A universal quantum estimator

Almost all computational tasks in the modem computer can be designed from basic building blocks. These building blocks provide a powerful and efficient language for describing algorithms. In quantum computers, the basic building blocks are the quantum gates. In this tutorial, we will look at quantum gates that act on one and two qubits and briefly discuss how these gates can be used in quantum networks

LiPISC: A Lightweight and Flexible Method for Privacy-Aware Intersection Set Computation

Privacy-aware intersection set computation (PISC) can be modeled as secure multi-party computation. The basic idea is to compute the intersection of input sets without leaking privacy. Furthermore, PISC should be sufficiently flexible to recommend approximate intersection items. In this paper, we reveal two previously unpublished attacks against PISC, which can be used to reveal and link one input set to another input set, resulting in privacy leakage. We coin these as Set Linkage Attack and Set Reveal Attack. We then present a lightweight and flexible PISC scheme (LiPISC) and prove its security (including against Set Linkage Attack and Set Reveal Attack)

Chemically Cross-Linked Graphene Oxide as a Selective Layer on Electrospun Polyvinyl Alcohol Nanofiber Membrane for Nanofiltration Application.

Graphene oxide (GO) nanosheets were utilized as a selective layer on a highly porous polyvinyl alcohol (PVA) nanofiber support via a pressure-assisted self-assembly technique to synthesize composite nanofiltration membranes. The GO layer was rendered stable by cross-linking the nanosheets (GO-to-GO) and by linking them onto the support surface (GO-to-PVA) using glutaraldehyde (GA). The amounts of GO and GA deposited on the PVA substrate were varied to determine the optimum nanofiltration membrane both in terms of water flux and salt rejection performances. The successful GA cross-linking of GO interlayers and GO-PVA via acetalization was confirmed by FTIR and XPS analyses, which corroborated with other characterization results from contact angle and zeta potential measurements. Morphologies of the most effective membrane (CGOPVA-50) featured a defect-free GA cross-linked GO layer with a thickness of ~67 nm. The best solute rejections of the CGOPVA-50 membrane were 91.01% for Na2SO4 (20 mM), 98.12% for Eosin Y (10 mg/L), 76.92% for Methylene blue (10 mg/L), and 49.62% for NaCl (20 mM). These findings may provide one of the promising approaches in synthesizing mechanically stable GO-based thin-film composite membranes that are effective for solute separation via nanofiltration

Ab-initio quantum chemistry with neural-network wavefunctions

Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also play an increasingly important role in scientific discovery. A key application of machine learning in the molecular sciences is to learn potential energy surfaces or force fields from ab-initio solutions of the electronic Schr\"odinger equation using datasets obtained with density functional theory, coupled cluster, or other quantum chemistry methods. Here we review a recent and complementary approach: using machine learning to aid the direct solution of quantum chemistry problems from first principles. Specifically, we focus on quantum Monte Carlo (QMC) methods that use neural network ansatz functions in order to solve the electronic Schr\"odinger equation, both in first and second quantization, computing ground and excited states, and generalizing over multiple nuclear configurations. Compared to existing quantum chemistry methods, these new deep QMC methods have the potential to generate highly accurate solutions of the Schr\"odinger equation at relatively modest computational cost.Comment: review, 17 pages, 6 figure

Effects of Escherichia coli vaccination in gilts on piglet performance in a farm in Perak

This study aimed to observe the effects of Escherichia coli (Neocoliporvaccine – Merial) vaccination on diarrhoea percentages, growth parameters (average weight per piglet and average daily gain) and mortality rate in new-born piglets. A field trial was conducted in 35 litters of piglets from gilts selected from a farm in Perak. They were randomly allocated into Treatment (16 litters from E. coli vaccinated gilts) and Control (19 litters) groups respectively. Body weights of the piglets were measured at days 1, 7, 14 and 21 of age and the episodes of diarrhoea and piglet mortality were monitored daily for each pen. The Treatment group had significantly lower Day 1 neonatal diarrhoea percentage (p 0.05)in the overall diarrhoea percentages (1 - 14 days) and weekly growth parameters between both groups. Environmental stress and inevitable routine treatment of diarrhoea with antimicrobials within the farm may have affected the significance of the diarrhoea percentages and growth parameters in this study. In conclusion, E. coli vaccination in gilts was shown to significantly reduce piglet mortality from Day 1 to Day 7 and neonatal diarrhoeal percentageson1-day-old piglets under typical farm conditions in this pilot study in Malaysia

A performance comparison of the contiguous allocation strategies in 3D mesh connected multicomputers

The performance of contiguous allocation strategies can be significantly affected by the distribution of job execution times. In this paper, the performance of the existing contiguous allocation strategies for 3D mesh multicomputers is re-visited in the context of heavy-tailed distributions (e.g., a Bounded Pareto distribution). The strategies are evaluated and compared using simulation experiments for both First-Come-First-Served (FCFS) and Shortest-Service-Demand (SSD) scheduling strategies under a variety of system loads and system sizes. The results show that the performance of the allocation strategies degrades considerably when job execution times follow a heavy-tailed distribution. Moreover, SSD copes much better than FCFS scheduling strategy in the presence of heavy-tailed job execution times. The results also show that the strategies that depend on a list of allocated sub-meshes for both allocation and deallocation have lower allocation overhead and deliver good system performance in terms of average turnaround time and mean system utilization

User Manual for Beta Version of TURBO-GRD: A Software System for Interactive Two-Dimensional Boundary/ Field Grid Generation, Modification, and Refinement

TURBO-GRD is a software system for interactive two-dimensional boundary/field grid generation. modification, and refinement. Its features allow users to explicitly control grid quality locally and globally. The grid control can be achieved interactively by using control points that the user picks and moves on the workstation monitor or by direct stretching and refining. The techniques used in the code are the control point form of algebraic grid generation, a damped cubic spline for edge meshing and parametric mapping between physical and computational domains. It also performs elliptic grid smoothing and free-form boundary control for boundary geometry manipulation. Internal block boundaries are constructed and shaped by using Bezier curve. Because TURBO-GRD is a highly interactive code, users can read in an initial solution, display its solution contour in the background of the grid and control net, and exercise grid modification using the solution contour as a guide. This process can be called an interactive solution-adaptive grid generation