Planning and Team Shared Mental Models as Predictors of Team Collaborative Processes

This study evaluates the role of team planning and the similarity of team shared mental models (TSMMs) as predictors of two types of collaborative behaviors that are known to contribute to team performance. A computer-based Networked Fire Chief (NFC) simulation task was used as a testing environment for emergent and dynamic situations. The relationships among team planning, similarity of task-focused team shared mental models (TASKTSMMs), similarity of team-focused team shared mental models (TEAMTSMMs), team backup behaviors, and implicit coordination were tested. This study provides evidence for the mediation effect of similarity of TASKTSMMs between team planning and team backup behaviors, and the mediation effect of team backup behaviors between similarity of TASKTSMMs and team performance. The results suggest that better team planning is more likely to encourage more backup behaviors and improved performance through teams having more similar task-focused mental models. Both the theoretical and practical implications were discussed and the limitations and future research were also addressed in the study

Syntax-aware Hybrid prompt model for Few-shot multi-modal sentiment analysis

Multimodal Sentiment Analysis (MSA) has been a popular topic in natural language processing nowadays, at both sentence and aspect level. However, the existing approaches almost require large-size labeled datasets, which bring about large consumption of time and resources. Therefore, it is practical to explore the method for few-shot sentiment analysis in cross-modalities. Previous works generally execute on textual modality, using the prompt-based methods, mainly two types: hand-crafted prompts and learnable prompts. The existing approach in few-shot multi-modality sentiment analysis task has utilized both methods, separately. We further design a hybrid pattern that can combine one or more fixed hand-crafted prompts and learnable prompts and utilize the attention mechanisms to optimize the prompt encoder. The experiments on both sentence-level and aspect-level datasets prove that we get a significant outperformance

Meta-Analysis of Life Cycle Assessment Studies on Solar Photovoltaic Systems

Nowadays, greenhouse gas emission problem is becoming more and more severe. At the same time, world energy demand increases a lot every year. All the countries focus on using renewable energy and take it as the solution of future energy demand problem. Although the solar energy only makes up 1% market share of the total renewable energy, it grows rapidly recent years. Because the energy coming from sun is tens of time more than energy coming from the fossil fuel. There are three main type of the photovoltaic technologies, which are crystalline silicon solar cell, thin-film solar cell and polymer solar cell. Crystalline silicon solar cell is the first generation technology, which make up 90% market share of the solar energy industry. Thin-film solar cell is the second generation technology, which makes up 10% market share of the solar energy. The goal of this thesis are 1) to evaluate the efficiency of each technologies of solar energy; 2) to compare the cumulative energy demand (CED) of solar module of each technology; 3) to compare the energy return on investment (EROI) of each technologies; 4) to know energy demand of balance of system of all technologies; 5) to show the trend of different generation of solar energy through time by showing relation between efficiency, cumulative energy demand and energy return on investment. To accomplish these goals, we use a meta-analysis method in thesis. We collect all the studies on solar energy which has passed the criteria we set. After getting all the data, we evaluate the CED and EROI by using our own method to harmonized each data

Towards Deterministic Reconfigurable Networks

Compared with legacy networks, programmable networks are highlyflexible and need to be reconfigured dynamically. In this early work paper, we studythe fast and consistent network update which is the key enabler to realize deterministicreconfigurable networks. The reconfiguration speed is one side of the coin. Theongoing best-effort traffic cannot be interrupted during the network reconfigurationas well. In terms of reconfiguration speed, we implement and compare our methodwith the state-of-the-art decentralized and centralized update methods

Boosting Adversarial Attack with Similar Target

Deep neural networks are vulnerable to adversarial examples, posing a threat to the models' applications and raising security concerns. An intriguing property of adversarial examples is their strong transferability. Several methods have been proposed to enhance transferability, including ensemble attacks which have demonstrated their efficacy. However, prior approaches simply average logits, probabilities, or losses for model ensembling, lacking a comprehensive analysis of how and why model ensembling significantly improves transferability. In this paper, we propose a similar targeted attack method named Similar Target~(ST). By promoting cosine similarity between the gradients of each model, our method regularizes the optimization direction to simultaneously attack all surrogate models. This strategy has been proven to enhance generalization ability. Experimental results on ImageNet validate the effectiveness of our approach in improving adversarial transferability. Our method outperforms state-of-the-art attackers on 18 discriminative classifiers and adversarially trained models

Developing Critical Collaboration Skills in Engineering Students: Results From an Empirical Study

In highly technical organizations, work is becoming increasingly distributed; requiring practicing engineers to master virtual collaboration skills while acquiring expertise in a range of collaboration technologies. Although there has been great emphasis on developing collaboration competencies in the engineering curriculum, empirical evidence of successful strategies for distributed team settings is scarce. As an attempt to fill this gap this study investigates the impact of a scalable intervention in developing virtual collaboration skills. The intervention, based on instructional scaffolds embedded with collaboration technologies, is aimed at supporting specific processes including planning, goal setting, clarifying goals and expectations, communication, coordination and progress monitoring. A quasi-experimental design was used to evaluate the impact of the intervention on student teamwork skills. Data from 278 graduate and undergraduate engineering students participating in virtual team projects was used in the analysis. Results from the analysis are presented suggesting a statistically significant impact of the intervention on self-management skills when comparing randomly assigned teams with and without the intervention. The intervention is designed to be scalable so that it can be embedded into existing project-based courses. Our findings have important implications for the development of teamwork skills in engineering courses and provide evidence of a successful strategy that can be integrated into the existing engineering curriculum

Hippocampal Long-Term Depression in the Presence of Calcium-Permeable AMPA Receptors

The GluA2 subunit of AMPA glutamate receptors (AMPARs) has been shown to be critical for the expression of NMDA receptor (NMDAR)-dependent long-term depression (LTD). However, in young GluA2 knockout (KO) mice, this form of LTD can still be induced in the hippocampus, suggesting that LTD mechanisms may be modified in the presence of GluA2-lacking, Ca2+ permeable AMPARs. In this study, we examined LTD at the CA1 synapse in GluA2 KO mice by using several well-established inhibitory peptides known to block LTD in wild type (WT) rodents. We showed that while LTD in the KO mice is still blocked by the protein interacting with C kinase 1 (PICK1) peptide pepEVKI, it becomes insensitive to the N-ethylmaleimide-sensitive factor (NSF) peptide pep2m. In addition, the effects of actin and cofilin inhibitory peptides were also altered. These results indicate that in the absence of GluA2, LTD expression mechanisms are different from those in WT animals, suggesting that there are multiple molecular processes enabling LTD expression that are adaptable to physiological and genetic manipulations

DPPMask: Masked Image Modeling with Determinantal Point Processes

Masked Image Modeling (MIM) has achieved impressive representative performance with the aim of reconstructing randomly masked images. Despite the empirical success, most previous works have neglected the important fact that it is unreasonable to force the model to reconstruct something beyond recovery, such as those masked objects. In this work, we show that uniformly random masking widely used in previous works unavoidably loses some key objects and changes original semantic information, resulting in a misalignment problem and hurting the representative learning eventually. To address this issue, we augment MIM with a new masking strategy namely the DPPMask by substituting the random process with Determinantal Point Process (DPPs) to reduce the semantic change of the image after masking. Our method is simple yet effective and requires no extra learnable parameters when implemented within various frameworks. In particular, we evaluate our method on two representative MIM frameworks, MAE and iBOT. We show that DPPMask surpassed random sampling under both lower and higher masking ratios, indicating that DPPMask makes the reconstruction task more reasonable. We further test our method on the background challenge and multi-class classification tasks, showing that our method is more robust at various tasks

Value of brain tissue oxygen saturation in neonatal respiratory distress syndrome: a clinical study

Neonatal respiratory distress syndrome (NRDS) is one of the major causes of pre-term mortality and morbidity among very-low-birth-weight infants (VLBWI) in low- and middle-income countries (LMIC). Some of the neonates pass away despite admission and care in intensive care units (ICUs). The present clinical trial seeks the application value of elevating oxygen saturation in the brain cells of pre-term neonates born with NRDS. Near-infrared spectroscopy (NIRS) was used to monitor the neonates’ microscopic cerebral oxygenation levels do determine hemoglobin concentration in brain tissues, whereas the pulse oximetry was used to measure oxygenation levels among the patients. In statistical analyses, the Analysis of Variance (ANOVA), and descriptive statistics was deployed in the Jupyter Notebook environment using Python language. High saturation of oxygen in the brain tissues result in important biological and physiological processes, including enhanced oxygen supply to cells, reduced severity of NRDS, and balancing oxygen demand and supply. The correlations of oxygen saturation with systemic saturation of oxygen, the saturation of oxygen in brain tissues, the association between brain-specific and systemic saturation, and the impact of these outcomes on clinical practices were deliberated. Also, the pH gas values, the saturation of oxygen in neonates’ brain tissues, metabolic acidosis, the effect of acid-base balance and cerebral oxygen supply, and the oxygenation of brain tissues and the pH values emerged as important variables of oxygenation of brain tissues in pre-term neonates. Oxygen saturation in brain cells influence vital physiological and biological processes. Balancing acid-base saturation or levels is needed despite the challenging achievement. Oxygenation of brain tissues improve the brain’s overall functioning

Fine-grained Appearance Transfer with Diffusion Models

Image-to-image translation (I2I), and particularly its subfield of appearance transfer, which seeks to alter the visual appearance between images while maintaining structural coherence, presents formidable challenges. Despite significant advancements brought by diffusion models, achieving fine-grained transfer remains complex, particularly in terms of retaining detailed structural elements and ensuring information fidelity. This paper proposes an innovative framework designed to surmount these challenges by integrating various aspects of semantic matching, appearance transfer, and latent deviation. A pivotal aspect of our approach is the strategic use of the predicted $x_0$ space by diffusion models within the latent space of diffusion processes. This is identified as a crucial element for the precise and natural transfer of fine-grained details. Our framework exploits this space to accomplish semantic alignment between source and target images, facilitating mask-wise appearance transfer for improved feature acquisition. A significant advancement of our method is the seamless integration of these features into the latent space, enabling more nuanced latent deviations without necessitating extensive model retraining or fine-tuning. The effectiveness of our approach is demonstrated through extensive experiments, which showcase its ability to adeptly handle fine-grained appearance transfers across a wide range of categories and domains. We provide our code at https://github.com/babahui/Fine-grained-Appearance-TransferComment: 14 pages, 15 figure