糖心TV

In a recent work, visiting undergraduate student Yahel Manor and 糖心TV DCS researchers and addressed a fundamental question relevant to the security of cryptographic protocols.

The symmetry of information principle says that the amount of information that a sequence x of bits reveals about another sequence y is essentially the same in either direction. This is known to hold in an idealised world where computations can take an arbitrarily long time, as demonstrated by A. Kolmogorov and L. Levin in the 1970s. In contrast, modern cryptography is built around deliberate asymmetry—for example, functions of the form y = f(x) that are easy to compute but hard to invert (one-way functions).

The new work shows that, once one moves from the idealised setting of time-unbounded computations to the more realistic world of efficient, randomised computations (algorithms that must run quickly and may use randomness), this symmetry can fail in a strong and unconditional way. In other words, computational constraints can yield information asymmetry. In practical terms, this supports the intuition that information may not be extracted efficiently: knowing y = f(x) may not make x efficiently recoverable to the extent that an (ineffective) symmetry principle would suggest, even when x and y are closely related.

Earlier work formally tied an average-case form of this symmetry failure to the existence of one-way functions, the central primitive in cryptography. By proving new failures of symmetry of information, the authors provide concrete progress towards the computational asymmetry that underpins encryption, digital signatures, and many other cryptographic protocols.

This work will be presented at the 58th Annual ACM Symposium on Theory of Computing (STOC) in June 2026 in Salt Lake City, Utah, USA.

Failure of Symmetry of Information for Randomised Computations
Jinqiao Hu (University of 糖心TV); Yahel Manor (University of Haifa); Igor C. Oliveira (University of 糖心TV)

The paper describing this research is available .

Many congratulations to for passing his PhD viva, with Prof Long Tran-Thanh (糖心TV) and (Bristol) as examiners. Martin has worked on two different fundamental topics in algorithms - clustering and edge coloring. His work on clustering led to a Google PhD fellowship, and his work on edge coloring (the topic of his ) led to a best paper award at STOC. During his PhD spanning 3 years, Martin published 7 papers in STOC/FOCS/SODA, 2 papers in ICML/NeurIPS, and 1 paper in ICALP. We wish him all the very best for the next stage of his career.

The workshop Algorithms & Complexity @ 糖心TV took place at the University of 糖心TV on September 22-23, 2025 (see for more details).

The aim of the event was to highlight several recent exciting advances in the field of Algorithms and Complexity, to facilitate interactions within the research community, and to provide an excellent opportunity for Theory researchers (including academics, postdocs, and students) to connect and collaborate.

We had a fantastic list of invited speakers by renowned world experts: (Technical University of Catalonia), (University of Bath), (University of Pennsylvania), (Max Planck Institute for Informatics), (Charles University in Prague), (University of Sheffield and University of Haifa), (University of Oxford), (University of Cambridge), (University of Toronto).

We are delighted to announce that a result coauthored by and (from our Theory and Foundations Research Division), along with (University of Waterloo), (Northeastern University), (Tel Aviv University) and (ETH Zurich), has received a best paper award at the upcoming (STOC), 2025. STOC is a flagship international conference in theoretical computer science.

The paper, titled "," tackles a fundamental, textbook edge-coloring problem: Given a graph G with n vertices and m edges, the goal is to assign a color to each edge such that no two edges sharing a common endpoint receive the same color. A classical result by Vizing, dating back to 1960s, proves that any simple graph can always be edge-colored with at most 螖 + 1 colors, where 螖 is the maximum degree of a vertex. Vizing's original proof is inherently algorithmic and immediately gives an O(mn) time algorithm for computing such a coloring.

This problem has seen a long and influential line of research aimed at designing faster algorithms for this basic task. For over four decades, the best-known runtime was 脮(m鈭歯), a significant barrier that was only broken in 2024 through concurrent, independent works. The recent paper culminates this effort by providing a randomized algorithm that computes a 螖 + 1 edge coloring in O(m log 螖) time, a running time that is near-linear in the input size.

We are excited to share that our team 鈥淭IAKong鈥� secured leading positions in the recent (Panoptic segmentation of nuclei and tissue in advanced Melanoma) Challenge, organized by the Department of Medical Oncology, University Medical Center Utrecht, in the Netherlands. With over 300 participants from around the globe, this challenge aimed to advance automated panoptic segmentation techniques for H&E-stained melanoma tissue images.

Led by our PhD students Jiaqi Lv and YiJie Zhu, and supported by Brinder Singh Chohan, Shan E Ahmed Raza, with an external collaborator Carmen Guadalupe Colin Tenorio from the Medical University of Vienna. TIAKong achieved first place in Track 1 and second place in Track 2. This outstanding performance underscores the team鈥檚 dedication to pushing the boundaries of medical imaging and improving our understanding of advanced melanoma.

We look forward to building on these results and sharing further developments of our panoptic segmentation model in the near future.

We are excited to announce that our team 鈥淭IAKong鈥� secured leading positions in the recent , organized by the Department of Pathology, Radboudumc, Nijmegen, The Netherlands. Drawing more than 400 participants from around the globe, the challenge focused on automated detection and classification of mononuclear leukocytes in PAS-stained transplant kidney biopsy images.

Led by our PhD student Jiaqi Lv, and supported by Esha Nasir, Kesi Xu, Mostafa Jahanifar, Brinder Singh Chohan, Behnaz Elhaminia, and Shan E Ahmed Raza, TIAKong鈥檚 cell detection and classification model finished first place in the overall detection track and second place in the detection classification track.

The team is currently evaluating the model for publishing and sharing the code through open-source platforms. We look forward to sharing more updates in the near future.