TY - CONF
AB - Direct Anonymous Attestation (DAA) is one of the most complex cryptographic protocols deployed in practice. It allows an embedded secure processor known as a Trusted Platform Module (TPM) to attest to the configuration of its host computer without violating the owner’s privacy. DAA has been standardized by the Trusted Computing Group and ISO/IEC.
The security of the DAA standard and all existing schemes is analyzed in the random-oracle model. We provide the first constructions of DAA in the standard model, that is, without relying on random oracles. Our constructions use new building blocks, including the first efficient signatures of knowledge in the standard model, which have many applications beyond DAA.
AU - Bernhard, David
AU - Fuchsbauer, Georg
AU - Ghadafi, Essam
ID - 2260
TI - Efficient signatures of knowledge and DAA in the standard model
VL - 7954
ER -
TY - JOUR
AB - Faithful progression through the cell cycle is crucial to the maintenance and developmental potential of stem cells. Here, we demonstrate that neural stem cells (NSCs) and intermediate neural progenitor cells (NPCs) employ a zinc-finger transcription factor specificity protein 2 (Sp2) as a cell cycle regulator in two temporally and spatially distinct progenitor domains. Differential conditional deletion of Sp2 in early embryonic cerebral cortical progenitors, and perinatal olfactory bulb progenitors disrupted transitions through G1, G2 and M phases, whereas DNA synthesis appeared intact. Cell-autonomous function of Sp2 was identified by deletion of Sp2 using mosaic analysis with double markers, which clearly established that conditional Sp2-null NSCs and NPCs are M phase arrested in vivo. Importantly, conditional deletion of Sp2 led to a decline in the generation of NPCs and neurons in the developing and postnatal brains. Our findings implicate Sp2-dependent mechanisms as novel regulators of cell cycle progression, the absence of which disrupts neurogenesis in the embryonic and postnatal brain.
AU - Liang, Huixuan
AU - Xiao, Guanxi
AU - Yin, Haifeng
AU - Hippenmeyer, Simon
AU - Horowitz, Jonathan
AU - Ghashghaei, Troy
ID - 2264
IS - 3
JF - Development
TI - Neural development is dependent on the function of specificity protein 2 in cell cycle progression
VL - 140
ER -
TY - CONF
AB - Representation languages for coalitional games are a key research area in algorithmic game theory. There is an inher-
ent tradeoff between how general a language is, allowing it to capture more elaborate games, and how hard it is computationally to optimize and solve such games. One prominent such language is the simple yet expressive
Weighted Graph Games (WGGs) representation (Deng and Papadimitriou 1994), which maintains knowledge about synergies between agents in the form of an edge weighted graph. We consider the problem of finding the optimal coalition structure in WGGs. The agents in such games are vertices in a graph, and the value of a coalition is the sum of the weights of the edges present between coalition members. The optimal coalition structure is a partition of the agents to coalitions, that maximizes the sum of utilities obtained by the coalitions. We show that finding the optimal coalition structure is not only hard for general graphs, but is also intractable for restricted families such as planar graphs which are amenable for many other combinatorial problems. We then provide algorithms with constant factor approximations for planar, minorfree and bounded degree graphs.
AU - Bachrach, Yoram
AU - Kohli, Pushmeet
AU - Kolmogorov, Vladimir
AU - Zadimoghaddam, Morteza
ID - 2270
TI - Optimal Coalition Structures in Cooperative Graph Games
ER -
TY - CONF
AB - We consider Conditional Random Fields (CRFs) with pattern-based potentials defined on a chain. In this model the energy of a string (labeling) x1...xn is the sum of terms over intervals [i,j] where each term is non-zero only if the substring xi...xj equals a prespecified pattern α. Such CRFs can be naturally applied to many sequence tagging problems.
We present efficient algorithms for the three standard inference tasks in a CRF, namely computing (i) the partition function, (ii) marginals, and (iii) computing the MAP. Their complexities are respectively O(nL), O(nLℓmax) and O(nLmin{|D|,log(ℓmax+1)}) where L is the combined length of input patterns, ℓmax is the maximum length of a pattern, and D is the input alphabet. This improves on the previous algorithms of (Ye et al., 2009) whose complexities are respectively O(nL|D|), O(n|Γ|L2ℓ2max) and O(nL|D|), where |Γ| is the number of input patterns.
In addition, we give an efficient algorithm for sampling. Finally, we consider the case of non-positive weights. (Komodakis & Paragios, 2009) gave an O(nL) algorithm for computing the MAP. We present a modification that has the same worst-case complexity but can beat it in the best case.
AU - Takhanov, Rustem
AU - Kolmogorov, Vladimir
ID - 2272
IS - 3
T2 - ICML'13 Proceedings of the 30th International Conference on International
TI - Inference algorithms for pattern-based CRFs on sequence data
VL - 28
ER -
TY - GEN
AB - We propose a new family of message passing techniques for MAP estimation in graphical models which we call Sequential Reweighted Message Passing (SRMP). Special cases include well-known techniques such as Min-Sum Diusion (MSD) and a faster Sequential Tree-Reweighted Message Passing (TRW-S). Importantly, our derivation is simpler than the original derivation of TRW-S, and does not involve a decomposition into trees. This allows easy generalizations. We present such a generalization for the case of higher-order graphical models, and test it on several real-world problems with promising results.
AU - Vladimir Kolmogorov
ID - 2273
TI - Reweighted message passing revisited
ER -