Using Gamification to Support Users’ Adoption of Contextual Achievement Goals

Gamification is a promising approach for motivating and engaging users in nongame tasks. However, theoretical support on why and how gamification enhances users’ motivation or behavior is limited. Considering the concepts of goal orientation and goal structure suggested by achievement goal theory, we prescribe gamification design as purposely creating goal structures to support users’ goal adoption and achievement behaviors. This conceptual work addresses the question: what types of achievement goals can be associated with gamification design? Particularly, how can the use of gamification design help construct goal structures to support users’ goal adoption? Adapting achievement goal theory, we identify three sets of achievement goals, namely, cognitive competence, social competence, and social purpose, and develop six propositions on gamification design. Each proposition is illustrated with empirical examples from the literature. This research contributes to the theoretical advancement of gamification design and provides additional insights into the motivational design of information systems

UV R-CNN: Stable and Efficient Dense Human Pose Estimation

Dense pose estimation is a dense 3D prediction task for instance-level human analysis, aiming to map human pixels from an RGB image to a 3D surface of the human body. Due to a large amount of surface point regression, the training process appears to be easy to collapse compared to other region-based human instance analyzing tasks. By analyzing the loss formulation of the existing dense pose estimation model, we introduce a novel point regression loss function, named Dense Points} loss to stable the training progress, and a new balanced loss weighting strategy to handle the multi-task losses. With the above novelties, we propose a brand new architecture, named UV R-CNN. Without auxiliary supervision and external knowledge from other tasks, UV R-CNN can handle many complicated issues in dense pose model training progress, achieving 65.0% $AP_{gps}$ and 66.1% $AP_{gpsm}$ on the DensePose-COCO validation subset with ResNet-50-FPN feature extractor, competitive among the state-of-the-art dense human pose estimation methods.Comment: 9pages, 4 figure

HI-TOM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models

Theory of Mind (ToM) is the ability to reason about one's own and others' mental states. ToM plays a critical role in the development of intelligence, language understanding, and cognitive processes. While previous work has primarily focused on first and second-order ToM, we explore higher-order ToM, which involves recursive reasoning on others' beliefs. We introduce HI-TOM, a Higher Order Theory of Mind benchmark. Our experimental evaluation using various Large Language Models (LLMs) indicates a decline in performance on higher-order ToM tasks, demonstrating the limitations of current LLMs. We conduct a thorough analysis of different failure cases of LLMs, and share our thoughts on the implications of our findings on the future of NLP.Comment: Accepted at Findings of EMNLP 202

Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond

We propose task-adaptive tokenization as a way to adapt the generation pipeline to the specifics of a downstream task and enhance long-form generation in mental health. Inspired by insights from cognitive science, our task-adaptive tokenizer samples variable segmentations from multiple outcomes, with sampling probabilities optimized based on task-specific data. We introduce a strategy for building a specialized vocabulary and introduce a vocabulary merging protocol that allows for the integration of task-specific tokens into the pre-trained model's tokenization step. Through extensive experiments on psychological question-answering tasks in both Chinese and English, we find that our task-adaptive tokenization approach brings a significant improvement in generation performance while using up to 60% fewer tokens. Preliminary experiments point to promising results when using our tokenization approach with very large language models.Comment: Accepted at the main conference of The 2023 Conference on Empirical Methods in Natural Language Processing; 8 page

Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action Recognition from Egocentric RGB Videos

Understanding dynamic hand motions and actions from egocentric RGB videos is a fundamental yet challenging task due to self-occlusion and ambiguity. To address occlusion and ambiguity, we develop a transformer-based framework to exploit temporal information for robust estimation. Noticing the different temporal granularity of and the semantic correlation between hand pose estimation and action recognition, we build a network hierarchy with two cascaded transformer encoders, where the first one exploits the short-term temporal cue for hand pose estimation, and the latter aggregates per-frame pose and object information over a longer time span to recognize the action. Our approach achieves competitive results on two first-person hand action benchmarks, namely FPHA and H2O. Extensive ablation studies verify our design choices. We will open-source code and data to facilitate future research

Single-shot compressed ultrafast photography: a review

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields

Quantitative Comparison of Cephalogram and Cone-Beam Computed Tomography in the Evaluation of Alveolar Bone Thickness of Maxillary Incisors

Objective:This study aims to quantitatively compare cephalogram and cone-beam computed tomography (CBCT) when evaluating maxillary central incisor alveolar bone thickness.Methods:We used 30 sets of lateral cephalograms and CBCT images that were recorded at the same time. Labial, buccal, and overall alveolar bone thicknesses were measured on three measurement lines of the forward-most incisor in lateral cephalograms and four maxillary incisors in CBCT images. Paired t-test, interclass correlation coefficient analysis, one-way analysis of variance (ANOVA), and Bland–Altman analysis were used to assess cephalometrically measured alveolar bone thickness of maxillary incisors and compare these measurements with those made using CBCT images.Results:Significant differences were observed between cephalometric and CBCT-based measurements of maxillary incisor alveolar bone thickness; most values showed mild or moderate correlation between the two methods. In most cases, cephalometric measurements were greater than CBCT-based measurements. Bland–Altman plots and ANOVA revealed that measurement bias increased when measurement lines moved apically. Alveolar bone thickness was always overestimated on cephalograms.Conclusion:Maxillary incisor alveolar bone thickness is always overestimated on cephalograms compared with CBCT-based measurements, with the overestimations ranging from 0.3 to 1.3 mm. Cephalometric measurement bias increases when measurement lines move apically. Thus, CBCT should be recommended when the accurate evaluation of alveolar bone thickness is warranted

Construction of Trisubstituted Chromone Skeletons Carrying Electron-Withdrawing Groups Via PhIO-Mediated Dehydrogenation and Its Application to the Synthesis of Frutinone A

Abstract The construction of the biologically interesting chromone skeleton was realized by PhIO-mediated dehydrogenation of chromanones under mild conditions. Interestingly, this method also found application in the synthesis of the naturally occurring frutinone A

Single-shot compressed ultrafast photography: a review

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields

Metabolomic and transcriptomice analyses of flavonoid biosynthesis in apricot fruits

IntroductionFlavonoids, as secondary metabolites in plants, play important roles in many biological processes and responses to environmental factors.MethodsApricot fruits are rich in flavonoid compounds, and in this study, we performed a combined metabolomic and transcriptomic analysis of orange flesh (JN) and white flesh (ZS) apricot fruits.Results and discussionA total of 222 differentially accumulated flavonoids (DAFs) and 15855 differentially expressed genes (DEGs) involved in flavonoid biosynthesis were identified. The biosynthesis of flavonoids in apricot fruit may be regulated by 17 enzyme-encoding genes, namely PAL (2), 4CL (9), C4H (1), HCT (15), C3’H (4), CHS (2), CHI (3), F3H (1), F3’H (CYP75B1) (2), F3’5’H (4), DFR (4), LAR (1), FLS (3), ANS (9), ANR (2), UGT79B1 (6) and CYP81E (2). A structural gene-transcription factor (TF) correlation analysis yielded 3 TFs (2 bHLH, 1 MYB) highly correlated with 2 structural genes. In addition, we obtained 26 candidate genes involved in the biosynthesis of 8 differentially accumulated flavonoids metabolites in ZS by weighted gene coexpression network analysis. The candidate genes and transcription factors identified in this study will provide a highly valuable molecular basis for the in-depth study of flavonoid biosynthesis in apricot fruits