Search

Monocular depth estimation is a critical task for autonomous driving and many other computer vision applications. While significant progress has been made in this field, the effects of viewpoint shifts on depth estimation models remain largely underexplored. This paper introduces a novel dataset and evaluation methodology to quantify the impact of different camera positions and orientations on monocular depth estimation performance. We propose a ground truth strategy based on homography estimation and object detection, eliminating the need for expensive lidar sensors. We collect a diverse dataset of road scenes from multiple viewpoints and use it to assess the robustness of a modern depth estimation model to geometric shifts. After assessing the validity of our strategy on a public dataset, we provide valuable insights into the limitations of current models and highlight the importance of considering viewpoint variations in real-world applications., Comment: 17 pages, 5 figures. Accepted at ECCV 2024 2nd Workshop on Vision-Centric Autonomous Driving (VCAD)

This paper addresses the challenge of integrating low-resource languages into multilingual automatic speech recognition (ASR) systems. We introduce a novel application of weighted cross-entropy, typically used for unbalanced datasets, to facilitate the integration of low-resource languages into pre-trained multilingual ASR models within the context of continual multilingual learning. We fine-tune the Whisper multilingual ASR model on five high-resource languages and one low-resource language, employing language-weighted dynamic cross-entropy and data augmentation. The results show a remarkable 6.69% word error rate (WER) reduction for the low-resource language compared to the fine-tuned model without applying our approach, and a 48.86% WER reduction compared to the original Whisper model. In addition, our approach yields an average WER reduction of 3.29% across the six languages, showing no degradation for the high-resource languages., Comment: 5 pages, 1 figure. Presented at Interspeech 2024

In this work, the tetrahydrofuran (THF) hydrate-water interfacial free energy is determined at $500\,\text{bar}$, at one point of the univariant two-phase coexistence line of the THF hydrate, by molecular dynamics simulation. The Mold Integration-Host methodology, an extension of the original Mold Integration technique to deal with hydrate-fluid interfaces, is used to calculate the interfacial energy. Water is described using the well-known TIP4P/Ice model and THF is described using a rigid version of the TraPPE model. We have recently used the combination of these two models to accurately describe the univariant two-phase dissociation line of the THF hydrate, in a wide range of pressures, from computer simulation [J. Chem. Phys. 160, 164718 (2024)]. The THF hydrate-water interfacial free energy predicted in this work is compared with the only experimental data available in the literature. The value obtained, $27(2)\,\text{mJ/m}^{2}$, is in excellent agreement with the experimental data taken from the literature, $24(8)\,\text{mJ/m}^{2}$. To the best of our knowledge, this is the first time that the THF hydrate-water interfacial free energy is predicted from computer simulation. This work confirms that the Mold Integration technique can be used with confidence to predict solid-fluid interfaces of complex structures, including hydrates that exhibit sI and sII crystallographic structures., Comment: 11 pages, 6 figures

The rapid advance of quantum computing has highlighted the need for scalable and efficient software infrastructures to fully exploit its potential. While current quantum processors have significant scalability problems due to the limited number of qubits on each chip, distributed quantum computing offers a promising solution by networking multiple Quantum Processing Units (QPUs). To support this paradigm, robust Intermediate Representations (IRs) are crucial for translating high-level quantum algorithms into executable instructions across distributed systems. This paper introduces NetQIR, an extension of Microsoft's Quantum Intermediate Representation (QIR), specifically designed for distributed quantum computing. NetQIR is designed to meet the specific needs of distributed quantum systems by incorporating functions to manage quantum and classical communications between QPUs. The main objective is to facilitate the development of new distributed compilers by improving the integration and execution of quantum programmes in a distributed infrastructure, taking advantage of modular architectures to improve scalability. By extending QIR to support distributed quantum computing, NetQIR aims to complement and add capabilities to an already supported quantum IR and, at the same time, take advantage of the tools previously created for QIR. Throughout this paper the specification of the intermediate representation is introduced, including the basic instructions necessary to enable distributed quantum computing in an abstract form independent of the target machine.

We investigate the driven-dissipative dynamics of 1D and 2D arrays of multilevel atoms interacting via dipole-dipole interactions and trapped at subwavelength scales. Here we show that in the weakly driven low excitation regime, multilevel atoms, in contrast to two-level atoms, can become strongly entangled. The entanglement manifests as the growth of collective spin-waves in the ground state manifold, and survives even after turning off the drive. We propose to use the $\sim 2.9~\mu$m transition between $\rm ^3{\rm P}_2 \leftrightarrow \, ^3{\rm D}_3$ in $\rm ^{88}Sr$ with $\rm 389~nm$ trapping light as an ideal experimental platform for validating our predictions and as a novel quantum interface for the exploration of complex many-body phenomena emerging from light-matter interactions., Comment: 7+11 pages, 4+14 figures

The emerging field of quantum computing has shown it might change how we process information by using the unique principles of quantum mechanics. As researchers continue to push the boundaries of quantum technologies to unprecedented levels, distributed quantum computing raises as an obvious path to explore with the aim of boosting the computational power of current quantum systems. This paper presents a comprehensive survey of the current state of the art in the distributed quantum computing field, exploring its foundational principles, landscape of achievements, challenges, and promising directions for further research. From quantum communication protocols to entanglement-based distributed algorithms, each aspect contributes to the mosaic of distributed quantum computing, making it an attractive approach to address the limitations of classical computing. Our objective is to provide an exhaustive overview for experienced researchers and field newcomers.

Ultra-cold atomic systems are among the most promising platforms that have the potential to shed light on the complex behavior of many-body quantum systems. One prominent example is the case of a dense ensemble illuminated by a strong coherent drive while interacting via dipole-dipole interactions. Despite being subjected to intense investigations, this system retains many open questions. A recent experiment carried out in a pencil-shaped geometry reported measurements that seemed consistent with the emergence of strong collective effects in the form of a ``superradiant'' phase transition in free space, when looking at the light emission properties in the forward direction. Motivated by the experimental observations, we carry out a systematic theoretical analysis of the system's steady-state properties as a function of the driving strength and atom number, $N$. We observe signatures of collective effects in the weak drive regime, which disappear with increasing drive strength as the system evolves into a single-particle-like mixed state comprised of randomly aligned dipoles. Although the steady-state features some similarities to the reported superradiant to normal non-equilibrium transition, also known as cooperative resonance fluorescence, we observe significant qualitative and quantitative differences, including a different scaling of the critical drive parameter (from $N$ to $\sqrt{N}$). We validate the applicability of a mean-field treatment to capture the steady-state dynamics under currently accessible conditions. Furthermore, we develop a simple theoretical model that explains the scaling properties by accounting for interaction-induced inhomogeneous effects and spontaneous emission, which are intrinsic features of interacting disordered arrays in free space., Comment: 25 pages, 19 figures

The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral algorithms for medical applications is the lack of specific, publicly available, and hyperspectral medical data. The work described in this paper was developed within the framework of the European project HELICoiD (HypErspectraL Imaging Cancer Detection), which had as a main goal the application of hyperspectral imaging to the delineation of brain tumors in real-time during neurosurgical operations. In this paper, the methodology followed to generate the first hyperspectral database of in-vivo human brain tissues is presented. Data was acquired employing a customized hyperspectral acquisition system capable of capturing information in the Visual and Near InfraRed (VNIR) range from 400 to 1000 nm. Repeatability was assessed for the cases where two images of the same scene were captured consecutively. The analysis reveals that the system works more efficiently in the spectral range between 450 and 900 nm. A total of 36 hyperspectral images from 22 different patients were obtained. From these data, more than 300 000 spectral signatures were labeled employing a semi-automatic methodology based on the spectral angle mapper algorithm. Four different classes were defined: normal tissue, tumor tissue, blood vessel, and background elements. All the hyperspectral data has been made available in a public repository., Comment: 19 pages, 12 figures

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a noncontact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

In an increasingly demanding marketplace that will put great strain on railway services, research on broadband wireless communication must continue to strive for improvement. Based on the mature narrowband GSM technology, Global System for Mobile Communications-Railways (GSM-R) has been deployed both for operational and voice communications. Although GSM-R fulfills the requirements of current railway services, it imposes limited capacity and high costs that restrict enhancements of operational efficiency, passenger security and transport quality. 4G Long Term Evolution (LTE) is expected to be the natural successor of GSM-R not only for its technical advantages and increasing performance, but also due to the current evolution of general-purpose communication systems. This paper examines the key features of LTE as well as its technical ability to support both the migration of current railway services and the provisioning of future ones., Comment: This is a portion of the ACCEPTED VERSION of the published document in: Kassab, M., Berbineau,M., Vinel, A., Jonsson, M., Garcia, F., Soler, J. (eds) Communication Technologies for Vehicles. Nets4Cars/Nets4Trains/Nets4Aircraft 2015. Lecture Notes in Computer Science, vol 9066. Springer, Cham. https://doi.org/10.1007/978-3-319-17765-6_14

Doubly-selective channels, such as those that occur when the transmitter and the receiver move relative to each other at high speeds, are a key scenario for fifth generation (5G) cellular systems, which are mostly based in the use of the orthogonal frequency-division multiplexing (OFDM) modulation. In this paper, we consider an OFDM system using quadrature amplitude modulation (QAM) symbols and we show that, when transmitting over deterministic doubly-selective channels, the inter-carrier interference (ICI) affecting a symbol can be well approximated by a complex-valued normal distribution. Based on this, we derive a lower bound for the link capacity using the Shannon-Hartley theorem. Finally, we provide an approximation of the bit error probability (BEP) using the well-known BEP expressions for Gray-coded QAM constellations over additive white Gaussian noise (AWGN) channels, and show numerical results that confirm that the proposed BEP expression approximates accurately the bit error ratio (BER) of the OFDM system for standardized channel models. The proposed closed-form analytical expressions for the capacity and the BEP do not only allow for discarding the need of computationally-costly Monte-Carlo system simulations, but also provide a theoretical framework to optimize the system parameters directly impacting on the achievable performance., Comment: 12 pages, 11 figures, published in IEEE Transactions on Vehicular Technology

We show how a driven-dissipative cavity coupled to a collective ensemble of atoms can dynamically generate metrologically useful spin-squeezed states. In contrast to other dissipative approaches, we do not rely on complex engineered dissipation or input states, nor do we require tuning the system to a critical point. Instead, we utilize a strong symmetry, a special type of symmetry that can occur in open quantum systems and emerges naturally in systems with collective dissipation, such as superradiance. This symmetry preserves coherence and allows for the accumulation of an atom number-dependent Berry phase which in turn creates spin-squeezed states via emergent one-axis twisting dynamics. This work shows that it is possible to generate entanglement in an atom-cavity resonant regime with macroscopic optical excitations of the system, going beyond the typical dispersive regime with negligible optical excitations often utilized in current cavity QED experiments., Comment: 5+14 pages, 3 figures

Antimicrobial peptides (AMPs) play important roles in cancer, autoimmune diseases, and aging. A critical aspect of AMP functionality is their targeted interaction with pathogen membranes, which often possess altered lipid compositions. Designing AMPs with enhanced therapeutic properties relies on a nuanced understanding of these interactions, which are believed to trigger a rearrangement of these peptides from random coil to alpha-helical conformations, essential for their lytic action. Traditional supercomputing has consistently encountered difficulties in accurately modeling these structural changes, especially within membrane environments, thereby opening an opportunity for more advanced approaches. This study extends an existing quantum computing algorithm to address the complexities of antimicrobial peptide interactions at interfaces. Our approach enables the prediction of the optimal conformation of peptides located in the transition region between hydrophilic and hydrophobic phases, akin to lipid membranes. The new method has been applied to model the structure of three 10-amino-acid-long peptides, each exhibiting hydrophobic, hydrophilic, or amphipathic properties in different media and at interfaces between solvents of different polarity. Notably, our approach does not demand a higher number of qubits compared to simulations in homogeneous media, making it more feasible with current quantum computing resources. Despite existing limitations in computational power and qubit accessibility, our findings demonstrate the significant potential of quantum computing in accurately characterizing complex biomolecular processes, particularly the folding of AMPs at membrane models. This research paves the way for future advances in quantum computing to enhance the accuracy and applicability of biomolecular simulations.

We utilize multilevel atoms trapped in a driven resonant optical cavity to produce scalable multi-mode squeezed states for quantum sensing and metrology. While superradiance or collective dissipative emission by itself has been typically a detrimental effect for entanglement generation in optical cavities, in the presence of additional drives it can also be used as an entanglement resource. In a recent work [Phys. Rev. Lett. 132, 033601 (2024)], we described a protocol for the dissipative generation of two-mode squeezing in the dark state of a six-level system with only one relevant polarization. There we showed that up to two quadratures can be squeezed. Here, we develop a generalized analytic treatment to calculate the squeezing in any multilevel system where atoms can collectively decay by emitting light into two polarization modes in a cavity. We show that in this more general system up to four spin squeezed quadratures can be obtained. We study how finite-size effects constrain the reachable squeezing, and analytically compute the scaling with $N$. Our findings are readily testable in current optical cavity experiments with alkaline-earth-like atoms., Comment: 14 pages, 5 figures + Appendix; References updated; Moved some sections from Appendix to main text

Twisted bilayers of transition metal dichalcogenides (TMDs) have revealed a rich exciton landscape including hybrid excitons and spatially trapped moir\'e excitons that dominate the optical response of the material. Recent studies have shown that in the low-twist-angle regime, the lattice undergoes a significant relaxation in order to minimize local stacking energies. Here, large domains of low energy stacking configurations emerge, deforming the crystal lattices via strain and consequently impacting the electronic band structure. However, so far the direct impact of atomic reconstruction on the exciton energy landscape and the optical properties has not been well understood. Here, we apply a microscopic and material-specific approach and predict a significant change in the potential depth for moir\'e excitons in a reconstructed lattice, with the most drastic change occurring in naturally stacked TMD homobilayers. We show the appearance of multiple flat bands and a significant change in the position of trapping sites compared to the rigid lattice. Most importantly, we predict a multi-peak structure emerging in optical absorption of WSe$_2$ homobilayers - in contrast to the single peak that dominates the rigid lattice. This finding can be exploited as an unambiguous signature of atomic reconstruction in optical spectra of moir\'e excitons in naturally stacked twisted homobilayers.

We consider a simple home energy system consisting of a (net) load, an energy storage device, and a grid connection. We focus on minimizing the cost for grid power that includes a time-varying usage price and a tiered peak power charge that depends on the average of the largest $N$ daily powers over a month. When the loads and prices are known, the optimal operation of the storage device can be found by solving a mixed-integer linear program (MILP). This prescient charging policy is not implementable in practice, but it does give a bound on the best performance possible. We propose a simple model predictive control (MPC) method that relies on simple forecasts of future prices and loads. The MPC problem is also an MILP, but it can be solved directly as a linear program (LP) using simple enumeration of the tiers for the current and next months, and so is fast and reliable. Numerical experiments on real data from a home in Trondheim, Norway, show that the MPC policy achieves a cost that is only $1.7\%$ higher than the prescient performance bound.

One of the challenges of excitonic materials is the accurate determination of the exciton binding energy and bandgap. The difficulty arises from the overlap of the discrete and continuous excitonic absorption at the band edge. Many researches have modeled the shape of the absorption edge of such materials on the Elliott model and its several modifications such as non-parabolic bands, magnetic potentials and electro-hole-polaron interactions. However, exciton binding energies obtained from measured data often vary strongly depending on the chosen model. Here, we propose an alternative and rather simple approach, which has previously been successful in the determination of the optical bandgap of amorphous, direct and indirect semiconductors, based on the bands-fluctuations (BF) model. In this model, the fluctuations due to disorder, temperature or lattice vibrations give rise to the well known exponential distribution of band tail states (Urbach tails). This analysis results in an analytic equation with 5 parameters only. The binding energies and optical bandgaps of GaAs and the family of tri-halide perovskites ($\textrm{MAPbX}_{3}$), $\textrm{X=Br,I,Cl}$, over a wide range of temperatures, are obtained with this model. The results for the bandgap, linewidth and exciton binding energy are in good agreement with previous reports. Moreover, due to the polar nature of perovskites, the obtained binding energies can be compared with the ones computed with a theoretical model for polar materials via a model proposed by Kane et al. In this model, the exciton is surrounded by a cloud of virtual phonons interacting via the Fr$\ddot{\textrm{o}}$lich interaction. As a consequence, the upper bound for the binding energy of the exciton-polaron system is calculated. Coincidentally, these results are in good agreement with the optical constants obtained with the EBF model., Comment: Preprint submitted to: J. Phys. D: Appl. Phys

Humanitarian agencies must be prepared to mobilize quickly in response to complex emergencies, and their effectiveness depends on their ability to identify, anticipate, and prepare for future needs. These are typically highly uncertain situations in which predictive modeling tools can be useful but challenging to build. To better understand the need for humanitarian support -- including shelter and assistance -- and strengthen contingency planning and protection efforts for displaced populations, we present a situational analysis tool to help anticipate the number of migrants and forcibly displaced persons that will cross a border in a humanitarian crisis. The tool consists of: (i) indicators of potential intent to move drawn from traditional and big data sources; (ii) predictive models for forecasting possible future movements; and (iii) a simulation of border crossings and shelter capacity requirements under different conditions. This tool has been specifically adapted to contingency planning in settings of high uncertainty, with an application to the Brazil-Venezuela border during the COVID-19 pandemic., Comment: 9 pages, 5 figures

We describe a method to create and store scalable and long-lived entangled spin-squeezed states within a manifold of many-body cavity dark states using collective emission of light from multilevel atoms inside an optical cavity. We show that the system can be tuned to generate squeezing in a dark state where it will be immune to superradiance. We also show more generically that squeezing can be generated using a combination of superradiance and coherent driving in a bright state, and subsequently be transferred via single-particle rotations to a dark state where squeezing can be stored. Our findings, readily testable in current optical cavity experiments with alkaline-earth-like atoms, can open a path for dissipative generation and storage of metrologically useful states in optical transitions., Comment: 4.5 pages, 3 figures + Supplement; New sections in Supplement

Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human-authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, to assist human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section., Comment: 11 pages, 1 figure

We propose new models to describe the imaginary part of the electrical permittivity of dielectric and semiconductor materials in the fundamental absorption region. We work out our procedure based on the well-known structure of the Tauc-Lorentz model and the band-fluctuations approach to derive a 5-parameter formula that describes the Urbach, Tauc and high-absorption regions of direct and indirect semiconductors. Main features of the models are the self-consistent generation of the exponential Urbach tail below the bandgap and the incorporation of the Lorentz oscillator behaviour due to electronic transitions above the fundamental region. We apply and test our models on optical data of direct (MAPbI$_{3}$, GaAs and InP), indirect (GaP and c-Si), and amorphous (a-Si) semiconductors, accurately describing the spectra of the imaginary part of the electrical permittivity. Lastly, we compare our models with other similarly inspired models to assess the optical bandgap, Urbach tail and oscillator central resonance energy., Comment: 15 pages, 9 figures, preprint submitted to J. Phys. D: Appl. Phys

We propose a new direction in quantum simulation that uses multilevel atoms in an optical cavity as a toolbox to engineer new types of bosonic models featuring correlated hopping processes in a synthetic ladder spanned by atomic ground states. The underlying mechanisms responsible for correlated hopping are collective cavity-mediated interactions that dress a manifold of excited levels in the far detuned limit. By weakly coupling the ground state levels to these dressed states using two laser drives with appropriate detunings, one can engineer correlated hopping processes while suppressing undesired single-particle and collective shifts of the ground state levels. We discuss the rich many-body dynamics that can be realized in the synthetic ladder including pair production processes, chiral transport and light-cone correlation spreading. The latter illustrates that an effective notion of locality can be engineered in a system with fully collective interactions., Comment: 6+9 pages, 4+4 figures

We propose to simulate bosonic pair creation using large arrays of long-lived dipoles with multilevel internal structure coupled to an undriven optical cavity. Entanglement between the atoms, generated by the exchange of virtual photons through a common cavity mode, grows exponentially fast and is described by two-mode squeezing of effective bosonic quadratures. The mapping between an effective bosonic model and the natural spin description of the dipoles allows us to realize the analog of optical homodyne measurements via straightforward global rotations and population measurements of the electronic states, and we propose to exploit this for quantum-enhanced sensing of an optical phase (common and differential between two ensembles). We discuss a specific implementation based on Sr atoms and show that our sensing protocol is robust to sources of decoherence intrinsic to cavity platforms. Our proposal can open unique opportunities for next-generation optical atomic clocks., Comment: 5 pages + 3 figures + references + supplement; Updated parts of main text and supplement

Nontrivial topology in lattices is characterized by invariants--such as the Zak phase for one dimensional (1D) lattices--derived from wave functions covering the Brillouin zone. We realized the 1D bipartite Rice-Mele (RM) lattice using ultracold $^{87}$Rb and focus on lattice configurations possessing various combinations of chiral, time-reversal and particle-hole symmetries. We quenched between configurations and used a form of quantum state tomography, enabled by diabatically tuning lattice parameters, to directly follow the time evolution of the Zak phase as well as a chiral winding number. The Zak phase evolves continuously; however, when chiral symmetry transiently appears in the out-of-equilibrium system, the chiral winding number is well defined and can take on different integer values. When quenching between two configurations obeying all three symmetries the Zak phase is time independent; we confirm the contrasting prediction of [M. McGinley and N. R.Cooper, PRL 121 090401 (2018)] that chiral symmetry is periodically restored, at which times the winding number changes by $\pm 2$, yielding values that are not present in the native RM Hamiltonian.