We explore the relationship between the SU(2) sector of a general integrable field theory and the all-loop guess for the anomalous dimensions of SU(2) operators in N = 4 super Yang-Mills theory. We demonstrate that the SU(2) structure of a nested Bethe ansatz alone reproduces much of the all-loop guess without depending on the details of the particular field theory. We speculate on the implicat...

Remote authentication is a method to authenticate remote users over insecure communication channel. Password-based authentication schemes have been widely deployed to verify the legitimacy of remote users. Very recently, Hsiang et al. pointed out that Yoon et al’s scheme is vulnerable to parallel session attack, masquerading attack and password guess attack. They proposed an improved scheme to ...

In this paper, we present a framework for guess-and-determine attack on stream ciphers that relies on guessing part of the internal state and solving for the remaining unknown cipher state based on known keystream bits. We show that this basic attack can always be extended to a Time-Memory-Data (TMD) Trade-Off attack. This allows us to easily extend any guess-and-determine attack to a guess-and...

A traditional way to design a binary response experiment is to design the experiment to be most efficient for a best guess of the parameter values. A design which is optimal for a best guess however may not be efficient for parameter values close to that best guess. We propose designs which formally account for the prior uncertainty in the parameter values. A design for a situation where the be...

Double-blind review relies on the authors’ ability and willingness to effectively anonymize their submissions. We explore anonymization effectiveness at ASE 2016, OOPSLA 2016, and PLDI 2016 by asking reviewers if they can guess author identities. We find that 74%–90% of reviews contain no correct guess and that reviewers who self-identify as experts on a paper’s topic are more likely to attempt...

CLIP (Contrastive Language-Image Pretraining) is an efficient method for learning computer vision tasks from natural language supervision that has powered a recent breakthrough in deep due to its zero-shot transfer capabilities. By training image-text pairs available on the internet, model transfers non-trivially most without need any data set specific training. In this work, we use implement e...