Miracle-dz/UniFAR-FTUs huggingface dataset:
Bloch Oscillation Phases investigated by Multi-path Stuckelberg Atom Interferometry

Atoms undergoing Bloch oscillations (BOs) in an accelerating optical lattice acquire momentum of two photon recoils per BO. This technique provides a large momentum transfer tool for atom optics, but its full exploitation for atom interferometric sensors requires experimental characterization of associated phases. Each BO involves a Landau-Zener crossing with multiple crossings inducing interference known as Stuckelberg interference. We develop a multi-path Stuckelberg interferometer and investigate atomic phase evolution during BOs, up to 100 photon recoil momentum transfer. We compare to numerically calculated single-particle Schrodinger evolution, demonstrate highly coherent BO sequences, and assess phase stability requirements for BO-enhanced precision interferometry in fundamental physics and sensing applications.
WonderFlow: Narration-Centric Design of Animated Data Videos

Creating an animated data video enriched with audio narration takes a significant amount of time and effort and requires expertise. Users not only need to design complex animations, but also turn written text scripts into audio narrations and synchronize visual changes with the narrations. This paper presents WonderFlow, an interactive authoring tool, that facilitates narration-centric design of animated data videos. WonderFlow allows authors to easily specify a semantic link between text and the corresponding chart elements. Then it automatically generates audio narration by leveraging text-to-speech techniques and aligns the narration with an animation. WonderFlow provides a visualization structure-aware animation library designed to ease chart animation creation, enabling authors to apply pre-designed animation effects to common visualization components. It also allows authors to preview and iteratively refine their data videos in a unified system, without having to switch between different creation tools. To evaluate WonderFlow's effectiveness and usability, we created an example gallery and conducted a user study and expert interviews. The results demonstrated that WonderFlow is easy to use and simplifies the creation of data videos with narration-animation interplay.
Asynchronous Evolution of Deep Neural Network Architectures

Many evolutionary algorithms (EAs) take advantage of parallel evaluation of candidates. However, if evaluation times vary significantly, many worker nodes (i.e.,\ compute clients) are idle much of the time, waiting for the next generation to be created. Evolutionary neural architecture search (ENAS), a class of EAs that optimizes the architecture and hyperparameters of deep neural networks, is particularly vulnerable to this issue. This paper proposes a generic asynchronous evaluation strategy (AES) that is then adapted to work with ENAS. AES increases throughput by maintaining a queue of up to $K$ individuals ready to be sent to the workers for evaluation and proceeding to the next generation as soon as $M<<K$ individuals have been evaluated. A suitable value for $M$ is determined experimentally, balancing diversity and efficiency. To showcase the generality and power of AES, it was first evaluated in eight-line sorting network design (a single-population optimization task with limited evaluation-time variability), achieving an over two-fold speedup. Next, it was evaluated in 11-bit multiplexer design (a single-population discovery task with extended variability), where a 14-fold speedup was observed. It was then scaled up to ENAS for image captioning (a multi-population open-ended-optimization task), resulting in an over two-fold speedup. In all problems, a multifold performance improvement was observed, suggesting that AES is a promising method for parallelizing the evolution of complex systems with long and variable evaluation times, such as those in ENAS.
Topologically protected subradiant cavity polaritons through linewidth narrowing enabled by dissipationless edge states

Cavity polaritons derived from the strong light-matter interaction at the quantum level provide a basis for efficient manipulation of quantum states via cavity field. Polaritons with narrow linewidth and long lifetime are appealing in applications such as quantum sensing and storage. Here, we propose a prototypical arrangement to implement a whispering-gallery-mode resonator with topological mirror moulded by one-dimensional atom array, which allows to boost the lifetime of cavity polaritons over an order of magnitude. This considerable enhancement attributes to the coupling of polaritonic states to dissipationless edge states protected by the topological bandgap of atom array that suppresses the leakage of cavity modes. When exceeding the width of Rabi splitting, topological bandgap can further reduce the dissipation from polaritonic states to bulk states of atom array, giving arise to subradiant cavity polaritons with extremely sharp linewidth. The resultant Rabi oscillation decays with a rate even below the free-space decay of a single quantum emitter. Inheriting from the topologically protected properties of edge states, the subradiance of cavity polaritons can be preserved in the disordered atom mirror with moderate perturbations involving the atomic frequency, interaction strengths and location. Our work opens up a new paradigm of topology-engineered quantum states with robust quantum coherence for future applications in quantum computing and network.
ForensiBlock: A Provenance-Driven Blockchain Framework for Data Forensics and Auditability

Maintaining accurate provenance records is paramount in digital forensics, as they underpin evidence credibility and integrity, addressing essential aspects like accountability and reproducibility. Blockchains have several properties that can address these requirements. Previous systems utilized public blockchains, i.e., treated blockchain as a black box, and benefiting from the immutability property. However, the blockchain was accessible to everyone, giving rise to security concerns and moreover, efficient extraction of provenance faces challenges due to the enormous scale and complexity of digital data. This necessitates a tailored blockchain design for digital forensics. Our solution, Forensiblock has a novel design that automates investigation steps, ensures secure data access, traces data origins, preserves records, and expedites provenance extraction. Forensiblock incorporates Role-Based Access Control with Staged Authorization (RBAC-SA) and a distributed Merkle root for case tracking. These features support authorized resource access with an efficient retrieval of provenance records. Particularly, comparing two methods for extracting provenance records - off-chain storage retrieval with Merkle root verification and a brute-force search - the off-chain method is significantly better, especially as the blockchain size and number of cases increase. We also found that our distributed Merkle root creation slightly increases smart contract processing time but significantly improves history access. Overall, we show that Forensiblock offers secure, efficient, and reliable handling of digital forensic data.
Stochastic energetics of a colloidal particle trapped in a viscoelastic bath

We investigate the statistics of the fluctuations of the energy transfer between an overdamped Brownian particle, whose motion is confined by a stationary harmonic potential, and a surrounding viscoelastic fluid at constant temperature. We derive an analytical expression for the probability density function of the energy exchanged with the fluid over a finite time interval, which implicitly involves the friction memory kernel that encodes the coupling with such a non-Markovian environment, and reduces to the well known expression for the heat distribution in a viscous fluid. We show that, while the odd moments of this distribution are zero, the even moments can be explicitly expressed in terms of the autocorrelation function of the particle position, which generally exhibits a non-mono-exponential decay when the fluid bath is viscoelastic. Our results are verified by experimental measurements for an optically-trapped colloidal bead in semidilute micellar and polymer solutions, finding and excellent agreement for all time intervals over which the energy exchange takes place.
Tolerant Testing of High-Dimensional Samplers with Subcube Conditioning

We study the tolerant testing problem for high-dimensional samplers. Given as input two samplers $\mathcal{P}$ and $\mathcal{Q}$ over the $n$-dimensional space $\{0,1\}^n$, and two parameters $\varepsilon_2>\varepsilon_1$, the goal of tolerant testing is to test whether the distributions generated by $\mathcal{P}$ and $\mathcal{Q}$ are $\varepsilon_1$-close or $\varepsilon_2$-far. Since exponential lower bounds (in $n$) are known for the problem in the standard sampling model, research has focused on models where one can draw \textit{conditional} samples. Among these models, \textit{subcube conditioning} ($\mathsf{SUBCOND}$), which allows conditioning on arbitrary subcubes of the domain, holds the promise of widespread adoption in practice owing to its ability to capture the natural behavior of samplers in constrained domains. To translate the promise into practice, we need to overcome two crucial roadblocks for tests based on $\mathsf{SUBCOND}$: the prohibitively large number of queries ($\tilde{\mathcal{O}}(n^5/\varepsilon_2^5)$) and limitation to non-tolerant testing (i.e., $\varepsilon_1 = 0$). The primary contribution of this work is to overcome the above challenges: we design a new tolerant testing methodology (i.e., $\varepsilon_1 \geq 0$) that allows us to significantly improve the upper bound to $\tilde{\mathcal{O}}(n^3/(\varepsilon_2-\varepsilon_1)^5)$.
PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection

Recently, polar-based representation has shown promising properties in perceptual tasks. In addition to Cartesian-based approaches, which separate point clouds unevenly, representing point clouds as polar grids has been recognized as an alternative due to (1) its advantage in robust performance under different resolutions and (2) its superiority in streaming-based approaches. However, state-of-the-art polar-based detection methods inevitably suffer from the feature distortion problem because of the non-uniform division of polar representation, resulting in a non-negligible performance gap compared to Cartesian-based approaches. To tackle this issue, we present PARTNER, a novel 3D object detector in the polar coordinate. PARTNER alleviates the dilemma of feature distortion with global representation re-alignment and facilitates the regression by introducing instance-level geometric information into the detection head. Extensive experiments show overwhelming advantages in streaming-based detection and different resolutions. Furthermore, our method outperforms the previous polar-based works with remarkable margins of 3.68% and 9.15% on Waymo and ONCE validation set, thus achieving competitive results over the state-of-the-art methods.
Atomistic Theory of Hot-Carrier Relaxation in Large Plasmonic Nanoparticles

Recently, there has been significant interest in harnessing hot-carriers generated from the decay of localized surface plasmons in metallic nanoparticles for applications in photocatalysis, photovoltaics, and sensing. In this work, we develop an atomistic method that makes it possible to predict the population of hot-carriers under continuous wave illumination for large nanoparticles of relevance to experimental studies. For this, we solve the equation of motion of the density matrix, taking into account both the excitation of hot-carriers and subsequent relaxation effects. We present results for spherical Au and Ag nanoparticles with up to 250,000 atoms. We find that the population of highly energetic carriers depends on both the material and the nanoparticle size. We also study the increase in the electronic temperature upon illumination and find that Ag nanoparticles exhibit a much larger temperature increase than Au nanoparticles. Finally, we investigate the effect of using different models for the relaxation matrix but find that the qualitative features of the hot-carrier population are robust. These insights can be harnessed for the design of improved hot-carrier devices.
Semantic Interpretation and Validation of Graph Attention-Based Explanations for GNN Models

In this work, we propose a methodology for investigating the use of semantic attention to enhance the explainability of Graph Neural Network (GNN)-based models. Graph Deep Learning (GDL) has emerged as a promising field for tasks like scene interpretation, leveraging flexible graph structures to concisely describe complex features and relationships. As traditional explainability methods used in eXplainable AI (XAI) cannot be directly applied to such structures, graph-specific approaches are introduced. Attention has been previously employed to estimate the importance of input features in GDL, however, the fidelity of this method in generating accurate and consistent explanations has been questioned. To evaluate the validity of using attention weights as feature importance indicators, we introduce semantically-informed perturbations and correlate predicted attention weights with the accuracy of the model. Our work extends existing attention-based graph explainability methods by analysing the divergence in the attention distributions in relation to semantically sorted feature sets and the behaviour of a GNN model, efficiently estimating feature importance. We apply our methodology on a lidar pointcloud estimation model successfully identifying key semantic classes that contribute to enhanced performance, effectively generating reliable post-hoc semantic explanations.
Quantum Mechanics From Principle of Least Distinguishability

We show that the formulations of non-relativistic quantum mechanics can be derived from the principle of least distinguishability. The principle can be considered as an extension of the least action principle from classical mechanics by factoring in two assumptions. First, the Planck constant deﬁnes the discrete amount of action a physical object needs to exhibit during its dynamics in order to be observable. This enables us to calculate the degree of distinguishability from a classical trajectory. Second, there is constant vacuum ﬂuctuation along a classical trajectory. A novel method is introduced to deﬁne the information metrics to measure additional distinguishability due to vacuum ﬂuctuations. Applying the variation principle to minimize the total degree of distin-guishability allows us to recover the basic quantum formulations including the uncertainty relation and the Schr¨odinger equation in both position and momentum representations. Furthermore, the principle brings in new results on two fronts. At the conceptual level, we ﬁnd that the information metrics for vacuum ﬂuctuations are responsible for manifesting entanglement eﬀects without underlying physical interactions, implying that entanglement eﬀects are non-causal. At the mathematical level, deﬁning the information metrics for vacuum ﬂuctuations using more general deﬁnitions of relative entropy results in a generalized Schr¨odinger equation that depends on the order of relative entropy. The least distinguishability principle is a new mathematical tool, and we expect other advanced quantum formulations can be obtained from it.
Symplectic Discretization Approach for Developing New Proximal Point Algorithms

Proximal point algorithms have found numerous applications in the field of convex optimization, and their accelerated forms have also been proposed. However, the most commonly used accelerated proximal point algorithm was first introduced in 1967, and recent studies on accelerating proximal point algorithms are relatively scarce. In this paper, we propose high-resolution ODEs for the proximal point operators for both closed proper convex functions and maximally monotone operators, and present a Lyapunov function framework to demonstrate that the trajectories of our high-resolution ODEs exhibit accelerated behavior. Subsequently, by symplectically discretizing our high-resolution ODEs, we obtain new proximal point algorithms known as symplectic proximal point algorithms. By decomposing the continuous-time Lyapunov function into its elementary components, we demonstrate that symplectic proximal point algorithms possess $O(1/k^2)$ convergence rates.
Shortcuts of Freely Relaxing Systems Using Equilibrium Physical Observables

Many systems, when initially placed far from equilibrium, exhibit surprising behavior in their attempt to equilibrate. Striking examples are the Mpemba effect and the cooling-heating asymmetry. These anomalous behaviors can be exploited to shorten the time needed to cool down (or heat up) a system. Though, a strategy to design these effects in mesoscopic systems is missing. We bring forward a description that allows us to formulate such strategies, and, along the way, makes natural these paradoxical behaviors. In particular, we study the evolution of macroscopic physical observables of systems freely relaxing under the influence of one or two instantaneous thermal quenches. The two crucial ingredients in our approach are timescale separation and a nonmonotonic temperature evolution of an important state function. We argue that both are generic features near a first-order transition. Our theory is exemplified with the one-dimensional Ising model in a magnetic field using analytic results and numerical experiments.
Real-Time Progressive Learning: Mutually Reinforcing Learning and Control with Neural-Network-Based Selective Memory

—Memory, as the basis of learning, determines the storage, update and forgetting of the knowledge and further determines the efficiency of learning. Featured with a mechanism of memory, a radial basis function neural network (RBFNN) based learning control scheme named real-time progressive learning (RTPL) is proposed to learn the unknown dynamics of the system with guaranteed stability and closed-loop performance. Instead of the stochastic gradient descent (SGD) update law in adaptive neural control (ANC), RTPL adopts the selective memory recursive least squares (SMRLS) algorithm to update the weights of the RBFNN. Through SMRLS, the approximation capabilities of the RBFNN are uniformly distributed over the feature space and thus the passive knowledge forgetting phenomenon of SGD method is suppressed. Subsequently, RTPL achieves the following merits over the classical ANC: 1) guaranteed learning capability under low-level persistent excitation (PE), 2) improved learning performance (learning speed, accuracy and generalization capability), and 3) low gain requirement ensuring robustness of RTPL in practical applications. Moreover, the RTPL based learning and control will gradually reinforce each other during the task execution, making it appropriate for long-term learning control tasks. As an example, RTPL is used to address the tracking control problem of a class of nonlinear systems with RBFNN being an adaptive feedforward controller. Corresponding theoretical analysis and simulation studies demonstrate the effectiveness of RTPL.
FLIPS: Federated Learning using Intelligent Participant Selection

This paper presents the design and implementation of FLIPS, a middleware system to manage data and participant heterogeneity in federated learning (FL) training workloads. In particular, we examine the benefits of label distribution clustering on participant selection in federated learning. FLIPS clusters parties involved in an FL training job based on the label distribution of their data apriori, and during FL training, ensures that each cluster is equitably represented in the participants selected. FLIPS can support the most common FL algorithms, including FedAvg, FedProx, FedDyn, FedOpt and FedYogi. To manage platform heterogeneity and dynamic resource availability, FLIPS incorporates a straggler management mechanism to handle changing capacities in distributed, smart community applications. Privacy of label distributions, clustering and participant selection is ensured through a trusted execution environment (TEE). Our comprehensive empirical evaluation compares FLIPS with random participant selection, as well as three other "smart" selection mechanisms -- Oort [51], TiFL [15] and gradient clustering [27] using four real-world datasets, two different non-IID distributions and three common FL algorithms (FedYogi, FedProx and FedAvg). We demonstrate that FLIPS significantly improves convergence, achieving higher accuracy by 17-20 percentage points with 20-60% lower communication costs, and these benefits endure in the presence of straggler participants.
Hardware Architecture for a Quantum Computer Trusted Execution Environment

The cloud-based environments in which today's and future quantum computers will operate, raise concerns about the security and privacy of user's intellectual property. Quantum circuits submitted to cloud-based quantum computer providers represent sensitive or proprietary algorithms developed by users that need protection. Further, input data is hard-coded into the circuits, and leakage of the circuits can expose users' data. To help protect users' circuits and data from possibly malicious quantum computer cloud providers, this work presented the first hardware architecture for a trusted execution environment for quantum computers. To protect the user's circuits and data, the quantum computer control pulses are obfuscated with decoy control pulses. While digital data can be encrypted, analog control pulses cannot and this paper proposed the novel decoy pulse approach to obfuscate the analog control pulses. The proposed decoy pulses can easily be added to the software by users. Meanwhile, the hardware components of the architecture proposed in this paper take care of eliminating, i.e. attenuating, the decoy pulses inside the superconducting quantum computer's dilution refrigerator before they reach the qubits. The hardware architecture also contains tamper-resistant features to protect the trusted hardware and users' information. The work leverages a new metric of variational distance to analyze the impact and scalability of hardware protection. The variational distance of the circuits protected with our scheme, compared to unprotected circuits, is in the range of only $0.16$ to $0.26$. This work demonstrates that protection from possibly malicious cloud providers is feasible and all the hardware components needed for the proposed architecture are available today.
El factor del talento humano en las organizaciones: Análisis desde una perspectiva crítica.

espanolEl articulo plantea un analisis critico del concepto de talento humano desde la teoria economica empresarial, mostrando los elementos que lo componen y su aplicacion en el campo del desarrollo economico. El mismo forma parte de la reflexion realizada al interior del grupo de investigacion “Nuevas tecnologias, trabajo y gestion”, del programa de Ingenieria Industrial de la Universidad de San Buenaventura (Caliz, Colombia), en relacion con un proyecto de investigacion sobre capacitacion integral del talento humano en las pequenas y medianas empresas. No obstante, se intenta ir mas alla de la descripcion economica o tecnica del concepto para formular un debate etico, sobre las relaciones economicas dentro del sistema economico capitalista globalizado. EnglishThe article presents a critical analysis of the concept of human talent from the business economic theory, showing the component elements and their application in the field of economic development. This article is part of the reflection on a research project about integral training of human talent in small and medium companies, carried out by the research group named “New technologies, work and management”, belonging to the Industrial Engineering program of San Buenaventura University (Caliz, Colombia). However, it attempts to go beyond the economic or technical description of the concept to develop an ethical debate on economic relations within the global capitalist economic system.