Synthesizing Coupling Proofs of Differential Privacy

Differential privacy has emerged as a promising probabilistic formulation of privacy, generating intense interest within academia and industry. We present a push-button, automated technique for verifying $\varepsilon$-differential privacy of sophisticated randomized algorithms. We make several conceptual, algorithmic, and practical contributions: (i) Inspired by the recent advances on approximate couplings and randomness alignment, we present a new proof technique called coupling strategies, which casts differential privacy proofs as a winning strategy in a game where we have finite privacy resources to expend. (ii) To discover a winning strategy, we present a constraint-based formulation of the problem as a set of Horn modulo couplings (HMC) constraints, a novel combination of first-order Horn clauses and probabilistic constraints. (iii) We present a technique for solving HMC constraints by transforming probabilistic constraints into logical constraints with uninterpreted functions. (iv) Finally, we implement our technique in the FairSquare verifier and provide the first automated privacy proofs for a number of challenging algorithms from the differential privacy literature, including Report Noisy Max, the Exponential Mechanism, and the Sparse Vector Mechanism

Design and implementation of MIMO-long term evolution-advanced to support larger bandwidth

The migration of mobile communication technologies are divided into four generations. Long Term Evolution (LTE) is called LTE rel-8, the evolution of LTE led to new technology referred to as LTE-Advanced, is the true fourth generation (4G) evolution step, with the first release of LTE (rel-8) which was labeled as “3.9G”. LTE-Advanced is a mobile broadband access technology founded as a response to the need for the improvement to support the increasing demand for high data rates. The standard for LTE-A is a milestone in the development of Third Generation Partnership Project (3GPP) technologies. Carrier Aggregation is one of the most distinct features of LTE�Advanced that makes the bandwidth extension of up to 100 MHz thus the theoretical peak data rate of LTE-A may be even up to 1 Gbps. This proposed system presents new LTE-Advanced depending on carrier aggregation to obtain better performance of the system. The new design of LTE-Advanced offers higher peak data rates than even the initial LTE-A; while the spectrum efficiency has been amended; As a result, the aggregated LTE-A will support 120 MHz instead of 100 MHz in order to obtain higher peak data rate access up to 4 Gbps. The system was applied with 8x8 Multiple Input Multiple Output (MIMO) using different modulation techniques: QPSK, 16 QAM, and 64 QAM. From the simulation results, it is clear that proposed LTE-Advanced with 64 QAM has high values of throughput in case of depending code rate equals to 5/6 with 8x8 MIMO

Preparation And Characterization Of Graphene Filled Epoxy Thin Film Nanocomposite For Electronic Applications

Grafin telah menarik perhatian yang besar dalam aplikasi elektronik kerana sifat-sifat mekanikal, elektrik dan haba yang unik kesan dari struktur dua dimensinya. Komposit nano filem nipis epoksi diisi serbuk nano grafin (GNP) telah dihasilkan menggunakan teknik ultrasonik dan salutan mejam. Kesan bebanan GNP dan masa sonikasi, kloroform sebagai larutan penyebaran untuk GNP (ch-GNP), amina sebagai ejen gandingan untuk GNP (m-GNP) dan sistem hibrid menggunakan GNP dan karbon nano tiub berbilang dinding (GNP-MWCNT) terhadap sifat tegangan, elektrik dan terma bagi komposit epoksi telah disiasat dalam kajian ini. Secara umumnya, penambahan GNP telah menurunkan kekuatan tegangan dan modulus untuk kesemua komposit epoksi. Walau bagaimanapun, komposit epoksi/GNP yang dihasilkan menggunakan 20 minit masa sonikasi mempunyai kekuatan tegangan dan modulus yang sedikit tinggi, dengan pecahan isipadu penelusan elektrik ambang yang rendah (0.1%). Kajian morfologi melalui mikroskop imbasan elektron (SEM) menunjukkan bahawa peningkatan pada penyerakan GNP telah dicapai dengan pertambahan masa sonikasi. Walau bagaimanapun, ubahbentuk GNP telah diperhatikan selepas masa sonikasi yang panjang. Kedua-dua komposit epoksi/ch-GNP dan epoksi/m-GNP yang dihasilkan menggunakan 20 minit masa sonikasi mempunyai sifat tegangan yang sedikit tinggi berbanding komposit epoksi/GNP, terutama pada bebanan pengisi yang rendah. Walau bagaimanapun, komposit epoksi/m-GNP menunjukkan nilai penelusan ambang pada bebanan pengisi yang lebih tinggi (isipadu 0.6 %) berbanding isipadunya 0.05 dan 0.1 %, masing-masing untuk komposit epoksi/ch-GNP dan epoksi/GNP. Analisis morfologi melalui SEM menunjukkan tahap penyebaran dan pengelupasan ch-GNP dan m-GNP yang tinggi di dalam komposit epoksi. Walau bagaimanpun, kajian morfologi melalui mikroskop transmisi elektron (TEM) menunjukkan bahawa kedua-dua saiz ch-GNP dan m-GNP adalah jauh lebih kecil berbanding GNP asal yang diperoleh. Dari sifat termal, didapati bahawa komposit epoksi/ch-GNP dan epoksi/m-GNP menunjukkan suhu peralihan kaca yang lebih tinggi berbanding komposit epoksi/GNP. Komposit hibrid epoksi/GNP-MWCNT menunjukkan peningkatan dalam sifat tegangan dengan peningkatan MWCNT berbanding GNP. Komposit hibrid epoksi/GNP-MWCNT dengan nisbah 0.1:0.4 bagi GNP:MWCNT, dengan jumlah pecahan isipadu pengisi nano 0.5 vol% menunjukkan sifat-sifat yang optimum di kalangan semua komposit, malahan sifat tegangan yang lebih tinggi daripada epoksi tulen dengan kekonduksian elektrik setinggi 1.82 x 10-2 S/cm, yang sekali ganda lebih tinggi daripada komposit epoksi dengan isipadu GNP 0.5%. Akhir sekali, kajian ini melibatkan penggunaan dua jenis resin epoksi yang berbeza, dengan menggunakan sistem hibrid GNP-MWCNT ke atas sifat-sifat komposit hibrid epoksi. Berbanding dengan komposit hibrid epoksi-DER (0.1:0.4), komposit hibrid epoksi-Epolam (0.1:0.4) telah menunjukkan sifat tegangan dan termal yang lebih rendah manakala kekonduksian elektrik komposit hibrid kekal hampir dengan penebat tulen matrik epoksi Epolam. ________________________________________________________________________________________________________________________ Graphene has attracted great attention in electronic applications because of the unique mechanical, electrical and thermal properties arising from its two dimensional structure. Graphene nanopowder (GNP) filled epoxy thin film nanocomposites were fabricated using ultrasonication and the spin coating techniques. The effect of GNP loading and sonication time, chloroform as dispersion solution for GNP (ch-GNP), amine as coupling agent for GNP (m-GNP) and hybrid system using GNP and multi-walled carbon nanotubes (MWCNT) (GNP-MWCNT) on the tensile, electrical and thermal properties of epoxy composites were investigated in this study. Generally, the addition of GNP decreased the tensile strength and modulus for all epoxy composites. However, the GNP/epoxy composites produced using 20 minutes of sonication time had a slightly higher tensile strength and modulus, with a lower electrical percolation threshold volume fraction (0.1 vol%). Morphological study via scanning electron microscopy (SEM) showed that improvement in GNP dispersion was achieved with increased sonication time. However, GNP deformation was observed after a long sonication time. Both ch-GNP/epoxy and m-GNP/epoxy composites produced using 20 minutes of sonication time had slightly higher tensile properties than GNP/epoxy composites, especially at low filler loading. However, the m-GNP/epoxy composites exhibit percolation threshold value at higher filler loading (0.6 vol%) compared to 0.05 and 0.1 vol% for ch-GNP/epoxy and GNP/epoxy composite, respectively. Morphological analysis via SEM showed a higher degree of dispersion and exfoliation of ch-GNP and m-GNP in the epoxy composites. However, morphological study via transmission electron microscopy (TEM) showed that both ch-GNP and m-GNP size was far smaller than the as received GNP. From the thermal properties, it was found that ch-GNP/epoxy and m-GNP/epoxy composites showed a higher glass transition temperature than GNP/epoxy composites. The GNP-MWCNT/epoxy hybrid composites showed improvement in the tensile properties with increasing concentration of MWCNT compared to GNP. GNP-MWCNT/epoxy hybrid composite with 0.1:0.4 ratio of GNP:MWCNT with total nanofiller volume fraction of 0.5 vol% showed the optimum properties among all composites even higher tensile properties than the unfilled epoxy with highest electrical conductivity of 1.82 × 10-2 S/cm, which is an order of magnitude higher than that of the epoxy composite with 0.5 vol% GNP. Finally, this study involves the use of two different types of epoxy resin using GNP-MWCNT hybrid system on the properties of epoxy hybrid composite. As compared to epoxy-DER (0.1:0.4) hybrid composites, epoxy-Epolam (0.1:0.4) hybrid composites showed lower tensile and thermal properties while the electrical conductivity of the hybrid composite remains very close to that of the pure insulating epoxy-Epolam matrix

Effect of isoflavones on the vascular actions of phosphodiesterase inhibitors

This journal suppl. entitled: EDHF 2012 - 10th Anniversary MeetingIsoflavones are present in high concentrations in soy products, the intake of which is associated with lower incidence of cardiovascular diseases. They can enhance contraction and inhibit relaxation through cyclic adenosine monophosphate (cAMP)-dependent pathway. The present study aimed to examine whether or not the major soy isoflav...postprin

Distribution Policies for Datalog

Modern data management systems extensively use parallelism to speed up query processing over massive volumes of data. This trend has inspired a rich line of research on how to formally reason about the parallel complexity of join computation. In this paper, we go beyond joins and study the parallel evaluation of recursive queries. We introduce a novel framework to reason about multi-round evaluation of Datalog programs, which combines implicit predicate restriction with distribution policies to allow expressing a combination of data-parallel and query-parallel evaluation strategies. Using our framework, we reason about key properties of distributed Datalog evaluation, including parallel-correctness of the evaluation strategy, disjointness of the computation effort, and bounds on the number of communication rounds