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Quantum imaging encompasses a broad range of methods that exploit the quantum properties of light to capture information about an object. One such approach involves using a two-photon quantum state, where only one photon interacts with the object being imaged while its entangled partner carries spatial or temporal information. To implement this technique, it is necessary to generate specific quantum states of light and detect photons at the single-photon level. While this method has been successfully demonstrated in the visible electromagnetic spectrum, extending it to X-rays has faced significant challenges due to the difficulties in producing a sufficient rate of X-ray photon pairs and detecting them with adequate resolution. Here, we demonstrate record high rates of correlated X-ray photon pairs produced via a spontaneous parametric down-conversion process and we employ these photons to perform quantum correlation imaging of several objects, including a biological sample (E. cardamomum seedpod). Notably, we report an unprecedented detection rate of about 6,300 pairs per hour and the observation of energy anti-correlation for the X-ray photon pairs. We also present a detailed analysis of the properties of the down-converted X-ray photons, as well as a comprehensive study of the correlation imaging formation, including a study of distortions and corrections. These results mark a substantial advancement in X-ray quantum imaging, expanding the possibilities of X-ray quantum optical technologies, and illustrating the pathway towards enhancing biological imaging with reduced radiation doses., Comment: 12 pages, 6 figures, publication preprint

We respond to arguments against our criticism that claim to show a divergence of Assembly Theory from popular compression. We have proven that any implementation of the concept of `copy number' underlying Assembly Theory (AT) and its assembly index (Ai) is equivalent to Shannon Entropy and not fundamentally or methodologically different from algorithms like ZIP compression. We show that the weak empirical correlation between Ai and LZW, which the authors offered as a defense against the proof that the assembly index calculation method is a compression scheme, is based on an incomplete and misleading experiment. When the experiment is completed, the asymptotic convergence to LZ compression and Shannon Entropy is evident and aligned with the mathematical proof previously offered. Therefore, this completes the theoretical and empirical demonstrations that any variation of the copy-number concept underlying AT, which resorts to counting the number of object repetitions `to arrive at a measure for life,' is equivalent to statistical compression and Shannon Entropy. We demonstrate that the authors' `we-are-better-because-we-are-worse' defense argument against compression does not withstand basic scrutiny and that their empirical results separating organic from inorganic compounds have not only been previously reported -- sans claims to unify physics and biology -- but are also driven solely by molecular length, not a particular feature of life captured by their assembly index. Finally, we show that Ai is a particular case of our BDM introduced almost a decade earlier and that arguments attributing special stochastic properties to Ai are misleading, not unique, and exactly the same as those that Shannon Entropy is already not only equipped with but designed for which we have also proven to be equivalent to Ai making AT redundant even in practice when applied to their own experimental data., Comment: 12 figures, 53 pages (minor tweaks and adding new refs of previous relevant work not cited by the authors of AT)

Spatial transformations of light are ubiquitous in optics, with examples ranging from simple imaging with a lens to quantum and classical information processing in waveguide meshes. Multi-plane light converter (MPLC) systems have emerged as a platform that promises completely general spatial transformations, i.e., a universal unitary. However until now, MPLC systems have demonstrated transformations that are far from general, e.g., converting from a Gaussian to Laguerre-Gauss mode. Here, we demonstrate the promise of an MLPC, the ability to impose an arbitrary unitary transformation that can be reconfigured dynamically. Specifically, we consider transformations on superpositions of parallel free-space beams arranged in an array, which is a common information encoding in photonics. We experimentally test the full gamut of unitary transformations for a system of two parallel beams and make a map of their fidelity. We obtain an average transformation fidelity of $0.85 \pm 0.03$. This high-fidelity suggests MPLCs are a useful tool implementing the unitary transformations that comprise quantum and classical information processing.

Modeling is often associated with complex and heavy tooling, leading to a negative perception among practitioners. However, alternative paradigms, such as everything-as-code or low-code, are gaining acceptance due to their perceived ease of use. This paper explores the dichotomy between these perceptions through the lens of ``modeler experience'' (MX). MX includes factors such as user experience, motivation, integration, collaboration \& versioning and language complexity. We examine the relationships between these factors and their impact on different modeling usage scenarios. Our findings highlight the importance of considering MX when understanding how developers interact with modeling tools and the complexities of modeling and associated tooling.

The Hanbury Brown-Twiss (HBT) effect holds a pivotal place in intensity interferometry and gave a seminal contribution to the development of quantum optics. To observe such an effect, both good spectral and timing resolutions are necessary. Most often, the HBT effect is observed for a single frequency at a time, due to limitations in dealing with multifrequencies simultaneously, halting and limiting some applications. Here, we report a fast and data-driven spectrometer built with a one-dimensional array of single-photon-sensitive avalanche diodes. We report observing the HBT effect for multifrequencies at the same time. Specifically, we observed the HBT for up to 5 lines of the Ne spectrum, but this can be improved even further. Our work represents a major step to make spectral binning and multifrequencies HBT more widely available. The technology we present can benefit both classical and quantum applications.

Adapting the user interface (UI) of software systems to meet the needs and preferences of users is a complex task. The main challenge is to provide the appropriate adaptations at the appropriate time to offer value to end-users. Recent advances in Machine Learning (ML) techniques may provide effective means to support the adaptation process. In this paper, we instantiate a reference framework for Intelligent User Interface Adaptation by using Reinforcement Learning (RL) as the ML component to adapt user interfaces and ultimately improving the overall User Experience (UX). By using RL, the system is able to learn from past adaptations to improve the decision-making capabilities. Moreover, assessing the success of such adaptations remains a challenge. To overcome this issue, we propose to use predictive Human-Computer Interaction (HCI) models to evaluate the outcome of each action (ie adaptations) performed by the RL agent. In addition, we present an implementation of the instantiated framework, which is an extension of OpenAI Gym, that serves as a toolkit for developing and comparing RL algorithms. This Gym environment is highly configurable and extensible to other UI adaptation contexts. The evaluation results show that our RL-based framework can successfully train RL agents able to learn how to adapt UIs in a specific context to maximize the user engagement by using an HCI model as rewards predictor., Comment: To be published in Companion of the16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS Companion '24). 9 pages, 2 figures, 28 references

Based on the principles of information theory, measure theory, and theoretical computer science, we introduce a signal deconvolution method with a wide range of applications to coding theory, particularly in zero-knowledge one-way communication channels, such as in deciphering messages (i.e., objects embedded into multidimensional spaces) from unknown generating sources about which no prior knowledge is available and to which no return message can be sent. Our multidimensional space reconstruction method from an arbitrary received signal is proven to be agnostic vis-\`a-vis the encoding-decoding scheme, computation model, programming language, formal theory, the computable (or semi-computable) method of approximation to algorithmic complexity, and any arbitrarily chosen (computable) probability measure. The method derives from the principles of an approach to Artificial General Intelligence (AGI) capable of building a general-purpose model of models independent of any arbitrarily assumed prior probability distribution. We argue that this optimal and universal method of decoding non-random data has applications to signal processing, causal deconvolution, topological and geometric properties encoding, cryptography, and bio- and technosignature detection., Comment: arXiv:2303.16045 is based on this paper. arXiv admin note: substantial text overlap with arXiv:2303.16045

Purpose: Software modelling and Model-Driven Engineering (MDE) is traditionally studied from a technical perspective. However, one of the core motivations behind the use of software models is inherently human-centred. Models aim to enable practitioners to communicate about software designs, make software understandable, or make software easier to write through domain-specific modelling languages. Several recent studies challenge the idea that these aims can always be reached and indicate that human factors play a role in the success of MDE. However, there is an under-representation of research focusing on human factors in modelling. Methods: During a GI-Dagstuhl seminar, topics related to human factors in modelling were discussed by 26 expert participants from research and industry. Results: In breakout groups, five topics were covered in depth, namely modelling human aspects, factors of modeller experience, diversity and inclusion in MDE, collaboration and MDE, and teaching human-aware MDE. Conclusion: We summarise our insights gained during the discussions on the five topics. We formulate research goals, questions, and propositions that support directing future initiatives towards an MDE community that is aware of and supportive of human factors and values.

Goal-oriented analysis tools are used to assess goal models and assist analysts in decision-making. We introduce the VeGAn-Tool, which prioritizes goals according to their qualitative importance for the stakeholders and propagates this information in the goal model according to the different types of relationships. The FTOPSIS technique is used to calculate the value of each intentional element by employing the fuzzified importance (importance level fuzzified and refined by a confidence level) and the impact among the related intentional elements. The result is a prioritized goal model according to the value of each intentional element from the stakeholders' point of view.

Light, being massless, casts no shadow; under ordinary circumstances, photons pass right through each other unimpeded. Here, we demonstrate a laser beam acting like an object - the beam casts a shadow upon a surface when the beam is illuminated by another light source. We observe a regular shadow in the sense it can be seen by the naked eye, it follows the contours of the surface it falls on, and it follows the position and shape of the object (the laser beam). Specifically, we use a nonlinear optical process involving four atomic levels of ruby. We are able to control the intensity of a transmitted laser beam by applying another perpendicular laser beam. We experimentally measure the dependence of the contrast of the shadow on the power of the object laser beam, finding a maximum of approximately of approximately 22 percent, similar to that of a shadow of a tree on a sunny day. We provide a theoretical model that predicts the contrast of the shadow. This work opens new possibilities for fabrication, imaging, and illumination.

We prove the full equivalence between Assembly Theory (AT) and Shannon Entropy via a method based upon the principles of statistical compression renamed `assembly index' that belongs to the LZ family of popular compression algorithms (ZIP, GZIP, JPEG). Such popular algorithms have been shown to empirically reproduce the results of AT, results that have also been reported before in successful applications to separating organic from non-organic molecules and in the context of the study of selection and evolution. We show that the assembly index value is equivalent to the size of a minimal context-free grammar. The statistical compressibility of such a method is bounded by Shannon Entropy and other equivalent traditional LZ compression schemes, such as LZ77, LZ78, or LZW. In addition, we demonstrate that AT, and the algorithms supporting its pathway complexity, assembly index, and assembly number, define compression schemes and methods that are subsumed into the theory of algorithmic (Kolmogorov-Solomonoff-Chaitin) complexity. Due to AT's current lack of logical consistency in defining causality for non-stochastic processes and the lack of empirical evidence that it outperforms other complexity measures found in the literature capable of explaining the same phenomena, we conclude that the assembly index and the assembly number do not lead to an explanation or quantification of biases in generative (physical or biological) processes, including those brought about by (abiotic or Darwinian) selection and evolution, that could not have been arrived at using Shannon Entropy or that have not been reported before using classical information theory or algorithmic complexity., Comment: 15 pages + appendix, 2 figures

We study the effective stochastic dynamics of a semiclassical probe induced by linear optomechanical interactions with a quantum oscillator. Quantum fluctuations lead to state-dependent non-equilibrium noise, which is exponentially enhanced by wavepacket delocalization. For the case of nanoparticles coupled by the Coulomb interaction such noise can imprint potentially measurable signatures in multiparticle levitation experiments. Quantum-induced optomechanical fluctuations hold strong analogy to quantum gravitational wave noise and interconnect stochastic thermodynamics, graviton physics and the detection of gravity-mediated entanglement., Comment: 23 pages, 5 figures

Inspired by the early Wittgenstein's concept of nonsense (meaning that which lies beyond the limits of language), we investigate two different types of nonsense: formal nonsense and pragmatic nonsense. The simpler notion of formal nonsense is defined in accordance with Tarski's semantic theory of truth; the notion of pragmatic nonsense is in turn formulated within the context of the theory of pragmatic truth, also known as quasi-truth, as formalized by da Costa and his collaborators. Pragmatic nonsense extends formal nonsense, the same way da Costa's pragmatic truth is an extension of Tarski's definition of truth. An expression is thus considered formally nonsensical in case the formal criteria required for the assignment of any truth-value (whether true, false, pragmatically true, or pragmatically false) are not met; and an expression, or even a well-formed formula, is considered pragmatically nonsensical if either the formal or the pragmatic criteria of relevance (inscribed within the context of scientific practice) required for the assignment of any pragmatic truth-value (pragmatically true or pragmatically false) are not met. We also introduce the concept of strictly pragmatic truth, which excludes pragmatic nonsense and necessarily depends on certain criteria of relevance, unlike the original definition of pragmatic truth/quasi-truth.

We offer an argument against simplicity as a sole intrinsic criterion for nomic realism. The argument is based on the simplicity bubble effect. Underdetermination in quantum foundations illustrates the case., Comment: Contributed talk for the Third Graduate Conference of the Italian Network for the Philosophy of Mathematics --- FilMat. Submitted: September 15, 2023. Approved: October 25, 2023

Spontaneous parametric down-conversion is a vital method for generating correlated photon pairs in the visible and near-infrared spectral regions; however, its extension to X-ray frequencies has faced substantial barriers. Here, we present an advancement in correlated X-ray pair generation and detection by employing a two-dimensional pixelated detector to obtain the first direct image of the pair distribution. Our study explores and directly visualizes the down-conversion process, revealing the characteristic ring structure of coincident photon pairs and demonstrating robust spatial correlations. A significant finding is the observation of energy anti-correlation, achieved at an unprecedented rate of approximately 4,100 pairs/hour, far exceeding previous reports in the literature. We believe these results represent a significant leap in X-ray quantum imaging, unlocking the potential for enhanced imaging of biological materials with reduced doses and broadening the applicability of X-ray quantum optical technologies., Comment: 23 pages, 6 main figures, 4 supplemental figures

Adapting the User Interface (UI) of software systems to user requirements and the context of use is challenging. The main difficulty consists of suggesting the right adaptation at the right time in the right place in order to make it valuable for end-users. We believe that recent progress in Machine Learning techniques provides useful ways in which to support adaptation more effectively. In particular, Reinforcement learning (RL) can be used to personalise interfaces for each context of use in order to improve the user experience (UX). However, determining the reward of each adaptation alternative is a challenge in RL for UI adaptation. Recent research has explored the use of reward models to address this challenge, but there is currently no empirical evidence on this type of model. In this paper, we propose a confirmatory study design that aims to investigate the effectiveness of two different approaches for the generation of reward models in the context of UI adaptation using RL: (1) by employing a reward model derived exclusively from predictive Human-Computer Interaction (HCI) models (HCI), and (2) by employing predictive HCI models augmented by Human Feedback (HCI&HF). The controlled experiment will use an AB/BA crossover design with two treatments: HCI and HCI&HF. We shall determine how the manipulation of these two treatments will affect the UX when interacting with adaptive user interfaces (AUI). The UX will be measured in terms of user engagement and user satisfaction, which will be operationalized by means of predictive HCI models and the Questionnaire for User Interaction Satisfaction (QUIS), respectively. By comparing the performance of two reward models in terms of their ability to adapt to user preferences with the purpose of improving the UX, our study contributes to the understanding of how reward modelling can facilitate UI adaptation using RL., Comment: 6 pages + 1 refs. 2 figures

By utilizing quantum optics techniques, we examine the characteristics of a quantum gravitational wave (GW) signature at interferometers. In particular, we study the problem by analyzing the equations of motion of a GW interacting with an idealized interferometer. Using this method, we reconstruct the classical GW signal from a representation of the quantum version of an almost classical monochromatic wave (a single-mode coherent state), then we discuss the experimental signatures of some specific, more general quantum states. We calculate the observables that could be used at future interferometers to probe possible quantum states carried by the gravitational waves.

Recent advances in machine learning and AI, including Generative AI and LLMs, are disrupting technological innovation, product development, and society as a whole. AI's contribution to technology can come from multiple approaches that require access to large training data sets and clear performance evaluation criteria, ranging from pattern recognition and classification to generative models. Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Generative AI, in general, and Large Language Models in particular, may represent an opportunity to augment and accelerate the scientific discovery of fundamental deep science with quantitative models. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery, including self-driven hypothesis generation and open-ended autonomous exploration of the hypothesis space. Integrating AI-driven automation into the practice of science would mitigate current problems, including the replication of findings, systematic production of data, and ultimately democratisation of the scientific process. Realising these possibilities requires a vision for augmented AI coupled with a diversity of AI approaches able to deal with fundamental aspects of causality analysis and model discovery while enabling unbiased search across the space of putative explanations. These advances hold the promise to unleash AI's potential for searching and discovering the fundamental structure of our world beyond what human scientists have been able to achieve. Such a vision would push the boundaries of new fundamental science rather than automatize current workflows and instead open doors for technological innovation to tackle some of the greatest challenges facing humanity today., Comment: 35 pages, first draft of the final report from the Alan Turing Institute on AI for Scientific Discovery

The perturbative integral method was applied to quantify the contribution of external forces during a specific interval of time in trajectories of spacecraft around asteroids and under the Luni-solar influence. However, this method has not been used to quantify the contributions of drag in aerocapture and aerobraking. For this reason, the planet Mars is selected to apply this method during an aerogravity-assisted maneuver. Several trajectories are analyzed, making use of a drag device with area to mass ratios varying from 0.0 to 20.0 m2/kg, simulating solar sails or de-orbit devices. The mathematical model is based in the restricted three-body problem. The use of this maneuver makes it possible to obtain the variations of energy in the trajectory, replacing expensive maneuvers based on fuel consumption. To observe the effects of the maneuvers, different values of pericenter velocity and altitude were selected for prograde and retrograde orbits. The innovation of this research is the application of an integral method to quantify the delta-V of the aero gravity maneuver, comparing the cost of the maneuver with the traditional methods of space propulsion. The results allow the identification of orbits with conditions to capture, and the perturbative maps show the velocity variations.

Knowing the properties of the single photons produced in a Spontaneous Parametric Down-Conversion (SPDC) source can be crucial for specific applications and uses. In particular, the spectral properties are of key relevance. Here, we investigate a commercial SPDC source using our fast broadband spectrometer. Our analysis is a valid method for other SPDC sources, as well as other single-photon generation techniques, thus providing a good example of how to use this spectrometer design. We calibrate the spectrometer using known lines of the argon emission spectrum. We show that the two down-converted photons from the SPDC source have different spectral properties depending on the pump power, and in which condition we measured spectrally similar down-converted photons. Lastly, we were able to reconstruct and investigate the spectral information for the pump photon.

Adaptive user interfaces have the advantage of being able to dynamically change their aspect and/or behaviour depending on the characteristics of the context of use, i.e. to improve user experience(UX). UX is an important quality factor that has been primarily evaluated with classical measures but to a lesser extent with physiological measures, such as emotion recognition, skin response, or brain activity.In a previous exploratory experiment involving users with different profiles and a wide range of ages, we analysed user experience in terms of cognitive load, engagement, attraction and memorisation when employing twenty graphical adaptive menus through the use of an Electroencephalogram (EEG) device. The results indicated that there were statistically significant differences for these four variables. However, we considered that it was necessary to confirm or reject these findings using a more homogeneous group of users.We conducted an operational internal replication study with 40 participants. We also investigated the potential correlation between EEG signals and the participants' user experience ratings, such as their preferences.The results of this experiment confirm that there are statistically significant differences between the EEG variables when the participants interact with the different adaptive menus. Moreover, there is a high correlation among the participants' UX ratings and the EEG signals, and a trend regarding performance has emerged from our analysis.These findings suggest that EEG signals could be used to evaluate UX. With regard to the menus studied, our results suggest that graphical menus with different structures and font types produce more differences in users' brain responses, while menus which use colours produce more similarities in users' brain responses. Several insights with which to improve users' experience of graphical adaptive menus are outlined., Comment: 10 pages, 4 figures, 2 tables, 34 references, International Conference on Evaluation and Assessment in Software Engineering (EASE '23)

We present a single-photon-sensitive spectrometer, based on a linear array of 512 single-photon avalanche diode detectors, with 0.04 nm spectral and 40 ps temporal resolutions. We employ a fast data-driven operation that allows direct measurement of time and frequency for simultaneous single photons, time- and frequency-stamping each single-photon detection. Our results combine excellent temporal and spectral resolution. We benchmark our result against the Heisenberg Uncertainty Principle limit of hbar/2 for time and energy, and we are only a factor of 10 above it, despite the simplicity of our experimental setup, including room temperature operation. This work opens numerous applications in both classical and quantum photonics, especially when both spectral and temporal properties of single photons are exploited.

In this article, we discuss the ubiquity of Big Data and machine learning in society and propose that it evinces the need of further investigation of their fundamental limitations. We extend the ``too much information tends to behave like very little information'' phenomenon to formal knowledge about lawlike universes and arbitrary collections of computably generated datasets. This gives rise to the simplicity bubble problem, which refers to a learning algorithm equipped with a formal theory that can be deceived by a dataset to find a locally optimal model which it deems to be the global one. In the context of lawlike (computable) universes and formal learning systems, we show that there is a ceiling above which formal knowledge cannot further decrease the probability of zemblanitous findings, should the randomly generated data made available to the formal learning system be sufficiently large in comparison to their joint complexity. Zemblanity, the opposite of serendipity, is defined by an undesirable but expected finding that reveals an underlying problem or negative consequence in a given model or theory, which is in principle predictable in case the formal theory contains sufficient information. We also argue that this is an epistemological limitation that may generate unpredictable problems in digitally intermediated societies.

We present an agnostic signal reconstruction method for zero-knowledge one-way communication channels in which a receiver aims to interpret a message sent by an unknown source about which no prior knowledge is available and to which no return message can be sent. Our reconstruction method is agnostic vis-\`a-vis the arbitrarily chosen encoding-decoding scheme and other observer-dependent characteristics, such as the arbitrarily chosen computational model, probability distributions, or underlying mathematical theory. We investigate how non-random messages encode information about their intended physical properties, such as dimension and length scales of the space in which a signal or message may have been originally encoded, embedded, or generated. We focus on image data as a first illustration of the capabilities of the new method. We argue that our results have applications to life and technosignature detection, and to coding theory in general.

Classical optical interferometery requires maintaining live, phase-stable links between telescope stations. This requirement greatly adds to the cost of extending to long baseline separations, and limits on baselines will in turn limit the achievable angular resolution. Here we describe a novel type of two-photon interferometer for astrometry, which uses photons from two separate sky sources and does not require an optical link between stations. Such techniques may make large increases in interferometric baselines practical, even by orders of magnitude, with corresponding improvement in astrometric precision benefiting numerous fields in astrophysics. We tested a benchtop analogue version of the two-source interferometer and unambiguously observe correlated behavior in detections of photon pairs from two thermal light sources, in agreement with theoretical predictions. This work opens new possibilities in future astronomical measurements.

We demonstrate that the assembly pathway method underlying assembly theory (AT) is an encoding scheme widely used by popular statistical compression algorithms. We show that in all cases (synthetic or natural) AT performs similarly to other simple coding schemes and underperforms compared to system-related indexes based upon algorithmic probability that take into account statistical repetitions but also the likelihood of other computable patterns. Our results imply that the assembly index does not offer substantial improvements over existing methods, including traditional statistical ones, and imply that the separation between living and non-living compounds following these methods has been reported before.

We examine the precision limits of Hong-Ou-Mandel (HOM) timing measurements, as well as precision limits applying to generalized two-photon measurements. As a special case, we consider the use of two-photon measurements using photons with variable bandwidths and frequency correlations. When the photon bandwidths are not equal, maximizing the measurement precision involves a trade-off between high interference visibility and strong frequency anticorrelations, with the optimal precision occuring when the photons share non-maximal frequency anticorrelations. We show that a generalized measurement has precision limits that are qualitatively similar to those of the HOM measurement whenever the generalized measurement is insensitive to the net delay of both photons. By examining the performance of states with more general frequency distributions, our analysis allows for engineering of the joint spectral amplitude for use in realistic situations, in which both photons may not have ideal spectral properties., Comment: 12 pages, 6 figures; resubmission

This article describes the setup and performance of the near and far detectors in the Double Chooz experiment. The electron antineutrinos of the Chooz nuclear power plant were measured in two identically designed detectors with different average baselines of about 400 m and 1050 m from the two reactor cores. Over many years of data taking the neutrino signals were extracted from interactions in the detectors with the goal of measuring a fundamental parameter in the context of neutrino oscillation, the mixing angle {\theta}13. The central part of the Double Chooz detectors was a main detector comprising four cylindrical volumes filled with organic liquids. From the inside towards the outside there were volumes containing gadolinium-loaded scintillator, gadolinium-free scintillator, a buffer oil and, optically separated, another liquid scintillator acting as veto system. Above this main detector an additional outer veto system using plastic scintillator strips was installed. The technologies developed in Double Chooz were inspiration for several other antineutrino detectors in the field. The detector design allowed implementation of efficient background rejection techniques including use of pulse shape information provided by the data acquisition system. The Double Chooz detectors featured remarkable stability, in particular for the detected photons, as well as high radiopurity of the detector components., Comment: 49 pages, 29 figures

When mining large datasets in order to predict new data, limitations of the principles behind statistical machine learning pose a serious challenge not only to the Big Data deluge, but also to the traditional assumptions that data generating processes are biased toward low algorithmic complexity. Even when one assumes an underlying algorithmic-informational bias toward simplicity in finite dataset generators, we show that current approaches to machine learning (including deep learning, or any formal-theoretic hybrid mix of top-down AI and statistical machine learning approaches), can always be deceived, naturally or artificially, by sufficiently large datasets. In particular, we demonstrate that, for every learning algorithm (with or without access to a formal theory), there is a sufficiently large dataset size above which the algorithmic probability of an unpredictable deceiver is an upper bound (up to a multiplicative constant that only depends on the learning algorithm) for the algorithmic probability of any other larger dataset. In other words, very large and complex datasets can deceive learning algorithms into a ``simplicity bubble'' as likely as any other particular non-deceiving dataset. These deceiving datasets guarantee that any prediction effected by the learning algorithm will unpredictably diverge from the high-algorithmic-complexity globally optimal solution while converging toward the low-algorithmic-complexity locally optimal solution, although the latter is deemed a global one by the learning algorithm. We discuss the framework and additional empirical conditions to be met in order to circumvent this deceptive phenomenon, moving away from statistical machine learning towards a stronger type of machine learning based on, and motivated by, the intrinsic power of algorithmic information theory and computability theory.

Previous work has shown that perturbation analysis in software space can produce candidate computable generative models and uncover possible causal properties from the finite description of an object or system quantifying the algorithmic contribution of each of its elements relative to the whole. One of the challenges for defining emergence is that one observer's prior knowledge may cause a phenomenon to present itself to such observer as emergent while for another as reducible. By formalising the act of observing as mutual perturbations between dynamical systems, we demonstrate that emergence of algorithmic information do depend on the observer's formal knowledge, while robust to other subjective factors, particularly: the choice of the programming language and the measurement method; errors or distortions during the information acquisition; and the informational cost of processing. This is called observer-dependent emergence (ODE). In addition, we demonstrate that the unbounded and fast increase of emergent algorithmic information implies asymptotically observer-independent emergence (AOIE). Unlike ODE, AOIE is a type of emergence for which emergent phenomena will remain considered to be emergent for every formal theory that any observer might devise. We demonstrate the existence of an evolutionary model that displays the diachronic variant of AOIE and a network model that displays the holistic variant of AOIE. Our results show that, restricted to the context of finite discrete deterministic dynamical systems, computable systems, and irreducible information content measures, AOIE is the strongest form of emergence that formal theories can attain.

Autonomous vehicles (AVs) must always have a safe motion to guarantee that they are not causing any accidents. In an AV system, the motion of the vehicle is represented as a trajectory. A trajectory planning component is responsible to compute such a trajectory at run-time, taking into account the perception information about the environment, the dynamics of the vehicles, the predicted future states of other road users and a number of safety aspects. Due to the enormous amount of information to be considered, trajectory planning algorithms are complex, which makes it non-trivial to guarantee the safety of all planned trajectories. In this way, it is necessary to have an extra component to assess the safety of the planned trajectories at run-time. Such trajectory safety assessment component gives a diverse observation on the safety of AV trajectories and ensures that the AV only follows safe trajectories. We use the term trajectory checker to refer to the trajectory safety assessment component. The trajectory checker must evaluate planned trajectories against various safety rules, taking into account a large number of possibilities, including the worst-case behavior of other traffic participants. This must be done while guaranteeing hard real-time performance since the safety assessment is carried out while the vehicle is moving and in constant interaction with the environment. In this paper, we present a prototype of the trajectory checker we have developed at IVEX. We show how our approach works smoothly and accomplishes real-time constraints embedded in an Infineon Aurix TC397B automotive platform. Finally, we measure the performance of our trajectory checker prototype against a set of NCAPS-inspired scenarios.

Incompatible observables underlie pillars of quantum physics such as contextuality and entanglement. The Heisenberg uncertainty principle is a fundamental limitation on the measurement of the product of incompatible observables, a `joint' measurement. However, recently a method using weak measurement has experimentally demonstrated joint measurement. This method [Lundeen, J. S., and Bamber, C. Phys. Rev. Lett. 108, 070402, 2012] delivers the standard expectation value of the product of observables, even if they are incompatible. A drawback of this method is that it requires coupling each observable to a distinct degree of freedom (DOF), i.e., a disjoint Hilbert space. Typically, this `read-out' system is an unused internal DOF of the measured particle. Unfortunately, one quickly runs out of internal DOFs, which limits the number of observables and types of measurements one can make. To address this limitation, we propose and experimentally demonstrate a technique to perform a joint weak-measurement of two incompatible observables using only one DOF as a read-out system. We apply our scheme to directly measure the density matrix of photon polarization states., Comment: 17 pages, 5 figures