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Waveguide arrays are a powerful platform for studying and manipulating quantum states of light. When nonlinearity arises due to a spontaneous parametric down-conversion process, the degree of entanglement can increase, contrary to a linear array, enabling the generation of nonclassical biphoton states -- which are a valuable resource for various quantum technologies. In this work, we employed a supermodes approach to obtain an analytic solution for the evolution of degenerate biphoton states under the undepleted pump approximation. We examined the general features of our solution, including results for small arrays, propagation when only one waveguide is pumped, and the inversion problem of a target output state. Analytic results offer valuable physical insights into the propagation of light in arrays of nonlinear waveguides, and enable the determination of the initial conditions required to achieve a desired quantum state -- for example, the injection pump profile. In general, such calculations can be computationally demanding for large arrays. However, the numerical implementation of the proposed method scales efficiently -- both for the direct, and inverse problems. In future work, our approach could be extended to non-degenerate biphoton states. Also, it could be applied in the study of diffusion regimes, the introduction of disorder, and the development of reliable optimization methods for inverting arbitrary output states., Comment: 11 pages, 4 figures

Water molecules predominantly act as solvents in electrochemical systems and are often modeled as a passive dielectric medium. In this work, we use molecular dynamics simulations and theoretical analysis to revisit this conventional view. We reveal that the interfacial polarized water overscreens the electrostatic potential between ions and the surface beyond being a passive dielectric medium. This overscreening enables the interfacial water to dominate the electric potential spatial distribution, inverting the electrode surface potential polarity and dominating the capacitance. A model is then developed to incorporate this critical interfacial water polarization.

We argue that topological operators for continuous symmetries written in terms of currents need regularization, which effectively gives them a small but finite width. The regulated operator is a finite tension object which fluctuates. In the zero-width limit these fluctuations freeze, recovering the properties of a topological operator. When gravity is turned on, the zero-width limit becomes ill-defined, thereby prohibiting the existence of topological operators., Comment: 8 pages including references

Estimated brain age from magnetic resonance image (MRI) and its deviation from chronological age can provide early insights into potential neurodegenerative diseases, supporting early detection and implementation of prevention strategies. Diffusion MRI (dMRI), a widely used modality for brain age estimation, presents an opportunity to build an earlier biomarker for neurodegenerative disease prediction because it captures subtle microstructural changes that precede more perceptible macrostructural changes. However, the coexistence of macro- and micro-structural information in dMRI raises the question of whether current dMRI-based brain age estimation models are leveraging the intended microstructural information or if they inadvertently rely on the macrostructural information. To develop a microstructure-specific brain age, we propose a method for brain age identification from dMRI that minimizes the model's use of macrostructural information by non-rigidly registering all images to a standard template. Imaging data from 13,398 participants across 12 datasets were used for the training and evaluation. We compare our brain age models, trained with and without macrostructural information minimized, with an architecturally similar T1-weighted (T1w) MRI-based brain age model and two state-of-the-art T1w MRI-based brain age models that primarily use macrostructural information. We observe difference between our dMRI-based brain age and T1w MRI-based brain age across stages of neurodegeneration, with dMRI-based brain age being older than T1w MRI-based brain age in participants transitioning from cognitively normal (CN) to mild cognitive impairment (MCI), but younger in participants already diagnosed with Alzheimer's disease (AD). Approximately 4 years before MCI diagnosis, dMRI-based brain age yields better performance than T1w MRI-based brain ages in predicting transition from CN to MCI.

Prostate cancer is the second most common form of cancer, though most patients have a positive prognosis with many experiencing long-term survival with current treatment options. Yet, each treatment carries varying levels of intensity and side effects, therefore determining the severity of prostate cancer is an important criteria in selecting the most appropriate treatment. The Gleason score is the most common grading system used to judge the severity of prostate cancer, but much of the grading process can be affected by human error or subjectivity. Finding biomarkers for prostate cancer Gleason scores in a quantitative, machine-driven approach could enable pathologists to validate their assessment of a patient cancer sample by examining such biomarkers. In our study, we identified biomarkers from multi-omics data using machine learning, statistical tools, and deep learning to train models against the Gleason score and capture the most important features that could potentially serve as biomarkers for the Gleason score. Through this process, multiple genes, such as COL1A1 and SFRP4, and cell cycle pathways, such as G2M checkpoint, E2F targets, and the PLK1 pathways, were found to be important predictive features for particular Gleason scores. The combination of these analytical methods shows potential for more accurate grading of prostate cancer, and greater understanding of biological processes behind prostate cancer severity that could provide additional therapeutic targets.

We studied the effects of point defects and Hg impurities in the electronic properties of bismuth iodide (Bi$_4$I$_4$). Our transport measurements after annealing at different temperatures show that the resistivity of Bi$_4$I$_4$ depends on its thermal history, suggesting that the formation of native defects and impurities can shape the temperature dependence of electrical resistivity. Our density functional theory calculations indicate that the bismuth and iodine antisites, and bismuth vacancies are the dominant native point defects. We find that bismuth antisites introduce resonant states in the band-edges, while iodine antisites and bismuth vacancies lead to a $n$-type and $p$-type doping of Bi$_4$I$_4$, respectively. The Hg impurities are likely to be found at Bi substitutional sites, giving rise to the $p$-type doping of Bi$_4$I$_4$. Overall, our findings indicate that the presence of native point defects and impurities can significantly modify the electronic properties, and, thus, impact the resistivity profile of Bi$_4$I$_4$ due to modifications in the amount and type of carriers, and the associated defect(impurity) scattering. Our results suggest possible routes for pursuing fine-tuning of the electronic properties of quasi-one-dimensional quantum materials.

We introduce a general approach for the simulation of quantum vibrational states of (symmetric and asymmetric) double-well potentials in molecules and materials for thermodynamic and spectroscopic applications. The method involves solving the nuclear Schr\"odinger equation associated with a one-mode potential of the type $V (Q) = aQ^2 + bQ^3 + cQ^4$ (with $a < 0$ and $c > 0$), and thus explicitly includes nuclear quantum effects. The potential, $V (Q)$, is obtained from density functional theory (DFT) calculations performed at displaced nuclear configurations along the selected normal mode, $Q$. The strategy has been implemented into the CRYSTAL electronic structure package and allows for i) the use of many density functional approximations, including hybrid ones, and ii) integration with a quasi-harmonic module. The method is applied to the spectroscopic characterization of soft lattice modes in two phases of the molecular crystal of thiourea: a low-temperature ferroelectric phase and a high-temperature paraelectric phase. Signature peaks associated to structural changes between the two phases are found in the terahertz region of the electromagnetic spectrum, which exhibit strong anharmonic character in their thermal evolution, as measured by temperature-dependent terahertz time-domain spectroscopy.

Local search is a common method for solving combinatorial optimisation problems. We focus on general-purpose local search solvers that accept as input a constraint model - a declarative description of a problem consisting of a set of decision variables under a set of constraints. Existing approaches typically take as input models written in solver-independent constraint modelling languages like MiniZinc. The Athanor solver we describe herein differs in that it begins from a specification of a problem in the abstract constraint specification language Essence, which allows problems to be described without commitment to low-level modelling decisions through its support for a rich set of abstract types. The advantage of proceeding from Essence is that the structure apparent in a concise, abstract specification of a problem can be exploited to generate high quality neighbourhoods automatically, avoiding the difficult task of identifying that structure in an equivalent constraint model. Based on the twin benefits of neighbourhoods derived from high level types and the scalability derived by searching directly over those types, our empirical results demonstrate strong performance in practice relative to existing solution methods., Comment: 48 pages

Data privacy and authentication are two main security requirements for remote access and cloud services. While QKD has been explored to address data privacy concerns, oftentimes its use is separate from the client authentication protocol despite implicitly providing authentication. Here, we present a quantum authentication key expansion (QAKE) protocol that (1) integrates both authentication and key expansion within a single protocol, and (2) provides key recycling property -- allowing all authentication keys to be reused. We analyse the security of the protocol in a QAKE framework adapted from a classical authentication key exchange (AKE) framework, providing separate security conditions for authentication and data privacy. An experimental implementation of the protocol, with appropriate post-selection, was performed to demonstrate its feasibility., Comment: 71 pages, comments are welcome

In order to solve problems of practical importance, quantum computers will likely need to incorporate quantum error correction, where a logical qubit is redundantly encoded in many noisy physical qubits. The large physical-qubit overhead typically associated with error correction motivates the search for more hardware-efficient approaches. Here, using a microfabricated superconducting quantum circuit, we realize a logical qubit memory formed from the concatenation of encoded bosonic cat qubits with an outer repetition code of distance $d=5$. The bosonic cat qubits are passively protected against bit flips using a stabilizing circuit. Cat-qubit phase-flip errors are corrected by the repetition code which uses ancilla transmons for syndrome measurement. We realize a noise-biased CX gate which ensures bit-flip error suppression is maintained during error correction. We study the performance and scaling of the logical qubit memory, finding that the phase-flip correcting repetition code operates below threshold, with logical phase-flip error decreasing with code distance from $d=3$ to $d=5$. Concurrently, the logical bit-flip error is suppressed with increasing cat-qubit mean photon number. The minimum measured logical error per cycle is on average $1.75(2)\%$ for the distance-3 code sections, and $1.65(3)\%$ for the longer distance-5 code, demonstrating the effectiveness of bit-flip error suppression throughout the error correction cycle. These results, where the intrinsic error suppression of the bosonic encodings allows us to use a hardware-efficient outer error correcting code, indicate that concatenated bosonic codes are a compelling paradigm for reaching fault-tolerant quantum computation., Comment: Comments on the manuscript welcome!

In this paper we construct the classical phase space of Jackiw-Teitelboim gravity with positive cosmological constant on spatial slices with circle topology. This turns out to be somewhat more intricate than in the case of negative cosmological constant; this phase space has many singular points and is not even Hausdorff. Nonetheless, it admits a group-theoretic description which is quite amenable to quantization., Comment: 27 pages plus appendices, 9 figures

Transition metal dichalcogenides (TMDCs) possess valley pseudospin, allowing photon spin to be coupled to electron spin and enabling initialization and readout of both classical and quantum information. Rapid valley-dephasing processes have impeded the development of scalable, high-performance valleytronic devices operating at room temperature. Here we demonstrate that a chiral resonant metasurface can enable room-temperature valley-selective emission, even with linearly polarized excitation. This platform provides circular eigen-polarization states with a high quality factor (Q-factor) and strong chiral near-field enhancement, resulting in unitary emission circular dichroism (i.e. single-handed circularly polarized emission). Our fabricated Si chiral metasurfaces exhibit chiral electromagnetic modes with Q-factors up to 450 at visible wavelengths, spectrally tuned to the exciton energy of MoSe2 monolayers. Using spatially- and spectrally-resolved mapping from temperatures of 100 K to 294 K, we demonstrate degrees of circular polarization (DOP) reaching a record high of 0.5 at room temperature. Reciprocal space mapping of the exciton emission reveals the chiral q-BIC localizes valley-selective emission in the vicinity of the photonic gamma-point. Photon-spin and time-resolved photoluminescence measurements show that the high DOP can be attributed to the significantly increased chiroptical local density of states provided by the metasurface, which enhances valley-specific radiative transition rates by a factor of approximately 13, with lifetimes as short as 189 ps. Our work could facilitate the development of compact chiral classical and quantum light sources and the creation of molecular chiral polaritons for quantum enantioselective synthesis.

Algebraic lattices are those obtained from modules in the ring of integers of algebraic number fields through the canonical or twisted embeddings. In turn, well-rounded lattices are those with maximal cardinality of linearly independent vectors in its set of minimal vectors. Both classes of lattices have been applied for signal transmission in some channels, such as wiretap channels. Recently, some advances have been made in the search for well-rounded lattices that can be realized as algebraic lattices. Moreover, some works have been published studying algebraic lattices obtained from modules in cyclic number fields of odd prime degree $p$. In this work, we generalize some results of a recent work of Tran et al. and we provide new constructions of well-rounded algebraic lattices from a certain family of modules in the ring of integers of each of these fields when $p$ is ramified in its extension over the field of rational numbers., Comment: 11 pages

In this thesis, we re-assess some aspects of axionic electrodynamics by coupling non-linear electromagnetic effects to axion physics. We present a number of motivations to justify the coupling of the axion to the photon in terms of a general non-linear extension of the electromagnetic sector. Our emphasis in the paper relies on the investigation of the constitutive permittivity and permeability tensors, for which the axion contribution introduces a dependence on the frequency and wave vector of the propagating radiation. Also, we point out how the axion mass and the axion-photon coupling constant contribute to a dispersive behavior of the electromagnetic waves, in contrast to what happens in the case of non-linear extensions, when effective refractive indices appear which depend only on the direction of the propagation with respect to the external fields. The axion changes this picture by yielding refractive indices with dependence on the wavelength. We apply our results to the special case of the Born-Infeld Electrodynamics and we show that it becomes birefringent whenever the axion is coupled. We also include in our analysis the Lorentz violation phenomenon through a Carroll-Field-Jackiw term, where we investigate the influence of this sector on the dispersion relations, and consequently, on the optical phenomena., Comment: PhD Thesis

In the dynamic field of Software Engineering (SE), where practice is constantly evolving and adapting to new technologies, conducting research is a daunting quest. This poses a challenge for researchers: how to stay relevant and effective in their studies? Empirical Software Engineering (ESE) has emerged as a contending force aiming to critically evaluate and provide knowledge that informs practice in adopting new technologies. Empirical research requires a rigorous process of collecting and analyzing data to obtain evidence-based findings. Challenges to this process are numerous, and many researchers, novice and experienced, found difficulties due to many complexities involved in designing their research. The core of this chapter is to teach foundational skills in research design, essential for educating software engineers and researchers in ESE. It focuses on developing a well-structured research design, which includes defining a clear area of investigation, formulating relevant research questions, and choosing appropriate methodologies. While the primary focus is on research design, this chapter also covers aspects of research scoping and selecting research methods. This approach prepares students to handle the complexities of the ever-changing technological landscape in SE, making it a critical component of their educational curriculum.

Multi-controlled gates are fundamental components in the design of quantum algorithms, where efficient decompositions of these operators can enhance algorithm performance. The best asymptotic decomposition of an n-controlled X gate with one borrowed ancilla into single qubit and CNOT gates produces circuits with degree 3 polylogarithmic depth and employs a divide-and-conquer strategy. In this paper, we reduce the number of recursive calls in the divide-and-conquer algorithm and decrease the depth of n-controlled X gate decomposition to a degree of 2.799 polylogarithmic depth. With this optimized decomposition, we also reduce the depth of n-controlled SU(2) gates and approximate n-controlled U(2) gates. Decompositions described in this work achieve the lowest asymptotic depth reported in the literature. We also perform an optimization in the base of the recursive approach. Starting at 52 control qubits, the proposed n-controlled X gate with one borrowed ancilla has the shortest circuit depth in the literature. One can reproduce all the results with the freely available open-source code provided in a public repository., Comment: 6 pages, 8 figures

Recent years have witnessed a growing call for renewed emphasis on neuroscience-inspired approaches in artificial intelligence research, under the banner of NeuroAI. A prime example of this is predictive coding networks (PCNs), based on the neuroscientific framework of predictive coding. This framework views the brain as a hierarchical Bayesian inference model that minimizes prediction errors through feedback connections. Unlike traditional neural networks trained with backpropagation (BP), PCNs utilize inference learning (IL), a more biologically plausible algorithm that explains patterns of neural activity that BP cannot. Historically, IL has been more computationally intensive, but recent advancements have demonstrated that it can achieve higher efficiency than BP with sufficient parallelization. Furthermore, PCNs can be mathematically considered a superset of traditional feedforward neural networks (FNNs), significantly extending the range of trainable architectures. As inherently probabilistic (graphical) latent variable models, PCNs provide a versatile framework for both supervised learning and unsupervised (generative) modeling that goes beyond traditional artificial neural networks. This work provides a comprehensive review and detailed formal specification of PCNs, particularly situating them within the context of modern ML methods. Additionally, we introduce a Python library (PRECO) for practical implementation. This positions PC as a promising framework for future ML innovations., Comment: 46 pages, 13 figures, 8 tables

We propose and discuss a paradigm that allows for expressing \emph{data-parallel} rendering with the classically non-parallel ANARI API. We propose this as a new standard for data-parallel sci-vis rendering, describe two different implementations of this paradigm, and use multiple sample integrations into existing apps to show how easy it is to adopt this paradigm, and what can be gained from doing so.

Dental panoramic radiographs offer vast diagnostic opportunities, but training supervised deep learning networks for automatic analysis of those radiology images is hampered by a shortage of labeled data. Here, a different perspective on this problem is introduced. A semi-supervised learning framework is proposed to classify thirteen dental conditions on panoramic radiographs, with a particular emphasis on teeth. Large language models were explored to annotate the most common dental conditions based on dental reports. Additionally, a masked autoencoder was employed to pre-train the classification neural network, and a Vision Transformer was used to leverage the unlabeled data. The analyses were validated using two of the most extensive datasets in the literature, comprising 8,795 panoramic radiographs and 8,029 paired reports and images. Encouragingly, the results consistently met or surpassed the baseline metrics for the Matthews correlation coefficient. A comparison of the proposed solution with human practitioners, supported by statistical analysis, highlighted its effectiveness and performance limitations; based on the degree of agreement among specialists, the solution demonstrated an accuracy level comparable to that of a junior specialist., Comment: 43 pages, 12 figures, 9 tables

We propose to use automatically generated instruction-following data to improve the zero-shot capabilities of a large multimodal model with additional support for generative and image editing tasks. We achieve this by curating a new multimodal instruction-following set using GPT-4V and existing datasets for image generation and editing. Using this instruction set and the existing LLaVA-Finetune instruction set for visual understanding tasks, we produce GenLLaVA, a Generative Large Language and Visual Assistant. GenLLaVA is built through a strategy that combines three types of large pretrained models through instruction finetuning: Mistral for language modeling, SigLIP for image-text matching, and StableDiffusion for text-to-image generation. Our model demonstrates visual understanding capabilities superior to LLaVA and additionally demonstrates competitive results with native multimodal models such as Unified-IO 2, paving the way for building advanced general-purpose visual assistants by effectively re-using existing multimodal models. We open-source our dataset, codebase, and model checkpoints to foster further research and application in this domain., Comment: Add more results using task tokens, expand the introduction and related work FIX: error in LLM-as-judge evaluation that was over-inflating the results

An important challenge in machine learning is to predict the initial conditions under which a given neural network will be trainable. We present a method for predicting the trainable regime in parameter space for deep feedforward neural networks, based on reconstructing the input from subsequent activation layers via a cascade of single-layer auxiliary networks. For both the MNIST and CIFAR10 datasets, we show that a single epoch of training of the shallow cascade networks is sufficient to predict the trainability of the deep feedforward network, thereby providing a significant reduction in overall training time. We achieve this by computing the relative entropy between reconstructed images and the original inputs, and show that this probe of information loss is sensitive to the phase behaviour of the network. Moreover, our approach illustrates the network's decision making process by displaying the changes performed on the input data at each layer. Our results provide a concrete link between the flow of information and the trainability of deep neural networks, further explaining the role of criticality in these systems., Comment: 21 pages, 5 figures, 1 table Minor changes to presentation, typos corrected

This paper analyzes the convergence rates of the {\it Frank-Wolfe } method for solving convex constrained multiobjective optimization. We establish improved convergence rates under different assumptions on the objective function, the feasible set, and the localization of the limit point of the sequence generated by the method. In terms of the objective function values, we firstly show that if the objective function is strongly convex and the limit point of the sequence generated by the method lies in the relative interior of the feasible set, then the algorithm achieves a linear convergence rate. Next, we focus on a special class of problems where the feasible constraint set is $(\alpha,q)$-uniformly convex for some $\alpha >0$ and $q \geq 2$, including, in particular, \(\ell_p\)-balls for all $p>1$. In this context, we prove that the method attains: (i) a rate of $\mathcal{O}(1/k^\frac{q}{q-1})$ when the objective function is strongly convex; and (ii) a linear rate (if $q=2$) or a rate of $\mathcal{O}(1/k^{\frac{q}{q-2}})$ (if $q>2$) under an additional assumption, which always holds if the feasible set does not contain an unconstrained weak Pareto point. We also discuss enhanced convergence rates for the algorithm in terms of an optimality measure. Finally, we provide some simple examples to illustrate the convergence rates and the set of assumptions.

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

Sliding tasks performed by aerial robots are valuable for inspection and simple maintenance tasks at height, such as non-destructive testing and painting. Although various end-effector designs have been used for such tasks, non-actuated wheel configurations are more frequently applied thanks to their rolling capability for sliding motion, mechanical simplicity, and lightweight design. Moreover, a non-actuated multi-wheel (more than one wheel) configuration in the end-effector design allows the placement of additional equipment e.g., sensors and tools in the center of the end-effector tip for applications. However, there is still a lack of studies on crucial contact conditions during sliding using aerial robots with such an end-effector design. In this article, we investigate the key challenges associated with sliding operations using aerial robots equipped with multiple non-actuated wheels through in-depth analysis grounded in physical experiments. The experimental data is used to create a simulator that closely captures real-world conditions. We propose solutions from both mechanical design and control perspectives to improve the sliding performance of aerial robots. From a mechanical standpoint, design guidelines are derived from experimental data. From a control perspective, we introduce a novel pressure-sensing-based control framework that ensures reliable task execution, even during sliding maneuvers. The effectiveness and robustness of the proposed approaches are then validated and compared using the built simulator, particularly in high-risk scenarios.

The evolution of a relativistic heavy-ion collision is typically understood as a process that transmutes the initial geometry of the system into the final momentum distribution of observed hadrons, which can be described via a cumulant expansion of the initial distribution of energy density and is represented at leading order as the well-known eccentricity scaling of anisotropic flow. We extend this framework to include the contribution from initial momentum-space properties, as encoded in other components of the energy-momentum tensor. We confirm the validity of the framework in state-of-the-art hydrodynamic simulations of large and small systems. With this new framework, it is possible to separate the effects of early-time dynamics from those of final-state evolution, even in the case when the distribution of energy does not fully determine subsequent evolution, as for example, in small systems. Specifically, we answer the question of when and how azimuthal correlations from the initial state survive to the final state. In very small systems such as $p$-$p$, for example, initial momentum degrees of freedom dominate over energy. Thus, even if the system forms a quark-gluon plasma that is well described by hydrodynamics, the usual hydrodynamic picture of the transmutation of initial geometry to final momentum anisotropy is broken. Nevertheless, we show that the hydrodynamic response to the full energy-momentum tensor can be well understood in a similar manner as larger systems. Additionally, this framework elucidates the generic features of the system's evolution that are responsible for the impressive success of hydrodynamic simulations, but which may still hold even in cases where hydrodynamics is not applicable., Comment: 10 pages, 5 figures