Institutional Sources of Resilience in Global ICT Leaders - Harness the Vigor of Emerging Power

In light of the significant impacts on global economy both nations and firms witnessed a dramatic advancement of information and communication technology (ICT). There was particularly bi-polarization between ICT advanced and growing economies compelling a vicious cycle between ICT advancement and its productivity decline in these economies. The institutional sources of resilience were analyzed. On the basis of an empirical analysis comparing technopreneurial performance in world top 500 ICT firms by market value, sales and profit over the last decade, resilient firms maintaining world top 100 position by all three values over the whole period were identified. Institutional sources enabling resilient firms maintain leading position can largely be attributed to co-evolutionary acclimatization ability, which harnesses the vigor of emerging power of counterparts both in home countries and in advanced countries as well as growing economies in a co-evolutional way. Such ability maximizes synergy between efficiency and resilience in their technopreneurial management. Contrasting business model in global ICT firms with and without resilience structure suggests the sources of emerging trap due to ICT advancement and endorsed the significance of co-evolutionary acclimatization. This suggests the significance of institutional co-evolution between ICT advanced and growing economies that enables both economies to harness the vigor of partners for global sustainability

Resonance between Innovation and Consumers: Suggestions for Emerging Market Customers

Consumption increase in emerging markets is significant for global sustainability as it helps in overcoming structural impediments that impede investment inducement. In this light, the paper aims at demonstrating a hypothesis that resonance between innovation and consumers triggers co-emergence of investment essential for an emerging market and further analyses resonant behavior between attractive goods and consumers. The elevation in face temperature of consumers looking at attractive goods was measured at the event corner of a Japanese supermarket by utilizing thermography. Noteworthy findings obtained include that consumer temperatures increase as they perceive, recognize and decide to purchase attractive goods while elevated temperatures decrease when the goods are not attractive enough to purchase. Consumer couples also incorporate a general tendency to converge toward the same decision in a resonant way. Through correlation analysis of sales records, it was demonstrated that sales of attractive goods represents innovation which increases by resonating consumer demand through construction of a spirally developing virtuous cycle. These findings provide a constructive suggestion for stimulating latent consumer vitality in emerging markets as a way of inducing investment

Co-emergence of Institutional Innovation Navigates the New Normal in Growing Economies

Increasing fear of the global simultaneous stagnation derived from the Euro-crisis together with the New Normal in growing economies reveals the limit of individual strength leading to the significance of fusion with global best practices. Dramatic advancement of the Internet has enabled consumers in any nation to choose and learn from world’s strongest suppliers. Both trends inevitably necessitate co-emergence of institutional innovation between suppliers and consumers for sustainability. On the basis of an empirical analysis comparing institutional systems in 100 nations, this paper demonstrates the significance of this co-emergence thereby navigating the New Normal in growing economies

Structural Source of the Trap of ICT Advancement - Lessons from World ICT Top Leaders

In light of the significant consequence of the trap of dramatic advancement of information and communication technology (ICT) in the global economy, both nations and firms that have been compelling their productivity decline. This resulted in great stagnation of ICT advanced economies and therefore its structural sources were analyzed. Based on an empirical analysis tracing, the trend in marginal productivity of ICT and its subsequent prices among the top ICT leaders in the world over the last two decades correlating with the effects of ICT, two faces of ICT advancement were identified. On one side, advancement of ICT contributes to its prices increase by new functionality development, its dramatic advancement particularly centered by internet results in the decline of its prices through freebies, easy copying, and standardization. It was demonstrated that the success of ICT leaders could largely be attributed to the way in which the two faces of ICT advancement were managed by maximizing the positive face of ICT advancement. This is done by means of the effective utilization of external resources in innovation while minimizing the negative face by outsourcing price decreasing factors. All of the aforementioned points can be invaluable lessons for global sustainability in both ICT advanced and growing economies in the midst of the advancement of ICT. The significance of innovation-consumption co-emergence for harnessing the vigor of counterparts is discussed

Dual Hybrid Management of Technology: Co-evolution with Growing Economies

Given the increasing significance of the co-evolution between advanced and growing economies for problem-solving innovation that aims at solving global critical issues, this paper attempts an empirical analysis to identify the optimal co-evolutionary trajectory, which could benefit both advanced and growing economies. While Japan has succeeded to develop the hybrid management of technology fusing indigenous strength and learning ability, it has revealed some limitations during the global simultaneous economic stagnation. The analysis suggests that the dual hybrid management of technology coevolving also with growing economies is decisive to the problem-solving innovation of the nation. This benefits nations in growing economies as well. This paper provides new insights into the problem-solving innovation, and also inducing strategy of growing economies for global sustainability

Hypopharyngeal Cancer: Staging, Diagnosis, and Therapy

Hypopharyngeal carcinoma is uncommon in all head and neck cancers. With a synergistic reaction of each, tobacco consumption and alcohol abuse contribute to the tumorigenesis. The aerodigestive tract epithelium exposure to similar risks causing multiple cancers. Thus, a pan-endoscopic screening offers a practical approach for evaluating second primary esophageal cancer. The common symptoms of hypopharyngeal carcinoma were globus pharyngeus, sore throat, dysphagia, otalgia, neck mass, hoarseness, and dyspnoea. However, approximately 75–80% of patients are initial diagnosed with advanced-stage. Although improvements in therapy, the prognosis is still lacking. In early-stage patients, primary surgical resection and radiotherapy achieved similar survival and locoregional control rates. T1–T2 malignancies with N0–N1 can usually be treated with radiation alone, open surgery, or transoral surgery. In some people, after primary surgery or transoral approaches is often required adjuvant radiotherapy. However, most cases have been in the advanced-stage when screened. Individual therapy programs should be chosen carefully to achieve a balance between swallowing-voice rehabilitation and organ preservation in advanced-stage ones. Meanwhile, reasonable reconstruction of intraoperative defect is essential for a surgeon who seeks satisfied postoperative outcomes. Considerable treatment (surgery or non-surgery) remains the key point of improving the survival rate

CPET: Effective Parameter-Efficient Tuning for Compressed Large Language Models

Parameter-efficient tuning (PET) has been widely explored in recent years because it tunes much fewer parameters (PET modules) than full-parameter fine-tuning (FT) while still stimulating sufficient knowledge from large language models (LLMs) for downstream tasks. Moreover, when PET is employed to serve multiple tasks, different task-specific PET modules can be built on a frozen LLM, avoiding redundant LLM deployments. Although PET significantly reduces the cost of tuning and deploying LLMs, its inference still suffers from the computational bottleneck of LLMs. To address the above issue, we propose an effective PET framework based on compressed LLMs, named "CPET". In CPET, we evaluate the impact of mainstream LLM compression techniques on PET performance and then introduce knowledge inheritance and recovery strategies to restore the knowledge loss caused by these compression techniques. Our experimental results demonstrate that, owing to the restoring strategies of CPET, collaborating task-specific PET modules with a compressed LLM can achieve comparable performance to collaborating PET modules with the original version of the compressed LLM and outperform directly applying vanilla PET methods to the compressed LLM

Estimation of PM2.5 concentrations in China using a spatial back propagation neural network

Methods for estimating the spatial distribution of PM2.5 concentrations have been developed but have not yet been able to effectively include spatial correlation. We report on the development of a spatial back-propagation neural network (S-BPNN) model designed specifically to make such correlations implicit by incorporating a spatial lag variable (SLV) as a virtual input variable. The S-BPNN fits the nonlinear relationship between ground-based air quality monitoring station measurements of PM2.5, satellite observations of aerosol optical depth, meteorological synoptic conditions data and emissions data that include auxiliary geographical parameters such as land use, normalized difference vegetation index, elevation, and population density. We trained and validated the S-BPNN for both yearly and seasonal mean PM2.5 concentrations. In addition, principal components analysis was employed to reduce the dimensionality of the data and a grid of neural network models was run to optimize the model design. The S-BPNN was cross-validated against an analogous but SLV-free BPNN model using the coefficient of determination (R2) and root mean squared error (RMSE) as statistical measures of goodness of fit. The inclusion of the SLV led to demonstrably superior performance of the S-BPNN over the BPNN with R2 values increasing from 0.80 to 0.89 and with the RMSE decreasing from 8.1 to 5.8 μg/m3. The yearly mean PM2.5 concentration in China during the study period was found to be 41.8 μg/m3 and the model estimated spatial distribution was found to exceed Level 2 of the China Ambient Air Quality Standards (CAAQS) enacted in 2012 (>35 μg/m3) in more than 70% of the Chinese territory. The inclusion of spatial correlation upgrades the performance of conventional BPNN models and provides a more accurate estimation of PM2.5 concentrations for air quality monitoring

OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models

The scale of large pre-trained models (PTMs) poses significant challenges in adapting to downstream tasks due to the high optimization overhead and storage costs associated with full-parameter fine-tuning. To address this, many studies explore parameter-efficient tuning methods, also framed as "delta tuning", which updates only a small subset of parameters, known as "delta modules", while keeping the backbone model's parameters fixed. However, the practicality and flexibility of delta tuning have been limited due to existing implementations that directly modify the code of the backbone PTMs and hard-code specific delta tuning methods for each PTM. In this paper, we present OpenDelta, an open-source library that overcomes these limitations by providing a plug-and-play implementation of various delta tuning methods. Our novel techniques eliminate the need to modify the backbone PTMs' code, making OpenDelta compatible with different, even novel PTMs. OpenDelta is designed to be simple, modular, and extensible, providing a comprehensive platform for researchers and practitioners to adapt large PTMs efficiently.Comment: Accepted to ACL 2023 Demo trac