Education in Singapore: for what, and for whom?

Singapore’s education system has been the focus of intense international interest for the past two decades thanks to its students’ repeated successes in cross-national tests of educational achievement such as PISA. The system has been hailed as a model worthy of emulation by countries eager to reform what many governments perceive to be schools that are failing to foster high achievement standards. Could it be possible that Singapore’s story offers valuable lessons for the path to educational..

Optimal Qubit Mapping with Simultaneous Gate Absorption

Before quantum error correction (QEC) is achieved, quantum computers focus on noisy intermediate-scale quantum (NISQ) applications. Compared to the well-known quantum algorithms requiring QEC, like Shor's or Grover's algorithm, NISQ applications have different structures and properties to exploit in compilation. A key step in compilation is mapping the qubits in the program to physical qubits on a given quantum computer, which has been shown to be an NP-hard problem. In this paper, we present OLSQ-GA, an optimal qubit mapper with a key feature of simultaneous SWAP gate absorption during qubit mapping, which we show to be a very effective optimization technique for NISQ applications. For the class of quantum approximate optimization algorithm (QAOA), an important NISQ application, OLSQ-GA reduces depth by up to 50.0% and SWAP count by 100% compared to other state-of-the-art methods, which translates to 55.9% fidelity improvement. The solution optimality of OLSQ-GA is achieved by the exact SMT formulation. For better scalability, we augment our approach with additional constraints in the form of initial mapping or alternating matching, which speeds up OLSQ-GA by up to 272X with no or little loss of optimality.Comment: 8 pages, 8 figures, to appear in ICCAD'2

Optimal Layout Synthesis for Quantum Computing

Recent years have witnessed the fast development of quantum computing. Researchers around the world are eager to run larger and larger quantum algorithms that promise speedups impossible to any classical algorithm. However, the available quantum computers are still volatile and error-prone. Thus, layout synthesis, which transforms quantum programs to meet these hardware limitations, is a crucial step in the realization of quantum computing. In this paper, we present two synthesizers, one optimal and one approximate but nearly optimal. Although a few optimal approaches to this problem have been published, our optimal synthesizer explores a larger solution space, thus is optimal in a stronger sense. In addition, it reduces time and space complexity exponentially compared to some leading optimal approaches. The key to this success is a more efficient spacetime-based variable encoding of the layout synthesis problem as a mathematical programming problem. By slightly changing our formulation, we arrive at an approximate synthesizer that is even more efficient and outperforms some leading heuristic approaches, in terms of additional gate cost, by up to 100%, and also fidelity by up to 10x on a comprehensive set of benchmark programs and architectures. For a specific family of quantum programs named QAOA, which is deemed to be a promising application for near-term quantum computers, we further adjust the approximate synthesizer by taking commutation into consideration, achieving up to 75% reduction in depth and up to 65% reduction in additional cost compared to the tool used in a leading QAOA study.Comment: to appear in ICCAD'2

Evaluations and Modelling of Residual Stress of a joining- Sialon to Austenitic Stainless Steel

It is not easy to join ceramic to metal due to the differences in the coefficient of thermal expansion of the two materials. The residual stress present has caused failure to the joining. Materials with a relatively low elastic modulus can accommodate strain and will tend to deform under the influence of this stress, while brittle materials such as glasses and ceramics, will have a tendency to fracture. The evaluations and modelling of residual stress of a joining-sialon to austenitic stainless steel was simulated using Finite Element Analysis (ANSYS 1 0) software and simple analytical model was used to evaluate the residual stress. The joining process was assumed as direct diffusion bonding. The stress contour plot was discuss based on failure criteria. It is found that at the area nearby the joining interface, stainless steel experiences tensil~ stress while ceramic experiences compressive stress. The stress intensity is the highest at a few points at the ceramic interface compared to the steel interface. Crack occurred at these points due to the mismatch of thermal expansion and the inability of ceramic to withstand the high concentration of tensile stress

Multiwavelength Polarimetric Lidar for Foliage Obscured Man-Made Target Detection

Non

Operating theatre time, where does it all go? A prospective observational study

Objective To assess the accuracy of surgeons and anaesthetists in predicting the time it will take them to complete an operation or procedure and therefore explain some of the difficulties encountered in operating theatre scheduling. Design Single centre, prospective observational study. Setting Plastic, orthopaedic, and general surgical operating theatres at a level 1 trauma centre serving a population of about 370 000. Participants 92 operating theatre staff including surgical consultants, surgical registrars, anaesthetic consultants, and anaesthetic registrars. Intervention Participants were asked how long they thought their procedure would take. These data were compared with actual time data recorded at the end of the case. Primary outcome measure Absolute difference between predicted and actual time. Results General surgeons underestimated the time required for the procedure by 31 minutes (95% confidence interval 7.6 to 54.4), meaning that procedures took, on average, 28.7% longer than predicted. Plastic surgeons underestimated by 5 minutes (−12.4 to 22.4), with procedures taking an average of 4.5% longer than predicted. Orthopaedic surgeons overestimated by 1 minute (−16.4 to 14.0), with procedures taking an average of 1.1% less time than predicted. Anaesthetists underestimated by 35 minutes (21.7 to 48.7), meaning that, on average, procedures took 167.5% longer than they predicted. The four specialty mean time overestimations or underestimations are significantly different from each other (P=0.01). The observed time differences between anaesthetists and both orthopaedic and plastic surgeons are significantly different (P<0.05), but the time difference between anaesthetists and general surgeons is not significantly different. Conclusion The inability of clinicians to predict the necessary time for a procedure is a significant cause of delay in the operating theatre. This study suggests that anaesthetists are the most inaccurate and highlights the potential differences between specialties in what is considered part of the “anaesthesia time.

Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks

It is desirable to train convolutional networks (CNNs) to run more efficiently during inference. In many cases however, the computational budget that the system has for inference cannot be known beforehand during training, or the inference budget is dependent on the changing real-time resource availability. Thus, it is inadequate to train just inference-efficient CNNs, whose inference costs are not adjustable and cannot adapt to varied inference budgets. We propose a novel approach for cost-adjustable inference in CNNs - Stochastic Downsampling Point (SDPoint). During training, SDPoint applies feature map downsampling to a random point in the layer hierarchy, with a random downsampling ratio. The different stochastic downsampling configurations known as SDPoint instances (of the same model) have computational costs different from each other, while being trained to minimize the same prediction loss. Sharing network parameters across different instances provides significant regularization boost. During inference, one may handpick a SDPoint instance that best fits the inference budget. The effectiveness of SDPoint, as both a cost-adjustable inference approach and a regularizer, is validated through extensive experiments on image classification