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The optical spectrum of a quantum dot is typically dominated by the fundamental transition between the lowest-energy configurations. However, the radiative Auger process can result in additional red-shifted emission lines. The origin of these lines is a combination of Coulomb interaction and symmetry-breaking in the quantum dot. In this paper, we present measurements of such radiative Auger lines for a range of InGaAs/GaAs self-assembled quantum dots and use a tight-binding model with a configuration interaction approach to explain their appearance. Introducing a composition fluctuation cluster in the dot, our calculations show excellent agreement with measurements. We relate our findings to group theory explaining the origin of the additional emission lines. Our model and results give insight into the interplay between the symmetry breaking in a quantum dot and the position and strength of the radiative Auger lines.

We describe the asymptotic behavior of the conformal modulus of an unbounded doubly-connected domain, non-symmetric with respect to the coordinate axes, when stretched in the direction of the abscissa axis with coefficient $H\to +\infty$. Therefore, we give a partial answer to a problem, suggested by M.~Vourinen, for the case of an arbitrary unbounded domain., Comment: 15 pages, 5 figures

Explaining artificial intelligence (AI) predictions is increasingly important and even imperative in many high-stakes applications where humans are the ultimate decision-makers. In this work, we propose two novel architectures of self-interpretable image classifiers that first explain, and then predict (as opposed to post-hoc explanations) by harnessing the visual correspondences between a query image and exemplars. Our models consistently improve (by 1 to 4 points) on out-of-distribution (OOD) datasets while performing marginally worse (by 1 to 2 points) on in-distribution tests than ResNet-50 and a $k$-nearest neighbor classifier (kNN). Via a large-scale, human study on ImageNet and CUB, our correspondence-based explanations are found to be more useful to users than kNN explanations. Our explanations help users more accurately reject AI's wrong decisions than all other tested methods. Interestingly, for the first time, we show that it is possible to achieve complementary human-AI team accuracy (i.e., that is higher than either AI-alone or human-alone), in ImageNet and CUB image classification tasks., Comment: NeurIPS 2022 conference paper

Firewall policies are an important line of defence in cybersecurity, specifying which packets are allowed to pass through a network and which are not. These firewall policies are made up of a list of interacting rules. In practice, firewall can consist of hundreds or thousands of rules. This can be very difficult for a human to correctly configure. One proposed solution is to model firewall policies as Boolean expressions and use existing computer programs such as SAT solvers to verify that the firewall satisfies certain conditions. This paper takes an in-depth look at the Boolean expressions that represent firewall policies. We present an algorithm that translates a list of firewall rules into a Boolean expression in conjunctive normal form (CNF) or disjunctive normal form (DNF). We also place an upper bound on the size of the CNF and DNF that is polynomial in the number of rules in the firewall policy. This shows that past results suggesting a combinatorial explosion when converting from a Boolean expression in CNF to one in DNF does note occur in the context of firewall analysis

Today deep learning is widely used for building software. A software engineering problem with deep learning is that finding an appropriate convolutional neural network (CNN) model for the task can be a challenge for developers. Recent work on AutoML, more precisely neural architecture search (NAS), embodied by tools like Auto-Keras aims to solve this problem by essentially viewing it as a search problem where the starting point is a default CNN model, and mutation of this CNN model allows exploration of the space of CNN models to find a CNN model that will work best for the problem. These works have had significant success in producing high-accuracy CNN models. There are two problems, however. First, NAS can be very costly, often taking several hours to complete. Second, CNN models produced by NAS can be very complex that makes it harder to understand them and costlier to train them. We propose a novel approach for NAS, where instead of starting from a default CNN model, the initial model is selected from a repository of models extracted from GitHub. The intuition being that developers solving a similar problem may have developed a better starting point compared to the default model. We also analyze common layer patterns of CNN models in the wild to understand changes that the developers make to improve their models. Our approach uses commonly occurring changes as mutation operators in NAS. We have extended Auto-Keras to implement our approach. Our evaluation using 8 top voted problems from Kaggle for tasks including image classification and image regression shows that given the same search time, without loss of accuracy, Manas produces models with 42.9% to 99.6% fewer number of parameters than Auto-Keras' models. Benchmarked on GPU, Manas' models train 30.3% to 641.6% faster than Auto-Keras' models.

Modern power systems are experiencing the challenge of high uncertainty with the increasing penetration of renewable energy resources and the electrification of heating systems. In this paradigm shift, understanding electricity users' demand is of utmost value to retailers, aggregators, and policymakers. However, behind-the-meter (BTM) equipment and appliances at the household level are unknown to the other stakeholders mainly due to privacy concerns and tight regulations. In this paper, we seek to identify residential consumers based on their BTM equipment, mainly rooftop photovoltaic (PV) systems and electric heating, using imported/purchased energy data from utility meters. To solve this problem with an interpretable, fast, secure, and maintainable solution, we propose an integrated method called Interpretable Refined Motifs And binary Classification (IRMAC). The proposed method comprises a novel shape-based pattern extraction technique, called Refined Motif (RM) discovery, and a single-neuron classifier. The first part extracts a sub-pattern from the long time series considering the frequency of occurrences, average dissimilarity, and time dynamics while emphasising specific times with annotated distances. The second part identifies users' types with linear complexity while preserving the transparency of the algorithms. With the real data from Australia and Denmark, the proposed method is tested and verified in identifying PV owners and electrical heating system users., Comment: 22 pages, 13 figures

Photonic quantum technology provides a viable route to quantum communication, quantum simulation, and quantum information processing. Recent progress has seen the realisation of boson sampling using 20 single-photons and quantum key distribution over hundreds of kilometres. Scaling the complexity requires architectures containing multiple photon-sources, photon-counters, and a large number of indistinguishable single photons. Semiconductor quantum dots are bright and fast sources of coherent single-photons. For applications, a significant roadblock is the poor quantum coherence upon interfering single photons created by independent quantum dots. Here, we demonstrate two-photon interference with near-unity visibility ($93.0\pm0.8$)\% using photons from two completely separate GaAs quantum dots. The experiment retains all the emission into the zero-phonon-line -- only the weak phonon-sideband is rejected -- and temporal post-selection is not employed. Exploiting the quantum interference, we demonstrate a photonic controlled-not circuit and an entanglement with fidelity ($85.0\pm 1.0$)\% between photons of different origins. The two-photon interference visibility is high enough that the entanglement fidelity is well above the classical threshold. The high mutual-coherence of the photons stems from high-quality materials, a diode-structure, and the relatively large quantum dot size. Our results establish a platform, GaAs QDs, for creating coherent single photons in a scalable way., Comment: 20 pages, 9 figures, supplementary information included in a separate PDF

Explaining the decisions of an Artificial Intelligence (AI) model is increasingly critical in many real-world, high-stake applications. Hundreds of papers have either proposed new feature attribution methods, discussed or harnessed these tools in their work. However, despite humans being the target end-users, most attribution methods were only evaluated on proxy automatic-evaluation metrics (Zhang et al. 2018; Zhou et al. 2016; Petsiuk et al. 2018). In this paper, we conduct the first user study to measure attribution map effectiveness in assisting humans in ImageNet classification and Stanford Dogs fine-grained classification, and when an image is natural or adversarial (i.e., contains adversarial perturbations). Overall, feature attribution is surprisingly not more effective than showing humans nearest training-set examples. On a harder task of fine-grained dog categorization, presenting attribution maps to humans does not help, but instead hurts the performance of human-AI teams compared to AI alone. Importantly, we found automatic attribution-map evaluation measures to correlate poorly with the actual human-AI team performance. Our findings encourage the community to rigorously test their methods on the downstream human-in-the-loop applications and to rethink the existing evaluation metrics., Comment: NeurIPS 2021; 10 pages of Main text; 28 pages of Appendix

Desiccation cracking in clayey soils occurs when they lose moisture, leading to an increase in their compressibility and hydraulic conductivity and hence significant reduction of soil strength. The prediction of desiccation cracking in soils is challenging due to the lack of insights into the complex coupled hydro-mechanical process at the grain scale. In this paper, a new hybrid discrete-continuum numerical framework, capable of capturing hydro-mechanical behaviour of soil at both grain and macro scales, is proposed for predicting desiccation cracking in clayey soil. In this framework, a soil layer is represented by an assembly of DEM particles, each occupies an equivalent continuum space and carries physical properties governing unsaturated flow. These particles move freely in the computational space following the discrete element method (DEM), while their contact network and the continuum mixture theory are used to model the unsaturated flow. The dependence of particle-to-particle contact behaviour on water content is represented by a cohesive-frictional contact model, whose material properties are governed by the water content. In parallel with the theoretical development is a series of experiments on 3D soil desiccation cracking to determine essential properties and provide data for the validation of mechanical and physical behaviour. Very good agreement in both physical behaviour (e.g. evolution of water content) and mechanical behaviour (e.g. occurrence and development of cracks, and distribution of compressive and tensile strains) demonstrates that the proposed framework is capable of capturing the hydro-mechanical behaviour of soil during desiccation. The capability of the proposed framework facilitates numerical experiments for insights into the hydro-mechanical behaviour of unsaturated soils that have not been possible before.

In a radiative Auger process, optical decay is accompanied by simultaneous excitation of other carriers. The radiative Auger process gives rise to weak red-shifted satellite peaks in the optical emission spectrum. These satellite peaks have been observed over a large spectral range: in the X-ray emission of atoms; close to visible frequencies on donors in semiconductors and quantum emitters; and at infrared frequencies as shake-up lines in two-dimensional systems. So far, all the work on the radiative Auger process has focussed on detecting the spontaneous emission. However, the fact that the radiative Auger process leads to photon emission suggests that the transition can also be optically excited. In such an inverted radiative Auger process, excitation would correspond to simultaneous photon absorption and electronic de-excitation. Here, we demonstrate optical driving of the radiative Auger transition on a trion in a semiconductor quantum dot. The radiative Auger and the fundamental transition together form a $\Lambda$-system. On driving both transitions of this $\Lambda$-system simultaneously, we observe a reduction of the fluorescence signal by up to $70\%$. Our results demonstrate a type of optically addressable transition connecting few-body Coulomb interactions to quantum optics. The results open up the possibility of carrying out THz spectroscopy on single quantum emitters with all the benefits of optics: coherent laser sources, efficient and fast single-photon detectors. In analogy to optical control of an electron spin, the $\Lambda$-system between the radiative Auger and the fundamental transitions allows optical control of the emitters' orbital degree of freedom., Comment: 8 pages, 6 figures

We construct Wong--Zakai approximations of time--inhomogeneous stochastic differential equations with regime switching (RSSDEs), and provide a convergence rate. %Given a family of finite-variation processes $\{\mathcal{F}^{\lambda}\}_{\lambda\ge 0}$ that converge strongly to a standard Brownian motion $\mathcal{B}$, we construct pathwise approximations for regime-switching, time-inhomogeneous stochastic differential equations in the Wong-Zakai sense. Moreover, we determine the rate of strong convergence to the solutions of such regime-switching SDEs, showing that this rate is almost as good as that of $\{\mathcal{F}^{\lambda}\}_{\lambda\ge 0}$ to $\mathcal{B}$. In the proposed approximations, the standard Brownian motion driving the time-inhomogeneous RSSDEs is replaced by a family of finite--variation processes $\{\mathcal{F}^{\lambda}\}_{\lambda > 0}$. We show that if $\mathcal{F}^{\lambda}$ strongly converges to $\mathcal{B}$ at rate $\delta(\lambda)$, then the Wong--Zakai approximation strongly converges to the original solution of the time--inhomogeneous RSSDE at rate $\delta(\lambda) \lambda^{\varepsilon}$, for any $\varepsilon > 0$. This is the first paper on Wong--Zakai approximations for time--inhomogeneous RSSDEs, and significantly extends the counterparts for time--homogeneous SDEs without regime switching in R\"{o}misch and Wakolbinger (1985).

We construct a stochastic fluid process with an underlying piecewise deterministic Markov process (PDMP) akin to the one used in the construction of the rational arrival process (RAP), which we call the RAP-modulated fluid process. As opposed to the classic stochastic fluid process driven by a Markov jump process, the underlying PDMP of a RAP-modulated fluid process has a continuous state space and is driven by matrix parameters which may not be related to an intensity matrix. Through novel techniques we show how well-known formulae associated to the classic stochastic fluid process, such as first passage probabilities and the stationary distribution of its queue, translate to its RAP-modulated counterpart.