Imaging-based Parametric Resonance in an Optical Dipole Atom Trap

We report sensitive detection of parametric resonances in a high-density sample of ultracold $^{87}Rb$ atoms confined to a far-off-resonance optical dipole trap. Fluorescence imaging of the expanded ultracold atom cloud after a period of parametric excitation shows significant modification of the atomic spatial distribution and has high sensitivity compared with traditional measurements of parametrically-driven trap loss. Using this approach, a significant shift of the parametric resonance frequency is observed, and attributed to the anharmonic shape of the dipole trap potential

Group-blind detection with very large antenna arrays in the presence of pilot contamination

Massive MIMO is, in general, severely affected by pilot contamination. As opposed to traditional detectors, we propose a group-blind detector that takes into account the presence of pilot contamination. While sticking to the traditional structure of the training phase, where orthogonal pilot sequences are reused, we use the excess antennas at each base station to partially remove interference during the uplink data transmission phase. We analytically derive the asymptotic SINR achievable with group-blind detection, and confirm our findings by simulations. We show, in particular, that in an interference-limited scenario with one dominant interfering cell, the SINR can be doubled compared to non-group-blind detection.Comment: 5 pages, 4 figure

Structured Near-Optimal Channel-Adapted Quantum Error Correction

We present a class of numerical algorithms which adapt a quantum error correction scheme to a channel model. Given an encoding and a channel model, it was previously shown that the quantum operation that maximizes the average entanglement fidelity may be calculated by a semidefinite program (SDP), which is a convex optimization. While optimal, this recovery operation is computationally difficult for long codes. Furthermore, the optimal recovery operation has no structure beyond the completely positive trace preserving (CPTP) constraint. We derive methods to generate structured channel-adapted error recovery operations. Specifically, each recovery operation begins with a projective error syndrome measurement. The algorithms to compute the structured recovery operations are more scalable than the SDP and yield recovery operations with an intuitive physical form. Using Lagrange duality, we derive performance bounds to certify near-optimality.Comment: 18 pages, 13 figures Update: typos corrected in Appendi

Human Rights Protection for Indonesian Migrant Workers: Challenges for ASEAN

The AEC is good news for Indonesian migrant workers wanting to work overseas. Unfortunately, many Indonesian migrant workers have been deported from ASEAN countries because of having problems. This study adopts the normative legal research method. It argues that AICHR may be slow in resolving the problems of human rights. It is also argued that the ASEAN Committee on Migrant Workers works in the absence of the political commitment of ASEAN leaders to implement the Cebu Declaration. Therefore, the best solution is public participation in the ASEAN countries to protect migrant workers.IntisariKomunitas Masyarakat Ekonomi ASEAN adalah berita baik untuk Tenaga Kerja Indonesia (TKI) untuk bekerja di luar negeri. Namun, banyak TKI yang kembali dari negara-negara ASEAN dikarenakan mendapatkan berbagai permasalahan. Penelitian ini mengadopsi jenis penelitian hukum normatif. Penelitian ini menyimpulkan bahwa AICHR lamban dalam menyelesaikan permaslahan tentang hak asasi manusia. Penelitian ini juga menyimpulkan bahwa komunitas ASEAN tentang Pekerja Migran bekerja dengan tidak adanya komitmen politik dari para pemimpim ASEAN dalam menerapkan Deklarasi Cebu. Oleh sebab itu, dibutuhkan partisipasi ASEAN dalam melindungi TKI

Spatiotemporal information coupling in network navigation

Network navigation, encompassing both spatial and temporal cooperation to locate mobile agents, is a key enabler for numerous emerging location-based applications. In such cooperative networks, the positional information obtained by each agent is a complex compound due to the interaction among its neighbors. This information coupling may result in poor performance: algorithms that discard information coupling are often inaccurate, and algorithms that keep track of all the neighbors’ interactions are often inefficient. In this paper, we develop a principled framework to characterize the information coupling present in network navigation. Specifically, we derive the equivalent Fisher information matrix for individual agents as the sum of effective information from each neighbor and the coupled information induced by the neighbors’ interaction. We further characterize how coupled information decays with the network distance in representative case studies. The results of this work can offer guidelines for the development of distributed techniques that adequately account for information coupling, and hence enable accurate and efficient network navigation.RYC-2016-1938

Optimum Quantum Error Recovery using Semidefinite Programming

Quantum error correction (QEC) is an essential element of physical quantum information processing systems. Most QEC efforts focus on extending classical error correction schemes to the quantum regime. The input to a noisy system is embedded in a coded subspace, and error recovery is performed via an operation designed to perfectly correct for a set of errors, presumably a large subset of the physical noise process. In this paper, we examine the choice of recovery operation. Rather than seeking perfect correction on a subset of errors, we seek a recovery operation to maximize the entanglement fidelity for a given input state and noise model. In this way, the recovery operation is optimum for the given encoding and noise process. This optimization is shown to be calculable via a semidefinite program (SDP), a well-established form of convex optimization with efficient algorithms for its solution. The error recovery operation may also be interpreted as a combining operation following a quantum spreading channel, thus providing a quantum analogy to the classical diversity combining operation.Comment: 7 pages, 3 figure

Soft range information for network localization

The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional range-based localization approaches rely on distance estimates obtained from measurements (e.g., delay or strength of received waveforms). This paper goes one step further and develops localization techniques that rely on all probable range values rather than on a single estimate of each distance. In particular, the concept of soft range information (SRI) is introduced, showing its essential role for network localization. We then establish a general framework for SRI-based localization and develop algorithms for obtaining the SRI using machine learning techniques. The performance of the proposed approach is quantified via network experimentation in indoor environments. The results show that SRI-based localization techniques can achieve performance approaching the Cramer–Rao lower bound and significantly outperform the conventional techniques especially in harsh wireless environments.RYC-2016-1938