We address the Statistical Process Control (SPC) of high-dimensional, dynamic industrial processes from two complementary perspectives: manifold fitting and manifold learning, both of which assume data lies on an underlying nonlinear, lower dimensional space. We propose two distinct monitoring frameworks for online or 'phase II' Statistical Process Control (SPC). The first method leverages state-of-the-art techniques in manifold fitting to accurately approximate the manifold where the data resides within the ambient high-dimensional space. It then monitors deviations from this manifold using a novel scalar distribution-free control chart. In contrast, the second method adopts a more traditional approach, akin to those used in linear dimensionality reduction SPC techniques, by first embedding the data into a lower-dimensional space before monitoring the embedded observations. We prove how both methods provide a controllable Type I error probability, after which they are contrasted for their corresponding fault detection ability. Extensive numerical experiments on a synthetic process and on a replicated Tennessee Eastman Process show that the conceptually simpler manifold-fitting approach achieves performance competitive with, and sometimes superior to, the more classical lower-dimensional manifold monitoring methods. In addition, we demonstrate the practical applicability of the proposed manifold-fitting approach by successfully detecting surface anomalies in a real image dataset of electrical commutators.

We consider the solvable neural scaling model with three parameters: data complexity, target complexity, and model-parameter-count. We use this neural scaling model to derive new predictions about the compute-limited, infinite-data scaling law regime. To train the neural scaling model, we run one-pass stochastic gradient descent on a mean-squared loss. We derive a representation of the loss curves which holds over all iteration counts and improves in accuracy as the model parameter count grows. The phase boundaries are determined by the relative importance of model capacity, optimizer noise, and embedding of the features.

The AgentSociety Challenge is the first competition in the Web Conference that aims to explore the potential of Large Language Model (LLM) agents in modeling user behavior and enhancing recommender systems on web platforms. The Challenge consists of two tracks: the User Modeling Track and the Recommendation Track. Participants are tasked to utilize a combined dataset from Yelp, Amazon, and Goodreads, along with an interactive environment simulator, to develop innovative LLM agents. The Challenge has attracted 295 teams across the globe and received over 1,400 submissions in total over the course of 37 official competition days. The participants have achieved 21.9% and 20.3% performance improvement for Track 1 and Track 2 in the Development Phase, and 9.1% and 15.9% in the Final Phase, representing a significant accomplishment. This paper discusses the detailed designs of the Challenge, analyzes the outcomes, and highlights the most successful LLM agent designs. To support further research and development, we have open-sourced the benchmark environment at https://tsinghua-fib-lab.github.io/AgentSocietyChallenge.

Collusion is a complex phenomenon in which companies secretly collaborate to engage in fraudulent practices. This paper presents an innovative methodology for detecting and predicting collusion patterns in different national markets using neural networks (NNs) and graph neural networks (GNNs). GNNs are particularly well suited to this task because they can exploit the inherent network structures present in collusion and many other economic problems. Our approach consists of two phases: In Phase I, we develop and train models on individual market datasets from Japan, the United States, two regions in Switzerland, Italy, and Brazil, focusing on predicting collusion in single markets. In Phase II, we extend the models' applicability through zero-shot learning, employing a transfer learning approach that can detect collusion in markets in which training data is unavailable. This phase also incorporates out-of-distribution (OOD) generalization to evaluate the models' performance on unseen datasets from other countries and regions. In our empirical study, we show that GNNs outperform NNs in detecting complex collusive patterns. This research contributes to the ongoing discourse on preventing collusion and optimizing detection methodologies, providing valuable guidance on the use of NNs and GNNs in economic applications to enhance market fairness and economic welfare.

Trade finance refers to products and financial instruments used to facilitate the export and import of trade and commerce--and, thereby, the smooth conduct of business. Some of the most popular instruments in trade finance are letters of credit (LC), bank guarantees (BG), documentary collections and remittances. Essentially, these instruments have one primary function: enabling parties to the trade to make a transaction and mitigate the associated risks related to supply and payment. Trade finance drives the global economy. This segment will only grow in the future, notwithstanding temporary setbacks like the Covid-19 pandemic or geopolitical conflicts.

Its name is'Artificial bee colony algorithm' and it follows a pattern observed in nature about bees: A fixed number of N'bees' is positioned randomly (uniform) in the domain. Local Search: In each step, each bee first tries to find a better position by selecting randomly another bee and moving a random distance towards or away from its counterpart. It only performs the actual move in case the new position would be a better one than the current. Onlooker Phase: When all bees have performed the local search, they are relocated by a categorical distribution w.r.t a fitness value. In detail, each bee is assigned a fitness by computing the distance of its value to the value of the current'worst' bee.

Reading an article, watching a video on TikTok or YouTube, listening to a podcast while you're out running, you feel you have a reasonable expectation the content you're consuming is created by a human being. There is good reason to assume at least part of what you're consuming was either created by or assisted by an AI or some form of NLP (Natural Language Processor) or machine learning algorithm. Whether it's a TikTok video about a viral trend, an article in a renowned newspaper, or an image accompanying a news story on television, chances are some forms of AI generation has taken place between the idea of the story being created and the story reaching you. It could be the image was generated using DALL·E 2 or another image-generating AI, it could be the title, or lede, or social media text was generated by an NLP, it's quite likely part of or the entire text was written by an AI based on the prompts and prior writings of a human creator, and if you leave your young kids watching YouTube videos, there's a very high chance they'll encounter videos entirely conceived of and generated by an AI. Whereas Web 1.0 was defined by people being able to publish content using HTML, CSS (and eventually JavaScript), and Web 2.0 was defined by people being able to publish content through user-friendly applications that generated the HTML and CSS and JavaScript for them, the next stage of the web is being defined right now.

AI maturity comes down to mastering critical capabilities in the right combinations--not only in data and AI but also in organizational strategy, talent, and culture. The AI transformation is occurring much faster than the digital transformation, because early successes have increased faith in AI as a value driver. There is a significant incentive to move rapidly. According to new research from Accenture, 'The art of AI Maturity', 63% of 1,200 companies were identified as "Experimenters," or companies stuck in the experimentation phase of their AI lives. They risk losing money since they haven't fully tapped into the technology's potential to innovate and revolutionize their industry. The companies with the highest advanced AI are already using this money.