Sky maps without anisotropies in the cosmic microwave background are a better fit to WMAP's uncalibrated time ordered data than the official sky maps

The purpose of this reanalysis of the WMAP uncalibrated time ordered data (TOD) was two fold. The first was to reassess the reliability of the detection of the anisotropies in the official WMAP sky maps of the cosmic microwave background (CMB). The second was to assess the performance of a proposed criterion in avoiding systematic error in detecting a signal of interest. The criterion was implemented by testing the null hypothesis that the uncalibrated TOD was consistent with no anisotropies when WMAP's hourly calibration parameters were allowed to vary. It was shown independently for all 20 WMAP channels that sky maps with no anisotropies were a better fit to the TOD than those from the official analysis. The recently launched Planck satellite should help sort out this perplexing result.Comment: 11 pages with 1 figure and 2 tables. Extensively rewritten to explain the research bette

Information-capacity description of spin-chain correlations

Information capacities achievable in the multi-parallel-use scenarios are employed to characterize the quantum correlations in unmodulated spin chains. By studying the qubit amplitude damping channel, we calculate the quantum capacity $Q$, the entanglement assisted capacity $C_E$, and the classical capacity $C_1$ of a spin chain with ferromagnetic Heisenberg interactions.Comment: 12 pages, 3 figures; typos corrected (to appear in PRA

Emergence of Zipf's Law in the Evolution of Communication

Zipf's law seems to be ubiquitous in human languages and appears to be a universal property of complex communicating systems. Following the early proposal made by Zipf concerning the presence of a tension between the efforts of speaker and hearer in a communication system, we introduce evolution by means of a variational approach to the problem based on Kullback's Minimum Discrimination of Information Principle. Therefore, using a formalism fully embedded in the framework of information theory, we demonstrate that Zipf's law is the only expected outcome of an evolving, communicative system under a rigorous definition of the communicative tension described by Zipf.Comment: 7 pages, 2 figure

A Static Optimality Transformation with Applications to Planar Point Location

Over the last decade, there have been several data structures that, given a planar subdivision and a probability distribution over the plane, provide a way for answering point location queries that is fine-tuned for the distribution. All these methods suffer from the requirement that the query distribution must be known in advance. We present a new data structure for point location queries in planar triangulations. Our structure is asymptotically as fast as the optimal structures, but it requires no prior information about the queries. This is a 2D analogue of the jump from Knuth's optimum binary search trees (discovered in 1971) to the splay trees of Sleator and Tarjan in 1985. While the former need to know the query distribution, the latter are statically optimal. This means that we can adapt to the query sequence and achieve the same asymptotic performance as an optimum static structure, without needing any additional information.Comment: 13 pages, 1 figure, a preliminary version appeared at SoCG 201

Augmentation of nucleon-nucleus scattering by information entropy

Quantum information entropy is calculated from the nucleon nucleus forward scattering amplitudes. Using a representative set of nuclei, from $^4$He to $^{208}$Pb, and energies, $T_{lab} < 1$\,[GeV], we establish a linear dependence of quantum information entropy as functions of logarithm nuclear mass $A$ and logarithm projectile energy $T_{lab}$.Comment: 5 pages, 2 figure

Dissipation: The phase-space perspective

We show, through a refinement of the work theorem, that the average dissipation, upon perturbing a Hamiltonian system arbitrarily far out of equilibrium in a transition between two canonical equilibrium states, is exactly given by $= -\Delta F =kT D(\rho\|\widetilde{\rho})= kT$, where $\rho$ and $\widetilde{\rho}$ are the phase space density of the system measured at the same intermediate but otherwise arbitrary point in time, for the forward and backward process. $D(\rho\|\widetilde{\rho})$ is the relative entropy of $\rho$ versus $\widetilde{\rho}$. This result also implies general inequalities, which are significantly more accurate than the second law and include, as a special case, the celebrated Landauer principle on the dissipation involved in irreversible computations.Comment: 4 pages, 3 figures (4 figure files), accepted for PR

Linear Complexity Lossy Compressor for Binary Redundant Memoryless Sources

A lossy compression algorithm for binary redundant memoryless sources is presented. The proposed scheme is based on sparse graph codes. By introducing a nonlinear function, redundant memoryless sequences can be compressed. We propose a linear complexity compressor based on the extended belief propagation, into which an inertia term is heuristically introduced, and show that it has near-optimal performance for moderate block lengths.Comment: 4 pages, 1 figur

Parallel Recursive State Compression for Free

This paper focuses on reducing memory usage in enumerative model checking, while maintaining the multi-core scalability obtained in earlier work. We present a tree-based multi-core compression method, which works by leveraging sharing among sub-vectors of state vectors. An algorithmic analysis of both worst-case and optimal compression ratios shows the potential to compress even large states to a small constant on average (8 bytes). Our experiments demonstrate that this holds up in practice: the median compression ratio of 279 measured experiments is within 17% of the optimum for tree compression, and five times better than the median compression ratio of SPIN's COLLAPSE compression. Our algorithms are implemented in the LTSmin tool, and our experiments show that for model checking, multi-core tree compression pays its own way: it comes virtually without overhead compared to the fastest hash table-based methods.Comment: 19 page

Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios.Comment: This revised version fixes two small typos in the published versio