Shadows of Relic Neutrino Masses and Spectra on Highest Energy GZK Cosmic Rays

The Ultra High Energy (UHE) neutrino scattering onto relic cosmic neutrinos in galactic and local halos offers an unique way to overcome GZK cut-off. The UHE nu secondary of UHE photo-pion decays may escape the GZK cut-off and travel on cosmic distances hitting local light relic neutrinos clustered in dark halos. The Z resonant production and the competitive W^+W^-, ZZ pair production define a characteristic imprint on hadronic consequent UHECR spectra. This imprint keeps memory both of the primary UHE nu spectra as well as of the possible relic neutrino masses values, energy spectra and relic densities. Such an hadronic showering imprint should reflect into spectra morphology of cosmic rays near and above GZK 10^{19}-10^{21}eV cut-off energies. A possible neutrino degenerate masses at eVs or a more complex and significant neutrino mass split below or near Super-Kamiokande \triangle m_{\nu_{SK}}= 0.1 eV masses might be reflected after each corresponding Z peak showering, into new twin unexpected UHECR flux modulation behind GZK energies: E_{p} sim 3(frac{triangle m_{\nu_{SK}}}/m_{\nu}10^{21}),eV. Other shadowsof lightest, nearly massless, neutrinos m_{nu_{2K} simeq 0.001eV simeq kT_{\nu}, their lowest relic temperatures, energies and densities might be also reflected at even higher energies edges near Grand Unification: E_{p} \sim 2.2(m_{\nu_{2K}/E_{\nu}})10^{23}, eV .Comment: 14 pages, 6 Figures,Invited Talk Heidelberg DARK 200

On the Learning Behavior of Adaptive Networks - Part I: Transient Analysis

This work carries out a detailed transient analysis of the learning behavior of multi-agent networks, and reveals interesting results about the learning abilities of distributed strategies. Among other results, the analysis reveals how combination policies influence the learning process of networked agents, and how these policies can steer the convergence point towards any of many possible Pareto optimal solutions. The results also establish that the learning process of an adaptive network undergoes three (rather than two) well-defined stages of evolution with distinctive convergence rates during the first two stages, while attaining a finite mean-square-error (MSE) level in the last stage. The analysis reveals what aspects of the network topology influence performance directly and suggests design procedures that can optimize performance by adjusting the relevant topology parameters. Interestingly, it is further shown that, in the adaptation regime, each agent in a sparsely connected network is able to achieve the same performance level as that of a centralized stochastic-gradient strategy even for left-stochastic combination strategies. These results lead to a deeper understanding and useful insights on the convergence behavior of coupled distributed learners. The results also lead to effective design mechanisms to help diffuse information more thoroughly over networks.Comment: to appear in IEEE Transactions on Information Theory, 201

Adore: Differentially Oblivious Relational Database Operators

There has been a recent effort in applying differential privacy on memory access patterns to enhance data privacy. This is called differential obliviousness. Differential obliviousness is a promising direction because it provides a principled trade-off between performance and desired level of privacy. To date, it is still an open question whether differential obliviousness can speed up database processing with respect to full obliviousness. In this paper, we present the design and implementation of three new major database operators: selection with projection, grouping with aggregation, and foreign key join. We prove that they satisfy the notion of differential obliviousness. Our differentially oblivious operators have reduced cache complexity, runtime complexity, and output size compared to their state-of-the-art fully oblivious counterparts. We also demonstrate that our implementation of these differentially oblivious operators can outperform their state-of-the-art fully oblivious counterparts by up to $7.4\times$.Comment: VLDB 202

Computational Study of Ionic Polymers: Multiscale Stiffness Predictions and Modeling of the Electromechanical Transduction

Ionic polymer transducers (IPTs) represent a relatively new class of active (¡®smart¡¯) materials, which can function as highly sensitive mechanical sensors as well as actuators. An IPT is made of an ionic polymer membrane sandwiched between two conductive electrodes. They generate controllable strain when applying a low voltage (<5 V) across their thickness and generate measurable currents due to extremely small mechanical strain. IPTs are cost effective and often have superior sensing capabilities compared to other active materials such as piezoelectrics. However, this novel class of transducers has not been widely employed mainly because the mechanism of IPT sensing is not clearly understood. In this dissertation, the mechanical properties of ionic polymers, the ionomer morphology, and the fundamental mechanism responsible for the electromechanical sensing responses of IPTs are studied. A multiscale model for the prediction of material stiffness is presented. The results give access to a fundamental material parameters currently inaccessible via experimentation, namely local stiffness. Subsequently the sensing mechanism of stream potential is hypothesized. It is argued that the mechanism of streaming potential, unlike prior hypotheses, is able to systematically explain generalized experimentally observed sensing phenomena, such as the observation of an optimum conductive particulate volume fraction in the interpenetrating electrode region of the transducer. Moreover, it is argued that coupling the exploration of local stiffness and streaming potential is prerequisite to gaining insight into subtler experimental sensing phenomena such as experimentally observed variations in sensing due to variations in IPT architecture

Practical Volume-Based Attacks on Encrypted Databases

Recent years have seen an increased interest towards strong security primitives for encrypted databases (such as oblivious protocols), that hide the access patterns of query execution, and reveal only the volume of results. However, recent work has shown that even volume leakage can enable the reconstruction of entire columns in the database. Yet, existing attacks rely on a set of assumptions that are unrealistic in practice: for example, they (i) require a large number of queries to be issued by the user, or (ii) assume certain distributions on the queries or underlying data (e.g., that the queries are distributed uniformly at random, or that the database does not contain missing values). In this work, we present new attacks for recovering the content of individual user queries, assuming no leakage from the system except the number of results and avoiding the limiting assumptions above. Unlike prior attacks, our attacks require only a single query to be issued by the user for recovering the keyword. Furthermore, our attacks make no assumptions about the distribution of issued queries or the underlying data. Instead, our key insight is to exploit the behavior of real-world applications. We start by surveying 11 applications to identify two key characteristics that can be exploited by attackers: (i) file injection, and (ii) automatic query replay. We present attacks that leverage these two properties in concert with volume leakage, independent of the details of any encrypted database system. Subsequently, we perform an attack on the real Gmail web client by simulating a server-side adversary. Our attack on Gmail completes within a matter of minutes, demonstrating the feasibility of our techniques. We also present three ancillary attacks for situations when certain mitigation strategies are employed.Comment: IEEE EuroS&P 202