Africa-China exchange

Events can be found on calendar page, with code AC.

An informal seminar to connect researchers in Africa, China and elsewhere.

Focussing on pure mathematics and related topics.

Welcome to register for updates: from China / elsewhere.

Organizer: Will Donovan

We plan to use the following format, to promote conversation in online setting.

• 20 minute talk (introduction)
• 10 minute discussion
• 20 minute talk (continued)

Participants

• Walle Tilahun, Mizan Tepi University, Ethiopia
• Lakachew Ayal Chekol, Woldia University, Ethiopia
• Herbert Batte, Makerere University / Wits University, Johannesburg
• Tagay Takele Fikadu, Ethiopia
• Bukayaw Kindu, Debre Markos University
• Annet Kyomuhangi, Busitema University, Tororo, Uganda
• Kefale Liche Mandida, Addis Ababa University, Ethiopia
• Brian Makonzi, Makerere University
• Elizabeth Mrema, Dar es Salaam, Tanzania
• Caroline Namanya, Uganda
• Ismail Opio, Lira University, Uganda
• Belela Samuel, Ethiopia
• Chala Tena Moti, Dire Dawa, Ethiopia
• Tilahun Walle, Ethiopia
• Girma Workneh, Ethiopia
• Beza Zeleke, Ethiopia

Talks

• West Africa Time: 9am
• East Africa Time: 11am
• China time: 4pm

Herbert Batte (Makerere University / Wits University, Johannesburg)

Nov 28 2024

On the Largest Prime Factor of the k-generalized Lucas Numbers

Elizabeth Mrema

Dec 5 2024

Exploration of Cyclotomic Extensions

SUN Youran 孙悠然

Mar 5 2025

Phase Transitions in Large Language Models and the O(N) Model

Large language models (LLMs) exhibit unprecedentedly rich scaling behaviors. In physics, scaling behavior is closely related to phase transitions, critical phenomena, and field theory. To investigate the phase transition phenomena in LLMs, we reformulated the Transformer architecture as an O(N) model. Our study reveals two distinct phase transitions corresponding to the temperature used in text generation and the model’s parameter size, respectively. The first phase transition enables us to estimate the internal dimension of the model, while the second phase transition is of higher-depth and signals the emergence of new capabilities. As an application, the energy of the O(N) model can be used to evaluate whether an LLM’s parameters are sufficient to learn the training data.

TBD

Mar 12 2025