IST PubRep
Not a member yet
752 research outputs found
Sort by
New realizations of somewhere statistically binding hashing and positional accumulators
Get PDFA somewhere statistically binding (SSB) hash, introduced by Hubáček and Wichs (ITCS ’15), can be used to hash a long string x to a short digest y = H hk (x) using a public hashing-key hk. Furthermore, there is a way to set up the hash key hk to make it statistically binding on some arbitrary hidden position i, meaning that: (1) the digest y completely determines the i’th bit (or symbol) of x so that all pre-images of y have the same value in the i’th position, (2) it is computationally infeasible to distinguish the position i on which hk is statistically binding from any other position i’. Lastly, the hash should have a local opening property analogous to Merkle-Tree hashing, meaning that given x and y = H hk (x) it should be possible to create a short proof π that certifies the value of the i’th bit (or symbol) of x without having to provide the entire input x. A similar primitive called a positional accumulator, introduced by Koppula, Lewko and Waters (STOC ’15) further supports dynamic updates of the hashed value. These tools, which are interesting in their own right, also serve as one of the main technical components in several recent works building advanced applications from indistinguishability obfuscation (iO). The prior constructions of SSB hashing and positional accumulators required fully homomorphic encryption (FHE) and iO respectively. In this work, we give new constructions of these tools based on well studied number-theoretic assumptions such as DDH, Phi-Hiding and DCR, as well as a general construction from lossy/injective functions
Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space
No full textMany stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space
Hanani-Tutte for radial planarity
Get PDFA drawing of a graph G is radial if the vertices of G are placed on concentric circles C1, . . . , Ck with common center c, and edges are drawn radially: every edge intersects every circle centered at c at most once. G is radial planar if it has a radial embedding, that is, a crossing- free radial drawing. If the vertices of G are ordered or partitioned into ordered levels (as they are for leveled graphs), we require that the assignment of vertices to circles corresponds to the given ordering or leveling. We show that a graph G is radial planar if G has a radial drawing in which every two edges cross an even number of times; the radial embedding has the same leveling as the radial drawing. In other words, we establish the weak variant of the Hanani-Tutte theorem for radial planarity. This generalizes a result by Pach and Tóth
Unifying Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes
Get PDFWe consider Markov decision processes (MDPs) with multiple limit-average
(or mean-payoff) objectives.
There have been two different views: (i) the expectation semantics, where the
goal is to optimize the expected mean-payoff objective, and
(ii) the satisfaction semantics, where the goal is to maximize the
probability of runs such that the mean-payoff value stays above a
given vector.
We consider the problem where the goal is to optimize the expectation under
the constraint that the satisfaction semantics is ensured, and thus consider
a generalization that unifies the existing semantics.
Our problem captures the notion of optimization with respect to strategies
that are risk-averse (i.e., ensures certain probabilistic guarantee).
Our main results are algorithms for the decision problem which are always
polynomial in the size of the MDP.
We also show that an approximation of the Pareto-curve can be computed in
time polynomial in the size of the MDP, and the approximation factor, but
exponential in the number of dimensions.
Finally, we present a complete characterization of the strategy complexity
(in terms of memory bounds and randomization) required to solve our problem
Superfluid behavior of a Bose-Einstein condensate in a random potential
No full textWe investigate the relation between Bose-Einstein condensation (BEC) and superfluidity in the ground state of a one-dimensional model of interacting bosons in a strong random potential. We prove rigorously that in a certain parameter regime the superfluid fraction can be arbitrarily small while complete BEC prevails. In another regime there is both complete BEC and complete superfluidity, despite the strong disorde
Evolution of bow-tie architectures in biology
No full textBow-tie or hourglass structure is a common architectural feature found in many biological systems. A bow-tie in a multi-layered structure occurs when intermediate layers have much fewer components than the input and output layers. Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components, and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes. Little is known, however, about how bow-tie architectures evolve. Here, we address the evolution of bow-tie architectures using simulations of multi-layered systems evolving to fulfill a given input-output goal. We find that bow-ties spontaneously evolve when the information in the evolutionary goal can be compressed. Mathematically speaking, bow-ties evolve when the rank of the input-output matrix describing the evolutionary goal is deficient. The maximal compression possible (the rank of the goal) determines the size of the narrowest part of the network–that is the bow-tie. A further requirement is that a process is active to reduce the number of links in the network, such as product-rule mutations, otherwise a non-bow-tie solution is found in the evolutionary simulations. This offers a mechanism to understand a common architectural principle of biological systems, and a way to quantitate the effective rank of the goals under which they evolved
Quantifying emergent behavior of autonomous robots
No full textQuantifying behaviors of robots which were generated autonomously from task-independent objective functions is an important prerequisite for objective comparisons of algorithms and movements of animals. The temporal sequence of such a behavior can be considered as a time series and hence complexity measures developed for time series are natural candidates for its quantification. The predictive information and the excess entropy are such complexity measures. They measure the amount of information the past contains about the future and thus quantify the nonrandom structure in the temporal sequence. However, when using these measures for systems with continuous states one has to deal with the fact that their values will depend on the resolution with which the systems states are observed. For deterministic systems both measures will diverge with increasing resolution. We therefore propose a new decomposition of the excess entropy in resolution dependent and resolution independent parts and discuss how they depend on the dimensionality of the dynamics, correlations and the noise level. For the practical estimation we propose to use estimates based on the correlation integral instead of the direct estimation of the mutual information based on next neighbor statistics because the latter allows less control of the scale dependencies. Using our algorithm we are able to show how autonomous learning generates behavior of increasing complexity with increasing learning duration
A coherent transcriptional feed-forward motif model for mediating auxin-sensitive PIN3 expression during lateral root development
No full textMultiple plant developmental processes, such as lateral root development, depend on auxin distribution patterns that are in part generated by the PIN-formed family of auxin-efflux transporters. Here we propose that AUXIN RESPONSE FACTOR7 (ARF7) and the ARF7-regulated FOUR LIPS/MYB124 (FLP) transcription factors jointly form a coherent feed-forward motif that mediates the auxin-responsive PIN3 transcription in planta to steer the early steps of lateral root formation. This regulatory mechanism might endow the PIN3 circuitry with a temporal 'memory'; of auxin stimuli, potentially maintaining and enhancing the robustness of the auxin flux directionality during lateral root development. The cooperative action between canonical auxin signalling and other transcription factors might constitute a general mechanism by which transcriptional auxin-sensitivity can be regulated at a tissue-specific level
An exploration of the universe of polyglutamine structures
No full textDeposits of misfolded proteins in the human brain are associated with the development of many neurodegenerative diseases. Recent studies show that these proteins have common traits even at the monomer level. Among them, a polyglutamine region that is present in huntingtin is known to exhibit a correlation between the length of the chain and the severity as well as the earliness of the onset of Huntington disease. Here, we apply bias exchange molecular dynamics to generate structures of polyglutamine expansions of several lengths and characterize the resulting independent conformations. We compare the properties of these conformations to those of the standard proteins, as well as to other homopolymeric tracts. We find that, similar to the previously studied polyvaline chains, the set of possible transient folds is much broader than the set of known-to-date folds, although the conformations have different structures. We show that the mechanical stability is not related to any simple geometrical characteristics of the structures. We demonstrate that long polyglutamine expansions result in higher mechanical stability than the shorter ones. They also have a longer life span and are substantially more prone to form knotted structures. The knotted region has an average length of 35 residues, similar to the typical threshold for most polyglutamine-related diseases. Similarly, changes in shape and mechanical stability appear once the total length of the peptide exceeds this threshold of 35 glutamine residues. We suggest that knotted conformers may also harm the cellular machinery and thus lead to disease
Auxin transporters and binding proteins at a glance
No full textThe plant hormone auxin is a key regulator of plant growth and development. Differences in auxin distribution within tissues are mediated by the polar auxin transport machinery, and cellular auxin responses occur depending on changes in cellular auxin levels. Multiple receptor systems at the cell surface and in the interior operate to sense and interpret fluctuations in auxin distribution that occur during plant development. Until now, three proteins or protein complexes that can bind auxin have been identified. SCFTIR1 [a SKP1-cullin-1-F-box complex that contains transport inhibitor response 1 (TIR1) as the F-box protein] and S-phase-kinaseassociated protein 2 (SKP2) localize to the nucleus, whereas auxinbinding protein 1 (ABP1), predominantly associates with the endoplasmic reticulum and cell surface. In this Cell Science at a Glance article, we summarize recent discoveries in the field of auxin transport and signaling that have led to the identification of new components of these pathways, as well as their mutual interaction