used in an SKD11 steel micromilling process with ultrasonic dispersion. The nanofluid atomizer substantially improved nanofluid reunification and delivery to the cutting zone. The micromilling cutting force and temperature were used to calculate the signal-to-noise ratio (S/N). Normalization of the S/N in the gray relational analysis obtained a gray correlation coefficient, which is used in a fuzzy analysis to obtain a multiple performance characteristic indexes (MPCI). The MPCI is maximized by optimizing the process parameters for multiple quality characteristics and then comparing the optimized parameters with those in the MQL system. Experimental results indicate that the nanofluid parameters containing the density of nanofluid, feed rate, and distance of nozzle are the most influential control factors, while 3.617 N and 117.1 °C, respectively, are the best combination of parameters. Different lubricating methods are also compared in terms of effects on micromilling cutting force, micromilling temperature, micromilling tool wear, and surface burr. Experiments show that nanofluid multiwall carbon nanotube/ultrasonic atomization MQL obtained the best results.

Parent-child communication is an essential element in behavioral and character development in early childhood; however, parents may find it difficult to be aware of how they talk to their children. Through extensive field studies with experts and parents, we found that parents are more likely to experience communication conflict with preschool children (3-6 years old) on school mornings. In consultation with domain professionals and families, we designed WAKEY, a technology-based approach that helps parents use better communication strategies to teach preschool children to carry out their morning routines. Following the intervention with WAKEY, parents reported significantly reduced levels of frustration during morning routines and greater independent behavior by children. Furthermore, parents reported experiencing changes in their parenting attitudes and finding new insights into communication.

The goal of this paper is to explore whether the premarket regulatory system of the United States functions ideally in facing the emergence of commercial computer-aided diagnosis (CAD) systems for medical imaging. To outline the commercial CAD systems available in the United States, clinical trials published in PubMed and EMBASE from 2012 to 2016 that investigated the clinical competence of commercial CAD products were obtained, and the product information provided in these studies was searched in the Establishment Registration & Device Listing database, the Releasable 510(k) Premarket Notification database, and the Premarket Approval (PMA) database of the FDA to trace the processes through which such CAD systems entered the healthcare market. A review of current premarket regulatory system for medical devices, and the potential problems that may hinder the social and clinical integration of CAD systems are presented. We noticed expansion of regulatory definition and variation of device classes and product codes among CAD systems with similar clinical uses, which may compromise the efficacy of such regulatory controls. The results suggested ineffectiveness of current premarket regulatory controls for CAD systems in the United States.

Linear Classification has achieved complexity linear to the data size. However, in many applications, data contain large amount of samples that does not help improve the quality of model, but still cost much I/O and memory to process. In this paper, we show how a Block Coordinate Descent method based on Nearest-Neighbor Index can significantly reduce such cost when learning a dual-sparse model. In particular, we employ truncated loss function to induce a series of convex programs with superior dual sparsity, and solve each dual using Indexed Block Coordinate Descent, which makes use of Approximate Nearest Neighbor (ANN) search to select active dual variables without I/O cost on irrelevant samples. We prove that, despite the bias and weak guarantee from ANN query, the proposed algorithm has global convergence to the solution defined on entire dataset, with sublinear complexity each iteration. Experiments in both sufficient and limited memory conditions show that the proposed approach learns many times faster than other state-of-the-art solvers without sacrificing accuracy.

In this paper, we first introduce the traffic status estimation service, a location-based continuous range query application provided by the CarWeb system. The service provides users nearby realtime traffic estimation according to their locations and moving behaviors. This paper focuses on how to provide users with the most relevant information efficiently and accurately. We propose the Continuous Query with Dynamic Fan-Shape framework (CQ-DFS) which dynamically changes parameters of fan shape range query to efficiently acquire more useful query results without compromising the performance of the system. We conduct extensive experiments to demonstrate the effectiveness of CQ-DFS framework by using real world vehicle trajectories.

In this paper, we develop a framework of trajectory search called pattern-aware trajectory search (abbreviated as PATS). Given a set of trajectories, potential regions are extracted first and potential regions are viewed as popular regions interested by users. Furthermore, potential regions are organized as a region transition graph where each vertex is a potential region and edges capture sequential travels of potential regions from a set of trajectories given. By exploring the concept of random walk, the attractiveness of a potential region is derived. In light of attractiveness of potential regions the attractiveness of a trajectory is formulated and PATS will return top-K trajectories according to their attractiveness. We evaluated our framework by a real GPS dataset. Experimental results show that PATS is able to retrieve trajectories interested by users.