Quantum Field Theory of Forward Rates with Stochastic Volatility

In a recent formulation of a quantum field theory of forward rates, the volatility of the forward rates was taken to be deterministic. The field theory of the forward rates is generalized to the case of stochastic volatility. Two cases are analyzed, firstly when volatility is taken to be a function of the forward rates, and secondly when volatility is taken to be an independent quantum field. Since volatiltiy is a positive valued quantum field, the full theory turns out to be an interacting nonlinear quantum field theory in two dimensions. The state space and Hamiltonian for the interacting theory are obtained, and shown to have a nontrivial structure due to the manifold moving with a constant velocity. The no arbitrage condition is reformulated in terms of the Hamiltonian of the system, and then exactly solved for the nonlinear interacting case.Comment: 7 Figure

Numerical modeling of dynamic powder compaction using the Kawakita equation of state

Dynamic powder compaction is analyzed using the assumption that the powder behaves, while it is being compacted, like a hydrodynamic fluid in which deviatoric stress and heat conduction effects can be ignored throughout the process. This enables techniques of computational fluid dynamics such the equilibrium flux method to be used as a modeling tool. The equation of state of the powder under compression is assumed to be a modified version of the Kawakita loading curve. Computer simulations using this model are performed for conditions matching as closely as possible with those from experiments by Page and Killen [Powder Metall. 30, 233 (1987)]. The numerical and experimental results are compared and a surprising degree of qualitative agreement is observed

Statistical Arbitrage Mining for Display Advertising

We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A/B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.Comment: In the proceedings of the 21st ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2015

Pengembangan Buku Ajar Matakuliah Biologi Sel dengan Pendekatan Bioinformatika untuk Mahasiswa S1 Pendidikan Biologi Universitas Negeri Malang

The purpose of this research is to produce research based textbook on Cell Biology of by using bioinformatics approaches. This study adapted the ADDIE development model. Feasibility of the textbook is based on the results of product testing by experts and small groups. Types of data collected is quantitative and qualitative data. The collected data have analyzed by quantitative and qualitative descriptive. The tests conducted by subject matter experts gave validity percentage of 93.15 %, an expert assessment of the media at 88.64 %, while a small group votes for 87.5 %. Based on the validation results, the textbook that has been developed is very valid and feasible to be used.Tujuan penelitian ini adalah menghasilkan buku ajar Biologi SEl dengan pendekatan Bioinformatika. Penelitian ini mengadaptasi model pengembangan ADDIE. Kelayakan buku ajar didasarkan pada hasil uji coba produk oleh tim ahli dan kelompok kecil. Jenis data yang dikumpulkan adalah data kuantitatif dan data kualitatif. Data yang telah terkumpul dianalisis secara deskriptif kuantitatif dan kualitatif. Hasil validasi yang dilakukan oleh ahli materi memberikan nilai persentase kevalidan buku ajar sebesar 93.15%, penilaian dari ahli media sebesar 88.64%. sedangkan penilaian uji perorangan sebesar 87.5%. Berdasarkan hasil validasi maka buku ajar yang dikembangkan dikategorikan sangat valid dan layak untuk dipergunakan

A scalable readout system for a superconducting adiabatic quantum optimization system

We have designed, fabricated and tested an XY-addressable readout system that is specifically tailored for the reading of superconducting flux qubits in an integrated circuit that could enable adiabatic quantum optimization. In such a system, the flux qubits only need to be read at the end of an adiabatic evolution when quantum mechanical tunneling has been suppressed, thus simplifying many aspects of the readout process. The readout architecture for an $N$-qubit adiabatic quantum optimization system comprises $N$ hysteretic dc SQUIDs and $N$ rf SQUID latches controlled by $2\sqrt{N} + 2$ bias lines. The latching elements are coupled to the qubits and the dc SQUIDs are then coupled to the latching elements. This readout scheme provides two key advantages: First, the latching elements provide exceptional flux sensitivity that significantly exceeds what may be achieved by directly coupling the flux qubits to the dc SQUIDs using a practical mutual inductance. Second, the states of the latching elements are robust against the influence of ac currents generated by the switching of the hysteretic dc SQUIDs, thus allowing one to interrogate the latching elements repeatedly so as to mitigate the effects of stochastic switching of the dc SQUIDs. We demonstrate that it is possible to achieve single qubit read error rates of $<10^{-6}$ with this readout scheme. We have characterized the system-level performance of a 128-qubit readout system and have measured a readout error probability of $8\times10^{-5}$ in the presence of optimal latching element bias conditions.Comment: Updated for clarity, final versio

Managing Risk of Bidding in Display Advertising

In this paper, we deal with the uncertainty of bidding for display advertising. Similar to the financial market trading, real-time bidding (RTB) based display advertising employs an auction mechanism to automate the impression level media buying; and running a campaign is no different than an investment of acquiring new customers in return for obtaining additional converted sales. Thus, how to optimally bid on an ad impression to drive the profit and return-on-investment becomes essential. However, the large randomness of the user behaviors and the cost uncertainty caused by the auction competition may result in a significant risk from the campaign performance estimation. In this paper, we explicitly model the uncertainty of user click-through rate estimation and auction competition to capture the risk. We borrow an idea from finance and derive the value at risk for each ad display opportunity. Our formulation results in two risk-aware bidding strategies that penalize risky ad impressions and focus more on the ones with higher expected return and lower risk. The empirical study on real-world data demonstrates the effectiveness of our proposed risk-aware bidding strategies: yielding profit gains of 15.4% in offline experiments and up to 17.5% in an online A/B test on a commercial RTB platform over the widely applied bidding strategies

Effective "Penetration Depth" in the Vortex State of a d-wave Superconductor

The temperature and field dependence of the effective magnetic penetration depth in the vortex state of a d-wave superconductor, as measured by muon spin rotation experiments, is calculated using a nonlocal London model. We show that at temperatures below T^* \propto \sqrt{B}, the linear T-dependence of the effective penetration depth crosses over to a T^3-dependence. This could provide an explanation for the low temperature flattening of the effective penetration depth curves observed in a recent muon spin rotation experiment.Comment: 4 pages, RevTex, 3 Postscript figure