The following items are provided: A comparison between Dynamic Quantization and TDQ in Section 2. Ablation study on time step encoding in Section 3. Detailed TDQ Module architecture in Section 4. Comparison with multiple quantization interval directly on PTQ in Section 5. Integration of TDQ with various QA T schemes in Section 6. V arious experiments about robustness of the TDQ Module in Section 7. Detailed experimental results on the Output dynamics of the TDQ module in Section 8. Detailed experimental results on the Evolution of Activation Distribution in Section 9. V arious non-cherry-picked results of generated images in Section 10.

This work introduces a novel deep learning-based approach for gravitational wave anomaly detection, aiming to overcome the limitations of traditional matched filtering techniques in identifying unknown waveform gravitational wave signals. We introduce a modified convolutional neural network architecture inspired by ResNet that leverages residual blocks to extract high-dimensional features, effectively capturing subtle differences between background noise and gravitational wave signals. This network architecture learns a high-dimensional projection while preserving discrepancies with the original input, facilitating precise identification of gravitational wave signals. In our experiments, we implement an innovative data augmentation strategy that generates new data by computing the arithmetic mean of multiple signal samples while retaining the key features of the original signals. In the NSF HDR A3D3: Detecting Anomalous Gravitational Wave Signals competition, it is honorable for us (group name: easonyan123) to get to the first place at the end with our model achieving a true negative rate (TNR) of 0.9708 during development/validation phase and 0.9832 on an unseen challenge dataset during final/testing phase, the highest among all competitors. These results demonstrate that our method not only achieves excellent generalization performance but also maintains robust adaptability in addressing the complex uncertainties inherent in gravitational wave anomaly detection.

Reading relevant scientific papers and analyzing research development trends is a critical step in generating new scientific ideas. However, the rapid increase in the volume of research literature and the complex citation relationships make it difficult for researchers to quickly analyze and derive meaningful research trends. The development of large language models (LLMs) has provided a novel approach for automatically summarizing papers and generating innovative research ideas. However, existing paper-based idea generation methods either simply input papers into LLMs via prompts or form logical chains of creative development based on citation relationships, without fully exploiting the semantic information embedded in these citations. Inspired by knowledge graphs and human cognitive processes, we propose a framework called the Graph of AI Ideas (GoAI) for the AI research field, which is dominated by open-access papers. This framework organizes relevant literature into entities within a knowledge graph and summarizes the semantic information contained in citations into relations within the graph. This organization effectively reflects the relationships between two academic papers and the advancement of the AI research field. Such organization aids LLMs in capturing the current progress of research, thereby enhancing their creativity. Experimental results demonstrate the effectiveness of our approach in generating novel, clear, and effective research ideas.

The International Semantic Web Research School (ISWS) is a week-long intensive program designed to immerse participants in the field. This document reports a collaborative effort performed by ten teams of students, each guided by a senior researcher as their mentor, attending ISWS 2023. Each team provided a different perspective to the topic of creative AI, substantiated by a set of research questions as the main subject of their investigation. The 2023 edition of ISWS focuses on the intersection of Semantic Web technologies and Creative AI. ISWS 2023 explored various intersections between Semantic Web technologies and creative AI. A key area of focus was the potential of LLMs as support tools for knowledge engineering. Participants also delved into the multifaceted applications of LLMs, including legal aspects of creative content production, humans in the loop, decentralised approaches to multimodal generative AI models, nanopublications and AI for personal scientific knowledge graphs, commonsense knowledge in automatic story and narrative completion, generative AI for art critique, prompt engineering, automatic music composition, commonsense prototyping and conceptual blending, and elicitation of tacit knowledge. As Large Language Models and semantic technologies continue to evolve, new exciting prospects are emerging: a future where the boundaries between creative expression and factual knowledge become increasingly permeable and porous, leading to a world of knowledge that is both informative and inspiring.

Weaknesses: No Explanation of Transformations of Stochastic Processes: I was under the impression that transforming / reparameterizing a stochasic process is non-trivial. Thus, I was expecting Equation 7 to include a second derivative term. I'm not saying that Equation 7 is wrong, per se---transforming just the increments agrees with intuition. However, the problem is that the paper provides no explanation or mathematical references for stochastic processes and their transformations. There are *zero* citations in both Section 2.2 and Section 3.1.

Springer has released hundreds of free books on a wide range of topics to the general public. The list, which includes 408 books in total, covers a wide range of scientific and technological topics. In order to save you some time, I have created one list of all the books (65 in number) that are relevant to the data and Machine Learning field. Among the books, you will find those dealing with the mathematical side of the domain (Algebra, Statistics, and more), along with more advanced books on Deep Learning and other advanced topics. You also could find some good books in various programming languages such as Python, R, MATLAB, etc.

A multi-armed bandit (MAB) is a machine learning framework that uses complex algorithms to dynamically allocate resources when presented with multiple choices. In other words, it's an advanced form of A/B testing that's most commonly used by data analysts, medicine researchers, and marketing specialists. Before we delve deeper into the concept of multi-armed bandits, we need to discuss reinforcement learning, as well as the exploration vs. exploitation dilemma. Then, we can focus on various bandit solutions and practical applications. Alongside supervised and unsupervised learning, reinforcement learning is one of the basic three paradigms of machine learning. Unlike the first two archetypes we mentioned, reinforcement learning focuses on rewards and punishments for the agent whenever it interacts with the environment.

GNNs are a type of neural network that can process data with complex, non-Euclidean structure, such as graphs and networks. They have been widely used in AI and ML for tasks such as node classification, graph classification, and link prediction. One key area of focus has been on developing more efficient GNN architectures for large-scale graphs. This has included the development of hierarchical and modular GNNs, as well as the use of sparsification and approximation techniques to reduce the computational complexity of training and inference. Another area of focus has been on improving the ability of GNNs to capture long-range dependencies and higher-order connectivity patterns in graphs.

In 2023, do you want to be a Data Scientist, Machine Learning Engineer, or Deep Learning Engineer? I can share some advice if you don't know where you get started. You can solve a business problem with data and you can enter this field with amazing courses. The 2022 State of Data Science report of Anaconda shows us 20% of students want to enter the data science profession. But one of the biggest challenging questions is "Where I can start and What experience is actually required".

The Kalman filter is a powerful tool for modeling and estimating the state of dynamic systems. It is widely used in a variety of fields, including engineering, economics, and robotics, and has proven to be particularly useful for tracking objects or processes that are subject to noise and uncertainty. At its core, the Kalman filter is an algorithm that uses a series of measurements observed over time to estimate the underlying state of a system. It does this by combining the measurements with a mathematical model of the system, taking into account the uncertainties in both the measurements and the model. The Kalman filter has a number of attractive features that make it well-suited to a wide range of use cases. It is computationally efficient, easy to implement, and can handle both linear and nonlinear systems.