SemanticBoost: Elevating Motion Generation with Augmented Textual Cues

Current techniques face difficulties in generating motions from intricate semantic descriptions, primarily due to insufficient semantic annotations in datasets and weak contextual understanding. To address these issues, we present SemanticBoost, a novel framework that tackles both challenges simultaneously. Our framework comprises a Semantic Enhancement module and a Context-Attuned Motion Denoiser (CAMD). The Semantic Enhancement module extracts supplementary semantics from motion data, enriching the dataset's textual description and ensuring precise alignment between text and motion data without depending on large language models. On the other hand, the CAMD approach provides an all-encompassing solution for generating high-quality, semantically consistent motion sequences by effectively capturing context information and aligning the generated motion with the given textual descriptions. Distinct from existing methods, our approach can synthesize accurate orientational movements, combined motions based on specific body part descriptions, and motions generated from complex, extended sentences. Our experimental results demonstrate that SemanticBoost, as a diffusion-based method, outperforms auto-regressive-based techniques, achieving cutting-edge performance on the Humanml3D dataset while maintaining realistic and smooth motion generation quality

3-Methyl-1-(3-nitro­phen­yl)-5-phenyl-4,5-dihydro-1H-pyrazole

In the title compound, C16H15N3O2, the planar [maximum deviation 0.156 (2) Å] pyrazoline ring is nearly coplanar with the 3-nitro­phenyl group and is approximately perpendicular to the phenyl ring, making dihedral angles of 3.80 (8) and 80.58 (10)°, respectively. Weak inter­molecular C—H⋯O hydrogen bonding is present in the crystal structure

RESEARCH ON THE MARKETING AND PUBLIC RELATIONS EFFECT AND SPORT EVENT SATISFACTION OF THE TAIPEI 2017 UNIVERSIADE

The purpose of this study is to investigate the spectators’ marketing and public relations and sport event satisfaction with their participation in the Taipei 2017 Universiade. Meanwhile, based on the comparison of different personal background variables, this study compares the attractiveness and satisfaction of the spectators’ marketing and public relations recognition, marketing and public relations attitude, event planning and sport event services. A random sampling method is adopted in this study. Among spectators, university students of the Taipei 2017 Universiade are selected. A total of 700 questionnaires are distributed and 680 valid questionnaires are collected. The effective recovery rate is 97.14%. The research tool of this study is “Satisfaction scale of marketing and public relations effect and sport event satisfaction of the Taipei 2017 Universiade”. This study uses statistical methods such as descriptive statistics, independent sample t-tests, and so forth. The results of this study are: (1) In the Taipei 2017 Universiade, spectators have the highest attractiveness with “Internet” in “media tools” of marketing and public relations recognition, followed by the factor of “TV”; (2) In “marketing and public relations attitude” of the Taipei 2017 Universiade, “marketing and public relations present efforts and earnest of Taiwan” ranks the highest, followed by “marketing and public relations are impressed”; (3) In “sport event services” of the Taipei 2017 Universiade, “auditorium” ranks the highest, followed by “broadcast notification”; (4) There is no significant difference in the attractiveness and satisfaction among spectators with different personal background for “marketing and public relations recognition”, “marketing and public relations attitude”, and “sport event services” in the Taipei 2017 Universiade.  Article visualizations

5-(2-Fur­yl)-3-methyl-1-(3-nitro­phen­yl)-4,5-dihydro-1H-pyrazole

In the title compound, C14H13N3O3, the pyrazoline ring assumes an envelope conformation with the furanyl-bearing C atom at the flap position. The dihedral angle between the furan and nitrobenzene rings is 84.40 (9)°. Weak inter­molecular C—H⋯O hydrogen bonding is present in the crystal structure

On the Universal Adversarial Perturbations for Efficient Data-free Adversarial Detection

Detecting adversarial samples that are carefully crafted to fool the model is a critical step to socially-secure applications. However, existing adversarial detection methods require access to sufficient training data, which brings noteworthy concerns regarding privacy leakage and generalizability. In this work, we validate that the adversarial sample generated by attack algorithms is strongly related to a specific vector in the high-dimensional inputs. Such vectors, namely UAPs (Universal Adversarial Perturbations), can be calculated without original training data. Based on this discovery, we propose a data-agnostic adversarial detection framework, which induces different responses between normal and adversarial samples to UAPs. Experimental results show that our method achieves competitive detection performance on various text classification tasks, and maintains an equivalent time consumption to normal inference.Comment: Accepted by ACL2023 (Short Paper

Developing Routinized Information Processing Capabilities for Operational Agility: Insights from China

Operational agility, which reflects the agile practices at business process level, is increasingly deemed as a significant determinant of business success in a turbulent business environment. Despite its importance, how operational agility can be attained is not answered by existing research. Drawing on the classic organization theory—information processing view of firms, the main contribution of this study is that it provides a process model of developing routinized information processing capabilities for operational agility in a turbulent business environment which fulfills this theoretical gap. It indicates the significant roles played by IT-enabled information processing networks and organizational controls during the process. It also identifies three routinized information processing capabilities including information sensitivity, information fluidity, and information decomposability. This is achieved by conducting a case study of Haier, one of the largest producers of household appliances in China. This paper concludes with a discussion of potential theoretical and practical contributions

TapMo: Shape-aware Motion Generation of Skeleton-free Characters

Previous motion generation methods are limited to the pre-rigged 3D human model, hindering their applications in the animation of various non-rigged characters. In this work, we present TapMo, a Text-driven Animation Pipeline for synthesizing Motion in a broad spectrum of skeleton-free 3D characters. The pivotal innovation in TapMo is its use of shape deformation-aware features as a condition to guide the diffusion model, thereby enabling the generation of mesh-specific motions for various characters. Specifically, TapMo comprises two main components - Mesh Handle Predictor and Shape-aware Diffusion Module. Mesh Handle Predictor predicts the skinning weights and clusters mesh vertices into adaptive handles for deformation control, which eliminates the need for traditional skeletal rigging. Shape-aware Motion Diffusion synthesizes motion with mesh-specific adaptations. This module employs text-guided motions and mesh features extracted during the first stage, preserving the geometric integrity of the animations by accounting for the character's shape and deformation. Trained in a weakly-supervised manner, TapMo can accommodate a multitude of non-human meshes, both with and without associated text motions. We demonstrate the effectiveness and generalizability of TapMo through rigorous qualitative and quantitative experiments. Our results reveal that TapMo consistently outperforms existing auto-animation methods, delivering superior-quality animations for both seen or unseen heterogeneous 3D characters

Serum selenium concentration is associated with metabolic factors in the elderly: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Selenium is an essential micronutrient known for its antioxidant function. However, the association of serum selenium with lipid profiles and fasting glucose are inconsistent in populations with average intake of selenium. Furthermore, there were few studies conducted specifically for the elderly. This study examined the relationship of serum selenium concentration with serum lipids and fasting glucose in the Taiwanese elderly population.</p> <p>Methods</p> <p>This was a cross-sectional study of 200 males and females aged 65-85 years (mean 71.5 ± 4.6 years) from Taipei, Taiwan. Serum selenium was measured by inductively coupled plasma-mass spectrometer. The association between serum selenium and metabolic factors was examined using a multivariate linear regression analysis after controlling several confounders.</p> <p>Results</p> <p>The mean serum selenium concentration was 1.14 μmol/L, without significant difference between sexes. Total cholesterol, triglycerides, and LDL cholesterol increased significantly with serum selenium concentration (<it>P </it>< 0.001, <it>P </it>< 0.05 and <it>P </it>< 0.001, respectively) after adjusting for age, gender, anthropometric indices, lifestyle factors, and cardio-vascular risk factors in several linear regression models. Furthermore, there was a significantly positive association between serum selenium and serum fasting glucose concentrations (<it>P </it>< 0.05).</p> <p>Conclusions</p> <p>Total cholesterol, triglycerides, and LDL cholesterol, and fasting serum glucose concentrations increased significantly with serum selenium concentration in the Taiwanese elderly. The underlying mechanism warrants further research.</p

MonoNeuralFusion: Online Monocular Neural 3D Reconstruction with Geometric Priors

High-fidelity 3D scene reconstruction from monocular videos continues to be challenging, especially for complete and fine-grained geometry reconstruction. The previous 3D reconstruction approaches with neural implicit representations have shown a promising ability for complete scene reconstruction, while their results are often over-smooth and lack enough geometric details. This paper introduces a novel neural implicit scene representation with volume rendering for high-fidelity online 3D scene reconstruction from monocular videos. For fine-grained reconstruction, our key insight is to incorporate geometric priors into both the neural implicit scene representation and neural volume rendering, thus leading to an effective geometry learning mechanism based on volume rendering optimization. Benefiting from this, we present MonoNeuralFusion to perform the online neural 3D reconstruction from monocular videos, by which the 3D scene geometry is efficiently generated and optimized during the on-the-fly 3D monocular scanning. The extensive comparisons with state-of-the-art approaches show that our MonoNeuralFusion consistently generates much better complete and fine-grained reconstruction results, both quantitatively and qualitatively.Comment: 12 pages, 12 figure