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Time Division Multiplexing (TDM) of cryogenic signal lines is a promising technique that can significantly reduce the required space, minimize the cooldown time, and increase the number of measurable quantum devices per cooldown. Here, we report the TDM of supercurrent with a 1-input-8-outputs voltage-actuated hybrid superconducting demultiplexer for the first time. The device comprises 14 ON/OFF InAsOI-based superconducting Josephson Field Effect Transistors (JoFETs) routed with Al traces. Each JoFET features Al as a superconductor and HfO2 as a gate insulator, and it can entirely suppress the switching current and increase the normal-state resistance by 20 times with a gate voltage of -4.5 V. The superconducting demultiplexer operates up to 100 MHz at 50 mK, features an insertion loss of ~ 0 dB in the superconducting state, and an OFF/ON ratio of ~ 17.5 dB in a 50-Ohm-matched cryogenic measurement setup. The frequency operation range can be extended by designing the demultiplexer with a proper microwave signal transport layout minimizing, at the same time, the impact of the parasitic electrical elements. These achievements open up the practical implementation of superconducting TDM as a key to drastically reducing I/O lines, costs, and space occupation in a cryostat, enabling the scalability of superconducting electronics., Comment: 13 pages, 5 figures, supporting information at the end of the paper. arXiv admin note: text overlap with arXiv:2405.07630

This paper introduces a novel classification for Autonomous Mobile Robots (AMRs), into three phases and five steps, focusing on autonomous collision-free navigation. Additionally, it presents the main methods and widely accepted technologies for each phase of the proposed classification. The purpose of this classification is to facilitate understanding and establish connections between the independent input variables of the system (hardware, software) and autonomous navigation. By analyzing well-established technologies in terms of sensors and methods used for autonomous navigation, this paper aims to provide a foundation of knowledge that can be applied in future projects of mobile robots., Comment: This paper was presented at the JAR Congress in Buenos Aires, Argentina, and published as ID 27 at 9:20 on June 5, 2024. You can find more details on the conference at the following link: https://jar.com.ar/programa.html#programa. Additionally, the content of the presentation was re-recorded and uploaded to YouTube for better understanding: https://www.youtube.com/watch?v=TU6EkT43VfE&t=4s

The hybrid organic-inorganic halide perovskite (HOIP), for example MAPbBr3, exhibits extended spin lifetime and apparent spin lifetime anisotropy in experiments. The underlying mechanisms of these phenomena remain illusive. By utilizing our first-principles densitymatrix dynamics approach with quantum scatterings including electron-phonon and electronelectron interactions and self-consistent spinorbit coupling, we present temperature- and magnetic field-dependent spin lifetimes in hybrid perovskites, in agreement with experimental observations. For centrosymmetric hybrid perovskite MAPbBr3, the experimentally observed spin lifetime anisotropy is mainly attributed to the dynamical Rashba effect arising from the interaction between organic and inorganic components and the rotation of the organic cation. For noncentrosymmetric perovskite, such as MPSnBr3, we found persistent spin helix texture at the conduction band minimum, which significantly enhances the spin lifetime anisotropy. Our study provides theoretical insight to spin dynamics in HOIP and strategies for controlling and optimizing spin transport.

Despite the recent advances in computer vision research, estimating the 3D human pose from single RGB images remains a challenging task, as multiple 3D poses can correspond to the same 2D projection on the image. In this context, depth data could help to disambiguate the 2D information by providing additional constraints about the distance between objects in the scene and the camera. Unfortunately, the acquisition of accurate depth data is limited to indoor spaces and usually is tied to specific depth technologies and devices, thus limiting generalization capabilities. In this paper, we propose a method able to leverage the benefits of depth information without compromising its broader applicability and adaptability in a predominantly RGB-camera-centric landscape. Our approach consists of a heatmap-based 3D pose estimator that, leveraging the paradigm of Privileged Information, is able to hallucinate depth information from the RGB frames given at inference time. More precisely, depth information is used exclusively during training by enforcing our RGB-based hallucination network to learn similar features to a backbone pre-trained only on depth data. This approach proves to be effective even when dealing with limited and small datasets. Experimental results reveal that the paradigm of Privileged Information significantly enhances the model's performance, enabling efficient extraction of depth information by using only RGB images., Comment: ECCV 2024 Workshop T-CAP: TOWARDS A COMPLETE ANALYSIS OF PEOPLE: FINE-GRAINED UNDERSTANDING FOR REAL-WORLD APPLICATIONS

The three-dimensional representation of objects or scenes starting from a set of images has been a widely discussed topic for years and has gained additional attention after the diffusion of NeRF-based approaches. However, an underestimated prerequisite is the knowledge of camera poses or, more specifically, the estimation of the extrinsic calibration parameters. Although excellent general-purpose Structure-from-Motion methods are available as a pre-processing step, their computational load is high and they require a lot of frames to guarantee sufficient overlapping among the views. This paper introduces KRONC, a novel approach aimed at inferring view poses by leveraging prior knowledge about the object to reconstruct and its representation through semantic keypoints. With a focus on vehicle scenes, KRONC is able to estimate the position of the views as a solution to a light optimization problem targeting the convergence of keypoints' back-projections to a singular point. To validate the method, a specific dataset of real-world car scenes has been collected. Experiments confirm KRONC's ability to generate excellent estimates of camera poses starting from very coarse initialization. Results are comparable with Structure-from-Motion methods with huge savings in computation. Code and data will be made publicly available., Comment: Accepted at ECCVW

We propose and experimentally demonstrate a method for laser cooling an oscillator based on sequences of spin-state-dependent displacements followed by spin repumping. For a thermal state with mean occupation $\bar{n}\gg 1$ the method attains a reduction to 0.632 of the initial thermal oscillator occupation for two repumps of the two-level spin state. This is within a factor of 2.53 of the optimum that might be expected due to the reduction of the oscillator entropy by $2 \ln(2)$. We show that the method, which is based on encoding the value of the modular-variable of the oscillator into the spin, has a simple semi-classical description in terms of a Bayesian update. We demonstrate the method experimentally using the internal and motional states of a single trapped ion.

In this work, we propose a simple yet effective method to tackle the problem of imbalanced multi-class semantic segmentation in deep learning systems. One of the key properties for a good training set is the balancing among the classes. When the input distribution is heavily imbalanced in the number of instances, the learning process could be hindered or difficult to carry on. To this end, we propose a Dynamic Label Injection (DLI) algorithm to impose a uniform distribution in the input batch. Our algorithm computes the current batch defect distribution and re-balances it by transferring defects using a combination of Poisson-based seamless image cloning and cut-paste techniques. A thorough experimental section on the Magnetic Tiles dataset shows better results of DLI compared to other balancing loss approaches also in the challenging weakly-supervised setup. The code is available at https://github.com/covisionlab/dynamic-label-injection.git, Comment: ECCV 2024 VISION Workshop

Understanding spin relaxation in topological systems such as quantum spin-hall (QSH) insulator is critical for realizing coherent transport at high temperature. WTe$_{2}$, known as a QSH insulator with a high transition temperature of 100K, is an important test-bed of unveiling spin relaxation mechanism in topological materials. In this work, we employ our recently-developed \emph{ab initio} density-matrix dynamics approach to investigate spin relaxation mechanism, and calculate spin lifetime and diffusion length of monolayer 1T'-WTe$_{2}$, at finite temperature under an external electric field. We found the spin lifetime of electrons have the largest anisotropy when measuring along the canted-spin-texture direction. Moreover, we found an opposite trend between spin and carrier relaxation against applied electric field. Most importantly, the relaxation mechanism under intermediate electric field around 1V/nm can not be explained by either Eillot-Yafet or Dyakonov-Perel models, which highlights the generality of our \emph{ab initio} density-matrix framework. We then proposed analytical models to explain its mechanism and compare well with \emph{ab initio} results at small and large electric field. We predict that spin lifetime and spin diffusion length of bulk-state electrons are $\sim$1 ps and $\sim$30 nm at room temperature respectively, suggesting its promise for spintronic applications.

We introduce a novel reservoir engineering approach for stabilizing multi-component Schr\"odinger's cat manifolds. The fundamental principle of the method lies in the destructive interference at crossings of gain and loss Hamiltonian terms in the coupling of an oscillator to a zero-temperature auxiliary system, which are nonlinear with respect to the oscillator's energy. The nature of these gain and loss terms is found to determine the rotational symmetry, energy distributions, and degeneracy of the resulting stabilized manifolds. Considering these systems as bosonic error-correction codes, we analyze their properties with respect to a variety of errors, including both autonomous and passive error correction, where we find that our formalism gives straightforward insights into the nature of the correction. We give example implementations using the anharmonic laser-ion coupling of a trapped ion outside the Lamb-Dicke regime as well as nonlinear superconducting circuits. Beyond the dissipative stabilization of standard cat manifolds and novel rotation symmetric codes, we demonstrate that our formalism allows for the stabilization of bosonic codes linked to cat states through unitary transformations, such as quadrature-squeezed cats. Our work establishes a design approach for creating and utilizing codes using nonlinearity, providing access to novel quantum states and processes across a range of physical systems., Comment: 31 pages, 14 figures. Comments welcome

In this paper, we introduce MoRSE (Mixture of RAGs Security Experts), the first specialised AI chatbot for cybersecurity. MoRSE aims to provide comprehensive and complete knowledge about cybersecurity. MoRSE uses two RAG (Retrieval Augmented Generation) systems designed to retrieve and organize information from multidimensional cybersecurity contexts. MoRSE differs from traditional RAGs by using parallel retrievers that work together to retrieve semantically related information in different formats and structures. Unlike traditional Large Language Models (LLMs) that rely on Parametric Knowledge Bases, MoRSE retrieves relevant documents from Non-Parametric Knowledge Bases in response to user queries. Subsequently, MoRSE uses this information to generate accurate answers. In addition, MoRSE benefits from real-time updates to its knowledge bases, enabling continuous knowledge enrichment without retraining. We have evaluated the effectiveness of MoRSE against other state-of-the-art LLMs, evaluating the system on 600 cybersecurity specific questions. The experimental evaluation has shown that the improvement in terms of relevance and correctness of the answer is more than 10\% compared to known solutions such as GPT-4 and Mixtral 7x8.

We propose a superconducting microwave relaxation oscillator based on a nanowire shunted by a resistor and an inductor controlled by quasiparticle injection from a tunnel junction positioned on it: the QUISTRON. This device exhibits relaxation oscillator behavior with DC voltage-controlled frequency tuning and DC current bias. The device frequency is modulated via the tunnel junction, which induces localized heating by injecting quasiparticles. This heating mechanism modulates the nanowire switching current, enabling relaxation oscillations when it falls below the bias current. We demonstrate the device operating principles and characterize its performance across various parameters, including different choices of shunt resistor, shunt inductance, and bath temperature ranging from 20 mK to 1 K. The device showed oscillation with a frequency range approximately between 1 GHz and 10 GHz and total energy dissipation per cycle of $\sim100$ zJ. Our results suggest that this design offers a promising platform for compact, tunable superconducting oscillators in the microwave spectrum with potential applications in quantum information processing, microwave technology, and ultra-low-power electronics. The straightforward frequency control mechanism and integration potential make this device an attractive candidate for superconducting microwave local oscillators., Comment: 17 pages, 7 figures

Dielectrics featuring a high relative permittivity, i.e., high-k dielectrics, have become the standard insulators in gate architectures, enhancing the electrical performance of both room temperature and cryogenic electronics. This study delves into the cryogenic (3 K) performance of high-k dielectrics commonly used as gate insulators. We fabricated Al2O3 and HfO2 layers via Atomic Layer Deposition (ALD) and we extrapolated relative permittivity (k) and dielectric strength (EBD) from AC (100 Hz to 100 kHz) and DC measurements on metal-insulator-metal capacitors. Our findings reveal a strong dependence of HfO2 cryogenic performance on the ALD growth temperature, while the latter shows a negligible impact on Al2O3. We estimated a ~9 % and ~14 % reduction of the relative permittivity of HfO2 and Al2O3, respectively, from 300 K to 3 K. Additionally, we designed and fabricated Al2O3/HfO2 bilayers and we checked their properties at cryogenic temperatures. The study also investigates the impact of the patterning method, namely, UV or electron-beam lithography (acceleration voltage of 10, 20, or 30 kV), on the high-k dielectric properties., Comment: 22 pages, 4 figures, supporting information at the end of the paper

We are proposing a hybrid superconductor-semiconductor platform using indium arsenide (InAs) grown on an insulating layer of indium aluminum arsenide (InAlAs) heterostructure (InAsOI) as an ideal candidate for coherent caloritronic devices. These devices aim to heat or cool electrons out of equilibrium with respect to the phonon degree of freedom. However, their performances are usually limited by the strength of the electron-phonon (e-ph) coupling and the associated power loss. Our work discusses the advantages of the InAsOI platform, which are based on the significantly low e-ph coupling measured compared to all-metallic state-of-the-art caloritronic devices. Our structure demonstrates values of the e-ph coupling constant up to two orders of magnitude smaller than typical values in metallic structures., Comment: 7 pages, 4 figures

Superconducting circuits based on hybrid InAs Josephson Junctions (JJs) play a starring role in the design of fast and ultra-low power consumption solid-state quantum electronics and exploring novel physical phenomena. Conventionally, 3D substrates, 2D quantum wells (QWs), and 1D nanowires (NWs) made of InAs are employed to create superconductive circuits with hybrid JJs. Each platform has its advantages and disadvantages. Here, we proposed the InAs-on-insulator (InAsOI) as a groundbreaking platform for developing superconducting electronics. An epilayer of semiconductive InAs with different electron densities was grown onto an InAlAs metamorphic buffer layer, efficiently used as a cryogenic insulator to decouple adjacent devices electrically. JJs with various lengths and widths were fabricated employing Al as a superconductor and InAs with different electron densities. We achieved a switching current density of 7.3 uA/um, a critical voltage of 50-to-80 uV, and a critical temperature equal to that of the superconductor used. For all the JJs, the switching current follows a characteristic Fraunhofer pattern with an out-of-plane magnetic field. These achievements enable the use of InAsOI to design and fabricate surface-exposed Josephson Field Effect Transistors with high critical current densities and superior gating properties., Comment: 19 pages, 4 figures, supporting information at the end of the paper

We propose a Machine Learning approach for optimal macroeconomic forecasting in a high-dimensional setting where the covariates present a known group structure. Our model is general enough to encompass specific forecasting models, featuring either many covariates, or unknown nonlinearities, or series sampled at different frequencies. By relying on the novel concept of bi-level sparsity in time-series econometrics, we propose a prior that induces sparsity both at the group level and within groups. We demonstrate that the corresponding posterior distribution contracts at the minimax-optimal rate, is consistent, and has a support that includes the support of the model asymptotically. Our theory allows for correlation between groups, while predictors in the same group can be characterized by strong covariation as well as common characteristics and patterns. Finite sample performance is illustrated through comprehensive Monte Carlo experiments and a real-data nowcasting exercise of the US GDP growth rate.

Efficient heat management at cryogenic temperatures is crucial for superconducting quantum technologies. This study demonstrates the controlled manipulation of the heat flow and heat rectification through an asymmetric superconducting tunnel junction. The system exhibits a non-reciprocal behavior, developing a thermoelectric regime exclusively when the electrode with the larger gap is heated. This feature significantly boosts thermal rectification effectively classifying the device as a heat diode. At the same time when operating as a thermoelectric engine, the same device also functions as a heat pipe, expelling heat from the cryogenic environment, minimizing losses at the cold terminal. This dual functionality is inherently passive, and the performance of the heat pipe and the heat diode can be finely adjusted by modifying the external electrical load., Comment: 6 pages, 4 color figures

We suggest using a device called the Bootstrap Superconducting Quantum Interference Device (BS-SQUID) to break the reciprocity in charge transport. This device uses magnetic flux back-action to create a nonreciprocal current-voltage characteristic, which results in a supercurrent rectification coefficient of up to approximately 95\%. The BS-SQUID works as a quasi-ideal supercurrent diode (SD) and maintains its efficiency up to about 40\% of its critical temperature. The external magnetic flux can be used to adjust or reverse the rectification polarity. Finally, we discuss the finite-voltage operation regime of the SD and present a possible application of our device as a half- and full-wave signal rectifier in the microwave regime., Comment: 9 pages, 7 figures