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Understanding the dynamics of dissipative quantum systems, particularly beyond the weak coupling approximation, is central to various quantum applications. While numerically exact methods provide accurate solutions, they often lack the analytical insight provided by theoretical approaches. In this study, we employ the recently-developed method dubbed the effective Hamiltonian theory to understand the dynamics of system-bath configurations without resorting to a perturbative description of the system-bath coupling energy. Through a combination of mapping steps and truncation, the effective Hamiltonian theory offers both analytical insights into signatures of strong couplings in open quantum systems and a straightforward path for numerical simulations. To validate the accuracy of the method, we apply it to two canonical models: a single spin immersed in a bosonic bath and two noninteracting spins in a common bath. In both cases, we study the transient regime and the steady state limit at nonzero temperature, and spanning system-bath interactions from the weak to the strong regime. By comparing the results of the effective Hamiltonian theory with numerically exact simulations, we show that although the former overlooks non-Markovian features in the transient equilibration dynamics, it correctly captures non-perturbative bath-generated couplings between otherwise non-interacting spins as observed in their synchronization dynamics and correlations. Altogether, the effective Hamiltonian theory offers a powerful approach to understanding strong coupling dynamics and thermodynamics, capturing the signatures of such interactions in both relaxation dynamics and in the steady state limit., Comment: Journal version

Discrete time crystalline (DTC) phases have attracted significant theoretical and experimental attention in the last few years. Such systems require a seemingly impossible combination of non-adiabatic driving and a finite-entropy long-time state, which is, however, possible in non-ergodic systems. Previous works have often relied on disorder for the required non-ergodicity; here, we describe the construction of a discrete time crystal (DTC) phase in non-disordered, non-integrable Ising-type systems. After discussing the conditions for interacting and periodically driven systems to display such phases in general, we propose a concrete model and then provide approximate analytical arguments and direct numerical evidence that it satisfies the conditions and displays a DTC phase robust to local periodic perturbations., Comment: 5 pages, 3 figures

The IoT market is diverse and characterized by a multitude of vendors that support different device functions (e.g., speaker, camera, vacuum cleaner, etc.). Within this market, IoT security and observability systems use real-time identification techniques to manage these devices effectively. Most existing IoT identification solutions employ machine learning techniques that assume the IoT device, labeled by both its vendor and function, was observed during their training phase. We tackle a key challenge in IoT labeling: how can an AI solution label an IoT device that has never been seen before and whose label is unknown? Our solution extracts textual features such as domain names and hostnames from network traffic, and then enriches these features using Google search data alongside catalog of vendors and device functions. The solution also integrates an auto-update mechanism that uses Large Language Models (LLMs) to update these catalogs with emerging device types. Based on the information gathered, the device's vendor is identified through string matching with the enriched features. The function is then deduced by LLMs and zero-shot classification from a predefined catalog of IoT functions. In an evaluation of our solution on 97 unique IoT devices, our function labeling approach achieved HIT1 and HIT2 scores of 0.7 and 0.77, respectively. As far as we know, this is the first research to tackle AI-automated IoT labeling.

Ambisonics, a popular format of spatial audio, is the spherical harmonic (SH) representation of the plane wave density function of a sound field. Many algorithms operate in the SH domain and utilize the Ambisonics as their input signal. The process of encoding Ambisonics from a spherical microphone array involves dividing by the radial functions, which may amplify noise at low frequencies. This can be overcome by regularization, with the downside of introducing errors to the Ambisonics encoding. This paper aims to investigate the impact of different ways of regularization on Deep Neural Network (DNN) training and performance. Ideally, these networks should be robust to the way of regularization. Simulated data of a single speaker in a room and experimental data from the LOCATA challenge were used to evaluate this robustness on an example algorithm of speaker localization based on the direct-path dominance (DPD) test. Results show that performance may be sensitive to the way of regularization, and an informed approach is proposed and investigated, highlighting the importance of regularization information., Comment: to be published in Icassp 2024

We train a bilingual Arabic-Hebrew language model using a transliterated version of Arabic texts in Hebrew, to ensure both languages are represented in the same script. Given the morphological, structural similarities, and the extensive number of cognates shared among Arabic and Hebrew, we assess the performance of a language model that employs a unified script for both languages, on machine translation which requires cross-lingual knowledge. The results are promising: our model outperforms a contrasting model which keeps the Arabic texts in the Arabic script, demonstrating the efficacy of the transliteration step. Despite being trained on a dataset approximately 60% smaller than that of other existing language models, our model appears to deliver comparable performance in machine translation across both translation directions.

We generalise the Fundamental Theorem of Calculus to higher dimensions. Our generalisation is based on the observation that the antiderivative of a function of $n$-variables is a solution of a partial differential equation of order $n$ generalising the classical case. The generalised Fundamental Theorem of Calculus then states that the $n$-dimensional integrals over $n$-dimensional axis-parallel rectangular hypercuboids is given by a combinatorial formula evaluating the antiderivative on the vertices of the hypercuboid., Comment: 8 pages, 2 figures

Langevin dynamics (LD) is widely used for sampling from distributions and for optimization. In this work, we derive a closed-form expression for the expected loss of preconditioned LD near stationary points of the objective function. We use the fact that at the vicinity of such points, LD reduces to an Ornstein-Uhlenbeck process, which is amenable to convenient mathematical treatment. Our analysis reveals that when the preconditioning matrix satisfies a particular relation with respect to the noise covariance, LD's expected loss becomes proportional to the rank of the objective's Hessian. We illustrate the applicability of this result in the context of neural networks, where the Hessian rank has been shown to capture the complexity of the predictor function but is usually computationally hard to probe. Finally, we use our analysis to compare SGD-like and Adam-like preconditioners and identify the regimes under which each of them leads to a lower expected loss., Comment: Accepted to AAAI-24 main track

In the realm of Graph Neural Networks (GNNs), two exciting research directions have recently emerged: Subgraph GNNs and Graph Transformers. In this paper, we propose an architecture that integrates both approaches, dubbed Subgraphormer, which combines the enhanced expressive power, message-passing mechanisms, and aggregation schemes from Subgraph GNNs with attention and positional encodings, arguably the most important components in Graph Transformers. Our method is based on an intriguing new connection we reveal between Subgraph GNNs and product graphs, suggesting that Subgraph GNNs can be formulated as Message Passing Neural Networks (MPNNs) operating on a product of the graph with itself. We use this formulation to design our architecture: first, we devise an attention mechanism based on the connectivity of the product graph. Following this, we propose a novel and efficient positional encoding scheme for Subgraph GNNs, which we derive as a positional encoding for the product graph. Our experimental results demonstrate significant performance improvements over both Subgraph GNNs and Graph Transformers on a wide range of datasets., Comment: Published at ICML 2024

Berry phases have long been known to significantly alter the properties of periodic systems, resulting in anomalous terms in the semiclassical equations of motion describing wave-packet dynamics. In non-Hermitian systems, generalizations of the Berry connection have been proposed and shown to have novel effects on dynamics and transport. In this work, we consider perturbing fields which are themselves non-Hermitian, in the form of complex external electric fields, which are realizable as gain/loss gradients. We derive the full set of semiclassical equations of motion and show that the anomalous velocity depends not only on the Berry curvature, but on the entirety of the quantum geometric tensor, including the quantum metric. This quantum metric dependent velocity appears regardless of whether the unperturbed Hamiltonian is Hermitian or not. These analytical results are compared with numerical lattice simulations which reveal these anomalous terms even in one-dimension. Our work expands the range of phenomena expected to be detectable in experimental setups, which should be realizable in currently available metamaterials and classical wave systems, including mechanical, acoustic, and optical., Comment: 19 pages, 6 figures

We investigate the fundamental limits of the recently proposed random access coverage depth problem for DNA data storage. Under this paradigm, it is assumed that the user information consists of $k$ information strands, which are encoded into $n$ strands via a generator matrix $G$. During the sequencing process, the strands are read uniformly at random, as each strand is available in a large number of copies. In this context, the random access coverage depth problem refers to the expected number of reads (i.e., sequenced strands) required to decode a specific information strand requested by the user. This problem heavily depends on the generator matrix $G$, and besides computing the expectation for different choices of $G$, the goal is to construct matrices that minimize the maximum expectation over all possible requested information strands, denoted by $T_{\max}(G)$. In this paper, we introduce new techniques to investigate the random access coverage depth problem, capturing its combinatorial nature and identifying the structural properties of generator matrices that are advantageous. We establish two general formulas to determine $T_{\max}(G)$ for arbitrary generator matrices. The first formula depends on the linear dependencies between columns of $G$, whereas the second formula takes into account recovery sets and their intersection structure. We also introduce the concept of recovery balanced codes and provide three sufficient conditions for a code to be recovery balanced. These conditions can be used to compute $T_{\max}(G)$ for various families of codes, such as MDS, simplex, Hamming, and binary Reed-Muller codes. Additionally, we study the performance of modified systematic MDS and simplex matrices, showing that the best results for $T_{\max}(G)$ are achieved with a specific combination of encoded strands and replication of the information strands.

This work presents the first calibration experiment of a 3 MV Tandetron accelerator, FRENA, performed in May 2022. The $^7$Li(p,n) reaction threshold was measured to calibrate the terminal voltage measuring device. A LiF target of thickness 175 $\mu$g/cm$^2$ was used in the experiment. The measured threshold was 1872$\pm$2.7 keV, indicating 6$-$10 keV energy shift.

We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis. To this end, we introduce a Space-Time U-Net architecture that generates the entire temporal duration of the video at once, through a single pass in the model. This is in contrast to existing video models which synthesize distant keyframes followed by temporal super-resolution -- an approach that inherently makes global temporal consistency difficult to achieve. By deploying both spatial and (importantly) temporal down- and up-sampling and leveraging a pre-trained text-to-image diffusion model, our model learns to directly generate a full-frame-rate, low-resolution video by processing it in multiple space-time scales. We demonstrate state-of-the-art text-to-video generation results, and show that our design easily facilitates a wide range of content creation tasks and video editing applications, including image-to-video, video inpainting, and stylized generation., Comment: Webpage: https://lumiere-video.github.io/ | Video: https://www.youtube.com/watch?v=wxLr02Dz2Sc

This note studies monotone Markov chains, a subclass of Markov chains with extensive applications in operations research and economics. While the properties that ensure the global stability of these chains are well studied, their establishment often relies on the fulfillment of a certain splitting condition. We address the challenges of verifying the splitting condition by introducing simple, applicable conditions that ensure global stability. The simplicity of these conditions is demonstrated through various examples including autoregressive processes, portfolio allocation problems and resource allocation dynamics.

The detailed process of preparing enriched $^{108}$Cd targets on mylar and copper backing using the vacuum evaporation technique is described. These targets were employed in an experiment to measure the proton capture cross-section at energies significantly below the Coulomb barrier, for the astrophysical p-process studies. Due to the low melting point and high vapor pressure of cadmium, some adjustments were implemented in the Telemark multipocket e-beam setup. The target thickness was determined through the measurement of alpha particle energy loss from a triple alpha source and also by RBS measurements. The thickness of the $^{108}$Cd films varies between 290 to 660 $\mu$g/cm$^2$, with a non-uniformity of approximately 10$\%$. X-ray Photoelectron Spectroscopy (XPS) and X-ray Fluorescence (XRF) analyses were conducted to examine the presence of impurities and to assess surface morphology, phase, and chemical composition.

The conventional circuit paradigm, utilizing a limited number of gates to construct arbitrary quantum circuits, is hindered by significant noise overhead. For instance, the standard gate paradigm employs two CNOT gates for the partial CPhase rotation in the quantum Fourier transform, even when the rotation angle is very small. In contrast, some quantum computer platforms can directly implement such operations using their native interaction, resulting in considerably shorter and less noisy implementations for small rotation angles. Unfortunately, coherent errors stemming from qubit crosstalk and calibration imperfections render these implementations impractical. In Clifford gates such as the CNOT, these errors can be addressed through Pauli twirling (also known as randomized compiling). However, this technique is not applicable to the non-Clifford native implementations described above. The present work introduces, analyzes, and experimentally demonstrates a technique called `Pseudo Twirling' to address coherent errors in general gates and circuits. Additionally, we experimentally showcase that integrating pseudo twirling with a quantum error mitigation method called `Adaptive KIK' enables the simultaneous mitigation of both noise and coherent errors in non-Clifford gates. Due to its unique features pseudo twirling could become a valuable asset in enhancing the capabilities of both present and future NISQ devices., Comment: V2 contains more analytical results and nicer figures

The integrated Berry curvature is a geometric property that has dramatic implications for material properties. This study investigates the integrated Berry curvature and other contributions to the anomalous Hall effect in CrGeTe$_3$ as a function of pressure. The anomalous Hall effect is absent in the insulating phase of CrGeTe$_3$ and evolves with pressure in a dome-like fashion as pressure is applied. The dome's edges are characterized by Fermi surface deformations, manifested as mixed electron and hole transport. We corroborate the presence of bipolar transport by ab-initio calculations which also predict a nonmonotonic behavior of the Berry curvature as a function of pressure. Quantitative discrepancies between our calculations and experimental results indicate that additional scattering mechanisms, which are also strongly tuned by pressure, contribute to the anomalous Hall effect in CrGeTe$_3$.

Complex quantum systems and their various applications are susceptible to noise of coherent and incoherent nature. Characterization of noise and its sources is an open, key challenge in quantum technology applications, especially in terms of distinguishing between inherent incoherent noise and systematic coherent errors. In this paper, we study a scheme of repeated sequential measurements that enables the characterization of incoherent errors by reducing the effects of coherent errors. We demonstrate this approach using a coherently controlled Nitrogen Vacancy in diamond, coupled to both a natural nuclear spin bath (non-Markovian) and to experimentally controlled relaxation through an optical pumping process (nearly Markovian). Our results show mitigation of coherent errors both for Markovian and Non-Markovian incoherent noise profiles. We apply this scheme to the estimation of the dephasing time ($T_2^*$) due to incoherent noise. We observe an improved robustness against coherent errors in the estimation of dephasing time ($T_2^*$) compared to the standard (Ramsey) measurement.

Neutron deficient nuclei from $^{74}$Se$-^{196}$Hg are thought to be produced by $\gamma$-induced reactions ($\gamma$,n), ($\gamma$,p) and ($\gamma,\alpha$) processes. The relatively high abundance of $^{113}$In odd A $p$-nuclei has inspired to study its production processes. As reaction with $\gamma$-beam is difficult to perform in the laboratory, $\gamma$-induced reaction rate is calculated from the inverse reaction data employing reciprocity theorem. Stacked foil activation method was used to measure the $^{113}$In($\alpha,\gamma$) and $^{113}$In($\alpha$, n) reactions cross-section near the astrophysical energies. Theoretical statistical model calculations were performed with different nuclear input parameters and compared with the experimental results. An appropriate $\alpha$-optical potential has been identified from the ($\alpha,\gamma$) and ($\alpha$, n) fitting, which provides the major source of uncertainty in the statistical model calculations. The other nuclear input parameters like level density, and $\gamma$-ray strength function were also constrained for theoretical calculations. $^{113}$In($\alpha,\gamma$)$^{117}$Sb and $^{117}$Sb($\alpha,\gamma$)$^{113}$In reaction rates were calculated using best-fitted input parameters.

The cross-section measurement of $^{144}$Sm(p, $\gamma$)$^{145}$Eu (T$_{1/2}=$5.93(4) days) reaction has been performed at proton energies around 2.6, 3.1, 3.7, 4.1, 4.2, 4.7, 5.1, 5.5, 5.9, 6.4, 6.8 MeV using the activation technique. These energies correspond to the Gamow window for 3, 4 GK and a partial portion for 2 GK. $^{144}$Sm has been chosen for the present study because of its significantly higher abundance compared to the other neighboring $p$-nuclides (Z $>$ 50). The astrophysical $S-$factor of this reaction has been measured for the first time at E$^{cm}_p=$ 2.57$\pm$0.13 MeV, $S-$ factor$=$2.542$\pm$1.152($\times$10$^{10}$) MeV-b. Cross-section data were compared with the previously measured experimental data from literature and the theoretical predictions obtained using Hauser-Feshbach statistical model codes TALYS 1.96 and NON-SMOKER. A satisfactory agreement between experimental data and theoretical results was observed. Molecular deposition technique was used to prepare the $^{144}$Sm targets having thickness between 100$-$350 $\mu$g/cm$^2$ on Al backing. Obtained results were utilized to predict the reaction rates for $^{144}$Sm (p, $\gamma$) and $^{145}$Eu ($\gamma$, p) reactions using TALYS 1.96 and the reciprocity theorem.