Tootfinder

On the Improvement of Predictive Modeling Using Bayesian Stacking and Posterior Predictive Checking
Mariana Nold, Florian Meinfelder, David Kaplan
https://arxiv.org/abs/2402.19046

On the Improvement of Predictive Modeling Using Bayesian Stacking and Posterior Predictive Checking
Model uncertainty is pervasive in real world analysis situations and is an often-neglected issue in applied statistics. However, standard approaches to the research process do not address the inherent uncertainty in model building and, thus, can lead to overconfident and misleading analysis interpretations. One strategy to incorporate more flexible models is to base inferences on predictive modeling. This approach provides an alternative to existing explanatory models, as inference is focused o…

If the world economy is back on track, it’s apparently a slower track.
Last month the World Bank released its updated report on Global Economic Prospects.
It drew a gloomy picture of slowing growth, marking what it calls a “wretched milestone” – a world economy that is expected to grow at its slowest rate in three decades:
2.4 per cent this year, with perhaps a slight improvement next year.
As to all that money sitting on the sidelines, it’s still sitting there.

A harsh truth: The world economy never recovered from the COVID-19 pandemic
It appears the pandemic left some deep wounds in the economy, something few economists saw coming

Daily Inspiration: "World class innovators focus on long term wins through constant incremental improvements" - Futurist Jim Carroll
Want something boring? This is it! It's the BIG impact of the new SMALL incrementalism!
Huh what?
It involves thinking like this: “How can we continue to evolve our solutions in small ways that provide regular, continuous improvement for our customers?” And a realization that success can come from pursuing a mindset that success …

Daily Inspiration: Mastering Innovation – “World class innovators focus on long term wins through constant incremental improvements”

A good management system doesn’t rely on heroic efforts to save the day. The organization is designed to succeed. It is robust. It will succeed with all the variation thrown at it by the outside world. A good management system takes advantage of the contributions people offer, but it will not perform poorly when others are relied on.
A well run organization has graceful degradation (when one component fails or one person is missing...

Agile Requirement Change Management Model for Global Software Development
Neha Koulecar, Bachan Ghimire
https://arxiv.org/abs/2402.14595 https://

Agile Requirement Change Management Model for Global Software Development
We propose a noble, comprehensive and robust agile requirements change management (ARCM) model that addresses the limitations of existing models and is tailored for agile software development in the global software development paradigm. To achieve this goal, we conducted an exhaustive literature review and an empirical study with RCM industry experts. Our study evaluated the effectiveness of the proposed RCM model in a real-world setting and identifies any limitations or areas for improvement. …

Brain-inspired Distributed Memorization Learning for Efficient Feature-free Unsupervised Domain Adaptation
Jianming Lv, Depin Liang, Zequan Liang, Yaobin Zhang, Sijun Xia
https://arxiv.org/abs/2402.14598 https://arxiv.org/pdf/2402.14598
arXiv:2402.14598v1 Announce Type: new
Abstract: Compared with gradient based artificial neural networks, biological neural networks usually show a more powerful generalization ability to quickly adapt to unknown environments without using any gradient back-propagation procedure. Inspired by the distributed memory mechanism of human brains, we propose a novel gradient-free Distributed Memorization Learning mechanism, namely DML, to support quick domain adaptation of transferred models. In particular, DML adopts randomly connected neurons to memorize the association of input signals, which are propagated as impulses, and makes the final decision by associating the distributed memories based on their confidence. More importantly, DML is able to perform reinforced memorization based on unlabeled data to quickly adapt to a new domain without heavy fine-tuning of deep features, which makes it very suitable for deploying on edge devices. Experiments based on four cross-domain real-world datasets show that DML can achieve superior performance of real-time domain adaptation compared with traditional gradient based MLP with more than 10% improvement of accuracy while reducing 87% of the timing cost of optimization.

Large Language Model with Graph Convolution for Recommendation
Yingpeng Du, Ziyan Wang, Zhu Sun, Haoyan Chua, Hongzhi Liu, Zhonghai Wu, Yining Ma, Jie Zhang, Youchen Sun
https://arxiv.org/abs/2402.08859

Large Language Model with Graph Convolution for Recommendation
In recent years, efforts have been made to use text information for better user profiling and item characterization in recommendations. However, text information can sometimes be of low quality, hindering its effectiveness for real-world applications. With knowledge and reasoning capabilities capsuled in Large Language Models (LLMs), utilizing LLMs emerges as a promising way for description improvement. However, existing ways of prompting LLMs with raw texts ignore structured knowledge of user-…

Brain-inspired Distributed Memorization Learning for Efficient Feature-free Unsupervised Domain Adaptation
Jianming Lv, Depin Liang, Zequan Liang, Yaobin Zhang, Sijun Xia
https://arxiv.org/abs/2402.14598 https://arxiv.org/pdf/2402.14598
arXiv:2402.14598v1 Announce Type: new
Abstract: Compared with gradient based artificial neural networks, biological neural networks usually show a more powerful generalization ability to quickly adapt to unknown environments without using any gradient back-propagation procedure. Inspired by the distributed memory mechanism of human brains, we propose a novel gradient-free Distributed Memorization Learning mechanism, namely DML, to support quick domain adaptation of transferred models. In particular, DML adopts randomly connected neurons to memorize the association of input signals, which are propagated as impulses, and makes the final decision by associating the distributed memories based on their confidence. More importantly, DML is able to perform reinforced memorization based on unlabeled data to quickly adapt to a new domain without heavy fine-tuning of deep features, which makes it very suitable for deploying on edge devices. Experiments based on four cross-domain real-world datasets show that DML can achieve superior performance of real-time domain adaptation compared with traditional gradient based MLP with more than 10% improvement of accuracy while reducing 87% of the timing cost of optimization.

Predicting Mergers and Acquisitions in Competitive Industries: A Model Based on Temporal Dynamics and Industry Networks
Dayu Yang
https://arxiv.org/abs/2404.07298 https://arxiv.org/pdf/2404.07298
arXiv:2404.07298v1 Announce Type: new
Abstract: M&A activities are pivotal for market consolidation, enabling firms to augment market power through strategic complementarities. Existing research often overlooks the peer effect, the mutual influence of M&A behaviors among firms, and fails to capture complex interdependencies within industry networks. Common approaches suffer from reliance on ad-hoc feature engineering, data truncation leading to significant information loss, reduced predictive accuracy, and challenges in real-world application. Additionally, the rarity of M&A events necessitates data rebalancing in conventional models, introducing bias and undermining prediction reliability. We propose an innovative M&A predictive model utilizing the Temporal Dynamic Industry Network (TDIN), leveraging temporal point processes and deep learning to adeptly capture industry-wide M&A dynamics. This model facilitates accurate, detailed deal-level predictions without arbitrary data manipulation or rebalancing, demonstrated through superior evaluation results from M&A cases between January 1997 and December 2020. Our approach marks a significant improvement over traditional models by providing detailed insights into M&A activities and strategic recommendations for specific firms.

Large Language Model with Graph Convolution for Recommendation
Yingpeng Du, Ziyan Wang, Zhu Sun, Haoyan Chua, Hongzhi Liu, Zhonghai Wu, Yining Ma, Jie Zhang, Youchen Sun
https://arxiv.org/abs/2402.08859

Large Language Model with Graph Convolution for Recommendation
In recent years, efforts have been made to use text information for better user profiling and item characterization in recommendations. However, text information can sometimes be of low quality, hindering its effectiveness for real-world applications. With knowledge and reasoning capabilities capsuled in Large Language Models (LLMs), utilizing LLMs emerges as a promising way for description improvement. However, existing ways of prompting LLMs with raw texts ignore structured knowledge of user-…

Double-stranded RNA activated caspase oligomerizer ( #DRACO ) technology, is an approach to ♦️selectively killing cells in which viral replication is taking place. ♦️
DRACO offered the promise of being broadly and rapidly effective for 🔸ending infection by many different viruses, and doing so with little adaptation of the core technology from virus to virus,🔸 a big improvement over the present state of…

This drug could be a game-changer for the human race
There are thousands of viruses in our world. But only about 220 are known to infect humans. Most people don’t understand how viruses work. They are not living things. They are very tiny packages of organic chemistry that get into an animal cell and...