Probabilistic cloning with supplementary information

We consider probabilistic cloning of a state chosen from a mutually nonorthogonal set of pure states, with the help of a party holding supplementary information in the form of pure states. When the number of states is 2, we show that the best efficiency of producing m copies is always achieved by a two-step protocol in which the helping party first attempts to produce m-1 copies from the supplementary state, and if it fails, then the original state is used to produce m copies. On the other hand, when the number of states exceeds two, the best efficiency is not always achieved by such a protocol. We give examples in which the best efficiency is not achieved even if we allow any amount of one-way classical communication from the helping party.Comment: 6 pages, no figure

Rabbit cardiac and slow-twitch muscle express the same phospholamban gene

AbstractThe nucleotide sequences of cDNAs encoding phospholamban were found to be virtually identical when the cDNA clones were isolated from rabbit slow-twitch (soleus) and rabbit cardiac muscle libraries. These findings demonstrate that both types of muscle express the same phospholamban gene. The deduced amino acid sequences of rabbit and dog phospholamban were identical except for a change from Asp (dog) to Glu (rabbit) at position 2. The nucleotide sequences of the 5′- and the very long 3′-untranslated regions of rabbit and dog phospholamban cDNAs also exhibited a high percentage of identity

Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos

Detecting manipulated images and videos is an important topic in digital media forensics. Most detection methods use binary classification to determine the probability of a query being manipulated. Another important topic is locating manipulated regions (i.e., performing segmentation), which are mostly created by three commonly used attacks: removal, copy-move, and splicing. We have designed a convolutional neural network that uses the multi-task learning approach to simultaneously detect manipulated images and videos and locate the manipulated regions for each query. Information gained by performing one task is shared with the other task and thereby enhance the performance of both tasks. A semi-supervised learning approach is used to improve the network's generability. The network includes an encoder and a Y-shaped decoder. Activation of the encoded features is used for the binary classification. The output of one branch of the decoder is used for segmenting the manipulated regions while that of the other branch is used for reconstructing the input, which helps improve overall performance. Experiments using the FaceForensics and FaceForensics++ databases demonstrated the network's effectiveness against facial reenactment attacks and face swapping attacks as well as its ability to deal with the mismatch condition for previously seen attacks. Moreover, fine-tuning using just a small amount of data enables the network to deal with unseen attacks.Comment: Accepted to be Published in Proceedings of the IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) 2019, Florida, US

Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems

Due to its convenience, biometric authentication, especial face authentication, has become increasingly mainstream and thus is now a prime target for attackers. Presentation attacks and face morphing are typical types of attack. Previous research has shown that finger-vein- and fingerprint-based authentication methods are susceptible to wolf attacks, in which a wolf sample matches many enrolled user templates. In this work, we demonstrated that wolf (generic) faces, which we call "master faces," can also compromise face recognition systems and that the master face concept can be generalized in some cases. Motivated by recent similar work in the fingerprint domain, we generated high-quality master faces by using the state-of-the-art face generator StyleGAN in a process called latent variable evolution. Experiments demonstrated that even attackers with limited resources using only pre-trained models available on the Internet can initiate master face attacks. The results, in addition to demonstrating performance from the attacker's point of view, can also be used to clarify and improve the performance of face recognition systems and harden face authentication systems.Comment: Accepted to be Published in Proceedings of the 2020 International Joint Conference on Biometrics (IJCB 2020), Houston, US

T cell receptor alpha-chain gene rearrangements in B-precursor leukemia are in contrast to the findings in T cell acute lymphoblastic leukemia. Comparative study of T cell receptor gene rearrangement in childhood leukemia.

We have analyzed T cell receptor alpha-chain gene configuration using three genomic joining (J) region probes in 64 children with acute lymphoblastic leukemia (ALL). 11 out of 18 T-ALLs were T3 positive; alpha-chain gene rearrangements were demonstrated in only two of 18, indicating that the majority of T-ALLs would have rearrangements involving J alpha segments located upstream of these probes. In contrast, 15 out of 46 B-precursor ALLs showed rearrangements of the alpha-chain gene and J alpha segments located approximately 20-30 kb upstream of the constant region were involved in 13 of these patients. Nine of 15 B-precursor ALLs with rearranged alpha-chain genes had rearrangements of both gamma- and beta-chain genes, whereas the remaining six had no rearrangements of gamma- and beta-chain genes. These findings indicated that alpha-chain gene rearrangement is not specific for T lineage cells and gamma- and/or beta-chain gene rearrangement does not appear essential for alpha-chain gene rearrangement, at least in B-precursor leukemic cells

Rearrangement of Variable Region T Cell Receptor y Genes in Acute Lymphoblastic Leukemia Vy Gene Usage Differs in Mature and Immature T Cells

Using probes recognizing variable regions (V gamma) and joining regions (J gamma) of the T cell receptor (TCR) gamma gene, we have analyzed the usage of V gamma genes in 24 patients with T cell acute lymphoblastic leukemia (ALL) and 36 patients with B-precursor ALL. In CD3- T-ALL derived from immature T cells, V gamma genes more proximal to J gamma were frequently rearranged; V gamma 8, V gamma 9, V gamma 10, and V gamma 11 were used in 19 of 24 rearrangements. In contrast, CD3+ T-ALL derived from a more mature stage of T cell ontogeny, showed a high frequency of rearrangements involving V gamma genes distal to J gamma; V gamma 2, V gamma 3, V gamma 4, and V gamma 5 were used in 17 of 25 rearrangements. In B-precursor ALL, no notable bias of V gamma gene usage was observed. This probably reflects the possibility that TCR genes may not rearrange according to a T cell hierarchy when under control of a B cell gene program. Furthermore, deletions of those V gamma genes located 3' to rearranged V gamma genes were observed in all patients analyzed. This supports the theory that loop deletion is a major mechanism for TCR-gamma gene rearrangement