Studentsa Ratings of School Climate and School Belonging for Understanding Their Effects and Relationship of Junior High Schools in Taiwan

This study was to explore junior high students perception of school climate and school belonging The main purposes of the present study were to test the relationships between school climate and school belonging How s the correlation between the two variables Which one has better effects on the other Three hundred and twenty-eight junior high students in Taiwan were selected to inclusion in the investigation A statistically significant relationship between school climate and school belonging was found From the construction analyses we found that the path coefficients of Model 1 and Model 2 are the same The outcomes meant that school climate had significant effects on school belonging and school belonging also had significant effects on school climate The results of the study can offer the relative schools for evaluating school effects and school improvemen

Age Differences in the Control of Posture and Movement During Standing Reach.

The performance of standing reach requires the maintenance of postural stability and the coordination of multiple joints. Although aging is associated with declines in postural stability, the impact of workspace target heights, reaching with the dominant versus non-dominant arm, and movement context on limb-posture control is not well understood in older adults. The first study of this dissertation examined anticipatory and dynamic postural control during standing reach to different heights with the dominant and non-dominant arm. Compared to younger individuals, older adults produced larger anticipatory postural adjustments (APAs), and center of pressure (COP) trajectories were less smooth, particularly when returning to an upright posture (Chapter 2). These results suggested that older adults used an active “over-control” strategy to increase the safety margin for balance, rather than relying on later, potentially inadequate compensatory postural responses. Older adults exhibited significant increases in APA amplitude and COP trajectory smoothness when reaching with the dominant compared to the non-dominant hand, perhaps reflecting handedness. In contrast, no differences between age groups were found when examining hand trajectory curvature, indicating that planning of multi-joint, standing reach movements was not affected by age (Chapter 3). Hand trajectories were more curved during reaching to low compared to higher targets regardless of age, suggesting that the biomechanical demands associated with controlling the trunk affects hand trajectory formation. The second study examined whether the movement context (pointing versus grasping) would affect postural control (Chapter 4). In older adults only, grasping was associated with a decrease in COP trajectory linearity, suggesting that aging affects the ability to anticipate and counteract the internal perturbations generated by grasping an object. From a rehabilitative perspective, the results of these studies indicate that standing balance training in older adults should incorporate different workspace locations, functional goals, as well as tasks involving reaching with both the dominant and non-dominant hands.Ph.D.KinesiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64709/1/mhhuang_1.pd

Modified basket plate for inferior patellar pole avulsion fractures—A report of three cases

AbstractIn patients who have sustained an avulsion fracture of the inferior patellar pole, the extensor mechanism is disrupted and should be repaired. The normal height of the patella can be maintained by preserving the patellar pole, but fractures of the inferior pole of the patella are not easy to reduce and fix firmly. In contrast with partial patellectomy, which requires postoperative immobilization, internal fixation with a basket plate allows for immediate mobilization and early weight-bearing. Owing to the unavailability of the basket plate in Taiwan, we have modified the plate with the titanium mesh as a possible alternative. We present three cases of this modified basket plate, which took place between 2008 and 2010. This technique avoided long-term immobilization of the knee with good clinical results

Multi-Trace Superpotentials vs. Matrix Models

We consider N = 1 supersymmetric U(N) field theories in four dimensions with adjoint chiral matter and a multi-trace tree-level superpotential. We show that the computation of the effective action as a function of the glueball superfield localizes to computing matrix integrals. Unlike the single-trace case, holomorphy and symmetries do not forbid non-planar contributions. Nevertheless, only a special subset of the planar diagrams contributes to the exact result. Some of the data of this subset can be computed from the large-N limit of an associated multi-trace Matrix model. However, the prescription differs in important respects from that of Dijkgraaf and Vafa for single-trace superpotentials in that the field theory effective action is not the derivative of a multi-trace matrix model free energy. The basic subtlety involves the correct identification of the field theory glueball as a variable in the Matrix model, as we show via an auxiliary construction involving a single-trace matrix model with additional singlet fields which are integrated out to compute the multi-trace results. Along the way we also describe a general technique for computing the large-N limits of multi-trace Matrix models and raise the challenge of finding the field theories whose effective actions they may compute. Since our models can be treated as N = 1 deformations of pure N =2 gauge theory, we show that the effective superpotential that we compute also follows from the N = 2 Seiberg-Witten solution. Finally, we observe an interesting connection between multi-trace local theories and non-local field theory.Comment: 35 pages, LaTeX, 6 EPS figures. v2: typos fixed, v3: typos fixed, references added, Sec. 5 added explaining how multi-trace theories can be linearized in traces by addition of singlet fields and the relation of this approach to matrix model

GraphiMind: LLM-centric Interface for Information Graphics Design

Information graphics are pivotal in effective information dissemination and storytelling. However, creating such graphics is extremely challenging for non-professionals, since the design process requires multifaceted skills and comprehensive knowledge. Thus, despite the many available authoring tools, a significant gap remains in enabling non-experts to produce compelling information graphics seamlessly, especially from scratch. Recent breakthroughs show that Large Language Models (LLMs), especially when tool-augmented, can autonomously engage with external tools, making them promising candidates for enabling innovative graphic design applications. In this work, we propose a LLM-centric interface with the agent GraphiMind for automatic generation, recommendation, and composition of information graphics design resources, based on user intent expressed through natural language. Our GraphiMind integrates a Textual Conversational Interface, powered by tool-augmented LLM, with a traditional Graphical Manipulation Interface, streamlining the entire design process from raw resource curation to composition and refinement. Extensive evaluations highlight our tool's proficiency in simplifying the design process, opening avenues for its use by non-professional users. Moreover, we spotlight the potential of LLMs in reshaping the domain of information graphics design, offering a blend of automation, versatility, and user-centric interactivity

Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

Table-based reasoning has shown remarkable progress in combining deep models with discrete reasoning, which requires reasoning over both free-form natural language (NL) questions and structured tabular data. However, previous table-based reasoning solutions usually suffer from significant performance degradation on huge evidence (tables). In addition, most existing methods struggle to reason over complex questions since the required information is scattered in different places. To alleviate the above challenges, we exploit large language models (LLMs) as decomposers for effective table-based reasoning, which (i) decompose huge evidence (a huge table) into sub-evidence (a small table) to mitigate the interference of useless information for table reasoning; and (ii) decompose complex questions into simpler sub-questions for text reasoning. Specifically, we first use the LLMs to break down the evidence (tables) involved in the current question, retaining the relevant evidence and excluding the remaining irrelevant evidence from the huge table. In addition, we propose a "parsing-execution-filling" strategy to alleviate the hallucination dilemma of the chain of thought by decoupling logic and numerical computation in each step. Extensive experiments show that our method can effectively leverage decomposed evidence and questions and outperforms the strong baselines on TabFact, WikiTableQuestion, and FetaQA datasets. Notably, our model outperforms human performance for the first time on the TabFact dataset.Comment: SIGIR 202

PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts

Perceiving multi-modal information and fulfilling dialogues with humans is a long-term goal of artificial intelligence. Pre-training is commonly regarded as an effective approach for multi-modal dialogue. However, due to the limited availability of multi-modal dialogue data, there is still scarce research on multi-modal dialogue pre-training. Yet another intriguing challenge emerges from the encompassing nature of multi-modal dialogue, which involves various modalities and tasks. Moreover, new forms of tasks may arise at unpredictable points in the future. Hence, it is essential for designed multi-modal dialogue models to possess sufficient flexibility to adapt to such scenarios. This paper proposes \textbf{PaCE}, a unified, structured, compositional multi-modal dialogue pre-training framework. It utilizes a combination of several fundamental experts to accommodate multiple dialogue-related tasks and can be pre-trained using limited dialogue and extensive non-dialogue multi-modal data. Furthermore, we propose a progressive training method where old experts from the past can assist new experts, facilitating the expansion of their capabilities. Experimental results demonstrate that PaCE achieves state-of-the-art results on eight multi-modal dialog benchmarks.Comment: ACL 202