Scaling and mechanism of the propagation speed of the upstream turbulent front in pipe flow

Scaling and mechanism of the propagation speed of turbulent fronts in pipe flow with the Reynolds number has been a long-standing problem in the past decades. Here, we derive an explicit scaling law of the upstream front speed, which approaches to a power-law scaling at high Reynolds numbers and explain the underlying mechanism. Our data show that the average wall distance of low-speed streaks at the tip of the upstream front, where transition occurs, appears to be constant in local wall units in the wide bulk-Reynolds-number range investigated, between 5000 and 60000. By further assuming that the axial propagation of velocity fluctuations at the front tip, resulting from streak instabilities, is dominated by the advection of the local mean flow, the front speed can be derived as an explicit function of the Reynolds number. The derived formula agrees well with the measured speed by front tracking. Our finding reveals the relationship between the structure and speed of a front, which enables to obtain a close approximation of the front speed based on a single velocity field without having to track the front over time

Key Structures: Improved Related-Key Boomerang Attack against the Full AES-256

This paper introduces structure to key, in the related-key attack settings. While the idea of structure has been long used in keyrecovery attacks against block ciphers to enjoy the birthday effect, the same had not been applied to key materials due to the fact that key structure results in uncontrolled differences in key and hence affects the validity or probabilities of the differential trails. We apply this simple idea to improve the related-key boomerang attack against AES-256 by Biryukov and Khovratovich in 2009. Surprisingly, it turns out to be effective, i.e., both data and time complexities are reduced by a factor of about 2^8, to 2^92 and 2^91 respectively, at the cost of the amount of required keys increased from 4 to 2^19. There exist some tradeoffs between the data/time complexity and the number of keys. To the best of our knowledge, this is the first essential improvement of the attack against the full AES-256 since 2009. It will be interesting to see if the structure technique can be applied to other AES-like block ciphers, and to tweaks rather than keys of tweakable block ciphers so the amount of required keys of the attack will not be affected

Searching the Adversarial Example in the Decision Boundary

Deep learning technology achieves state of the art result in many computer vision missions. However, some researchers point out that current widely used deep learning architectures are vulnerable to adversarial examples. Adversarial examples are inputs generated by applying small and often imperceptible perturbation to examples in the dataset, such that the perturbed examples can degrade the performance of the deep learning architecture.In the paper, we propose a novel adversarial examples generation method. Adversarial examples generated using this method can have small perturbation and have more diversity compare to adversarial examples generated by other method

MovieChat: From Dense Token to Sparse Memory for Long Video Understanding

Recently, integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific pre-defined vision tasks. Yet, existing systems can only handle videos with very few frames. For long videos, the computation complexity, memory cost, and long-term temporal connection impose additional challenges. Taking advantage of the Atkinson-Shiffrin memory model, with tokens in Transformers being employed as the carriers of memory in combination with our specially designed memory mechanism, we propose the MovieChat to overcome these challenges. MovieChat achieves state-of-the-art performance in long video understanding, along with the released MovieChat-1K benchmark with 1K long video and 14K manual annotations for validation of the effectiveness of our method.Comment: CVPR 2024. First three authors contribute equally to this work. Project Website https://rese1f.github.io/MovieChat

Genotype-phenotype correlation in patients with 21-hydroxylase deficiency.

INTRODUCTION: 21-hydroxylase deficiency (21OHD) is the most common cause of congenital adrenal hyperplasia (CAH). However, patients with 21OHD manifest various phenotypes due to a wide-spectrum residual enzyme activity of different CYP21A2 mutations. METHODS: A total of 15 individuals from three unrelated families were included in this study. Target Capture-Based Deep Sequencing and Restriction Fragment Length Polymorphism was conducted on peripheral blood DNA of the three probands to identify potential mutations/deletions in CYP21A2; Sanger sequencing was conducted with the DNA from the family members of the probands. RESULTS: Dramatically different phenotypes were seen in the three probands of CAH with different compound heterozygous mutations in CYP21A2. Proband 1 manifested simple virilizing with mutations of 30-kb deletion/c.[188A\u3eT;518T\u3eA], the latter is a novel double mutants classified as SV associated mutation. Although both probands carry the same compound mutations [293-13C\u3eG]:[518T\u3eA], gonadal dysfunction and giant bilateral adrenal myelolipoma were diagnosed for proband 2 and proband 3, respectively. CONCLUSION: Both gender and mutations contribute to the phenotypes, and patients with the same compound mutations and gender could present with different phenotypes. Genetic analysis could help the etiologic diagnosis, especially for atypical 21OHD patients

Could social robots facilitate children with autism spectrum disorders in learning distrust and deception?

Social robots have been increasingly involved in our daily lives and provide a new environment for children\u27s growth. The current study aimed to examine how children with and without Autism Spectrum Disorders (ASD)learned complex social rules from a social robot through distrust and deception games. Twenty children with ASD between the ages of 5–8 and 20 typically-developing (TD)peers whose age and IQ were matched participated in distrust and deception tasks along with an interview about their perception of the human-likeness of the robot. The results demonstrated that: 1)children with ASD were slower to learn to and less likely to distrust and deceive a social robot than TD children and 2)children with ASD who perceived the robot to appear more human-like had more difficulty in learning to distrust the robot. Besides, by comparing to a previous study the results showed that children with ASD appeared to have more difficulty in learning to distrust a human compared to a robot, particularly in the early phase of learning. Overall, our study verified that social robots could facilitate children with ASD\u27s learning of some social rules and showed that children\u27s perception of the robot plays an important role in their social learning, which provides insights on robot design and its clinical applications in ASD intervention

Defragmenting markets: Evidence from agency MBS

Agency mortgage-backed securities (MBS) issued by Fannie Mae and Freddie Mac have historically traded in separate forward markets. We study the consequences of this fragmentation, showing that market liquidity endogenously concentrated in Fannie Mae MBS, leading to higher issuance and trading volume, lower transaction costs, higher security prices, and a lower primary market cost of capital for Fannie Mae. We then analyze a change in market design - the Single Security Initiative - which consolidated Fannie Mae and Freddie Mac MBS trading into a single market in June 2019. We find that consolidation increased the liquidity and prices of Freddie Mac MBS without measurably reducing liquidity for Fannie Mae; this was in part achieved by aligning characteristics of the underlying MBS pools issued by the two agencies. Prices partially converged prior to the consolidation event, in anticipation of future liquidity. Consolidation increased Freddie Mac's fee income by enabling it to remove discounts that previously compensated loan sellers for lower liquidity