Bandit-based Random Mutation Hill-Climbing

The Random Mutation Hill-Climbing algorithm is a direct search technique mostly used in discrete domains. It repeats the process of randomly selecting a neighbour of a best-so-far solution and accepts the neighbour if it is better than or equal to it. In this work, we propose to use a novel method to select the neighbour solution using a set of independent multi-armed bandit-style selection units which results in a bandit-based Random Mutation Hill-Climbing algorithm. The new algorithm significantly outperforms Random Mutation Hill-Climbing in both OneMax (in noise-free and noisy cases) and Royal Road problems (in the noise-free case). The algorithm shows particular promise for discrete optimisation problems where each fitness evaluation is expensive

Evolving Game Skill-Depth using General Video Game AI agents

Most games have, or can be generalised to have, a number of parameters that may be varied in order to provide instances of games that lead to very different player experiences. The space of possible parameter settings can be seen as a search space, and we can therefore use a Random Mutation Hill Climbing algorithm or other search methods to find the parameter settings that induce the best games. One of the hardest parts of this approach is defining a suitable fitness function. In this paper we explore the possibility of using one of a growing set of General Video Game AI agents to perform automatic play-testing. This enables a very general approach to game evaluation based on estimating the skill-depth of a game. Agent-based play-testing is computationally expensive, so we compare two simple but efficient optimisation algorithms: the Random Mutation Hill-Climber and the Multi-Armed Bandit Random Mutation Hill-Climber. For the test game we use a space-battle game in order to provide a suitable balance between simulation speed and potential skill-depth. Results show that both algorithms are able to rapidly evolve game versions with significant skill-depth, but that choosing a suitable resampling number is essential in order to combat the effects of noise

Nanophotonic soliton-based microwave synthesizers

Microwave photonic technologies, which upshift the carrier into the optical domain to facilitate the generation and processing of ultrawide-band electronic signals at vastly reduced fractional bandwidths, have the potential to achieve superior performance compared to conventional electronics for targeted functions. For microwave photonic applications such as filters, coherent radars, subnoise detection, optical communications and low-noise microwave generation, frequency combs are key building blocks. By virtue of soliton microcombs, frequency combs can now be built using CMOS compatible photonic integrated circuits, operated with low power and noise, and have already been employed in system-level demonstrations. Yet, currently developed photonic integrated microcombs all operate with repetition rates significantly beyond those that conventional electronics can detect and process, compounding their use in microwave photonics. Here we demonstrate integrated soliton microcombs operating in two widely employed microwave bands, X- and K-band. These devices can produce more than 300 comb lines within the 3-dB-bandwidth, and generate microwave signals featuring phase noise levels below 105 dBc/Hz (140 dBc/Hz) at 10 kHz (1 MHz) offset frequency, comparable to modern electronic microwave synthesizers. In addition, the soliton pulse stream can be injection-locked to a microwave signal, enabling actuator-free repetition rate stabilization, tuning and microwave spectral purification, at power levels compatible with silicon-based lasers (<150 mW). Our results establish photonic integrated soliton microcombs as viable integrated low-noise microwave synthesizers. Further, the low repetition rates are critical for future dense WDM channel generation schemes, and can significantly reduce the system complexity of photonic integrated frequency synthesizers and atomic clocks

"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output

Large language models can produce creative and diverse responses. However, to integrate them into current developer workflows, it is essential to constrain their outputs to follow specific formats or standards. In this work, we surveyed 51 experienced industry professionals to understand the range of scenarios and motivations driving the need for output constraints from a user-centered perspective. We identified 134 concrete use cases for constraints at two levels: low-level, which ensures the output adhere to a structured format and an appropriate length, and high-level, which requires the output to follow semantic and stylistic guidelines without hallucination. Critically, applying output constraints could not only streamline the currently repetitive process of developing, testing, and integrating LLM prompts for developers, but also enhance the user experience of LLM-powered features and applications. We conclude with a discussion on user preferences and needs towards articulating intended constraints for LLMs, alongside an initial design for a constraint prototyping tool

Keeping a Single Qubit Alive by Experimental Dynamic Decoupling

We demonstrate the use of dynamic decoupling techniques to extend the coherence time of a single memory qubit by nearly two orders of magnitude. By extending the Hahn spin-echo technique to correct for unknown, arbitrary polynomial variations in the qubit precession frequency, we show analytically that the required sequence of pi-pulses is identical to the Uhrig dynamic decoupling (UDD) sequence. We compare UDD and CPMG sequences applied to a single Ca-43 trapped-ion qubit and find that they afford comparable protection in our ambient noise environment.Comment: 5 pages, 5 figure

Non-Parametric Learning for Monocular Visual Odometry

This thesis addresses the problem of incremental localization from visual information, a scenario commonly known as visual odometry. Current visual odometry algorithms are heavily dependent on camera calibration, using a pre-established geometric model to provide the transformation between input (optical flow estimates) and output (vehicle motion estimates) information. A novel approach to visual odometry is proposed in this thesis where the need for camera calibration, or even for a geometric model, is circumvented by the use of machine learning principles and techniques. A non-parametric Bayesian regression technique, the Gaussian Process (GP), is used to elect the most probable transformation function hypothesis from input to output, based on training data collected prior and during navigation. Other than eliminating the need for a geometric model and traditional camera calibration, this approach also allows for scale recovery even in a monocular configuration, and provides a natural treatment of uncertainties due to the probabilistic nature of GPs. Several extensions to the traditional GP framework are introduced and discussed in depth, and they constitute the core of the contributions of this thesis to the machine learning and robotics community. The proposed framework is tested in a wide variety of scenarios, ranging from urban and off-road ground vehicles to unconstrained 3D unmanned aircrafts. The results show a significant improvement over traditional visual odometry algorithms, and also surpass results obtained using other sensors, such as laser scanners and IMUs. The incorporation of these results to a SLAM scenario, using a Exact Sparse Information Filter (ESIF), is shown to decrease global uncertainty by exploiting revisited areas of the environment. Finally, a technique for the automatic segmentation of dynamic objects is presented, as a way to increase the robustness of image information and further improve visual odometry results

Massively parallel coherent laser ranging using soliton microcombs

Coherent ranging, also known as frequency-modulated continuous-wave (FMCW) laser based ranging (LIDAR) is currently developed for long range 3D distance and velocimetry in autonomous driving. Its principle is based on mapping distance to frequency, and to simultaneously measure the Doppler shift of reflected light using frequency chirped signals, similar to Sonar or Radar. Yet, despite these advantages, coherent ranging exhibits lower acquisition speed and requires precisely chirped and highly-coherent laser sources, hindering their widespread use and impeding Parallelization, compared to modern time-of-flight (TOF) ranging that use arrays of individual lasers. Here we demonstrate a novel massively parallel coherent LIDAR scheme using a photonic chip-based microcomb. By fast chirping the pump laser in the soliton existence range of a microcomb with amplitudes up to several GHz and sweep rate up to 10 MHz, the soliton pulse stream acquires a rapid change in the underlying carrier waveform, while retaining its pulse-to-pulse repetition rate. As a result, the chirp from a single narrow-linewidth pump laser is simultaneously transferred to all spectral comb teeth of the soliton at once, and allows for true parallelism in FMCW LIDAR. We demonstrate this approach by generating 30 distinct channels, demonstrating both parallel distance and velocity measurements at an equivalent rate of 3 Mpixel/s, with potential to improve sampling rates beyond 150 Mpixel/s and increase the image refresh rate of FMCW LIDAR up to two orders of magnitude without deterioration of eye safety. The present approach, when combined with photonic phase arrays based on nanophotonic gratings, provides a technological basis for compact, massively parallel and ultra-high frame rate coherent LIDAR systems.Comment: 18 pages, 12 Figure

Hopf bifurcations in time-delay systems with band-limited feedback

We investigate the steady-state solution and its bifurcations in time-delay systems with band-limited feedback. This is a first step in a rigorous study concerning the effects of AC-coupled components in nonlinear devices with time-delayed feedback. We show that the steady state is globally stable for small feedback gain and that local stability is lost, generically, through a Hopf bifurcation for larger feedback gain. We provide simple criteria that determine whether the Hopf bifurcation is supercritical or subcritical based on the knowledge of the first three terms in the Taylor-expansion of the nonlinearity. Furthermore, the presence of double-Hopf bifurcations of the steady state is shown, which indicates possible quasiperiodic and chaotic dynamics in these systems. As a result of this investigation, we find that AC-coupling introduces fundamental differences to systems of Ikeda-type [Ikeda et al., Physica D 29 (1987) 223-235] already at the level of steady-state bifurcations, e.g. bifurcations exist in which limit cycles are created with periods other than the fundamental ``period-2'' mode found in Ikeda-type systems.Comment: 32 pages, 5 figures, accepted for publication in Physica D: Nonlinear Phenomen