Miracle-dz/UniFAR-FTUs huggingface dataset:
Symmetry-resolved entanglement in fermionic systems with dissipation

We investigate symmetry-resolved entanglement in out-of-equilibrium fermionic systems subject to gain and loss dissipation, which preserves the block-diagonal structure of the reduced density matrix. We derive a hydrodynamic description of the dynamics of several entanglement-related quantities, such as the symmetry-resolved von Neumann entropy and the charge-imbalance-resolved fermionic negativity. We show that all these quantities admit a hydrodynamic description in terms of entangled quasiparticles. While the entropy is dominated by dissipative processes, the resolved negativity is sensitive to the presence of entangled quasiparticles, and it shows the typical ‘rise and fall’ dynamics. Our results hold in the weak-dissipative hydrodynamic limit of large intervals, long times and weak dissipation rates.
Orbits of the Backward Shifts with limit points

We show that the bilateral backward shift on $\ell^p(\mathbb{Z},\omega)$ that has a projective orbit with a non-zero limit point is supercyclic. This phenomenon holds also for $\Gamma$-supercyclicity, which extends a result obtained for the first time by Chan and Seceleanu. Moreover, we show that if $K$ is a compact subset of $\ell^p(\mathbb{N},\omega)$ such that its orbit under the unilateral backward shift $B$ on $\ell^p(\mathbb{N},\omega)$ has a non-zero weak limit point, then $B$ is hypercyclic.
Digital laguerre filters

This paper presents the theory and a time domain synthesis procedure for a class of orthogonal digital filters. These filters are derived from discrete Laguerre polynomials and in the case of exact representation possess an infinitely long structure whilst exhibiting an infinite impulse response. In practice the desired impulse response of a filter to be synthesized is truncated in time whilst speed and economic considerations impose a constraint on its length. By the nature of these filters, very few stages usually suffice to yield excellent fidelity in most practical cases. The filters, whose cascaded stages are eminently suited to multiplexing, are inherently stable. A computer-aided design algorithm using a Fibonacci search algorithm is presented for optimizing the practical case having finite length and span. Two examples illustrate the procedure.
On the Bit Error Performance of OTFS Modulation using Discrete Zak Transform

In orthogonal time frequency space (OTFS) modulation, Zak transform approach is a natural approach for converting information symbols multiplexed in the DD domain directly to time domain for transmission, and vice versa at the receiver. Past research on OTFS has primarily considered a two-step approach where DD domain symbols are first converted to time-frequency domain which are then converted to time domain for transmission, and vice versa at the receiver. The Zak transform approach can offer performance and complexity benefits compared to the two-step approach. This paper presents an early investigation on the bit error performance of OTFS realized using discrete Zak transform (DZT). We develop a compact DD domain input-output relation for DZT-OTFS using matrix decomposition that is valid for both integer and fractional delay-Dopplers. We analyze the bit error performance of DZT-OTFS using pairwise error probability analysis and simulations. Simulation results show that 1) both DZT-OTFS and two-step OTFS perform better than orthogonal frequency division multiplexing (OFDM), and 2) DZT-OTFS achieves better performance compared to two-step OTFS over a wide range of Doppler spreads.
Universal Approximation Property of Hamiltonian Deep Neural Networks

This letter investigates the universal approximation capabilities of Hamiltonian Deep Neural Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary Differential Equations. Recently, it has been shown that HDNNs enjoy, by design, non-vanishing gradients, which provide numerical stability during training. However, although HDNNs have demonstrated state-of-the-art performance in several applications, a comprehensive study to quantify their expressivity is missing. In this regard, we provide a universal approximation theorem for HDNNs and prove that a portion of the flow of HDNNs can approximate arbitrary well any continuous function over a compact domain. This result provides a solid theoretical foundation for the practical use of HDNNs.
Scattering and conversion of electromagnetic and gravitational waves by Reissner-Nordström black holes: The Regge pole description

We investigate the problem of scattering and conversion of monochromatic planar gravitational and electromagnetic waves impinging upon a Reissner-Nordstr\"om black hole using a Regge pole description, i.e., a complex angular momentum approach. For this purpose, we first compute numerically the Regge pole spectrum for various charge-to-mass ratio configurations. We then derive an asymptotic expressions for the lowest Regge poles, and by considering Bohr-Sommerfeld-type quantization conditions, obtain the spectrum of weakly damped quasinormal frequencies from the Regge trajectories. Next, we construct the scattering and conversion amplitudes as well as the total differential cross sections for different processes using both a complex angular momentum representation and a partial wave expansion method. Finally, we provide an analytical approximation of the scattering and conversion cross sections of different processes from asymptotic expressions for the lowest Regge poles and the associated residues based on the correspondence Regge poles,"surface waves"propagating close to the photon (graviton) sphere. This allows us to extract the physical interpretation encoded in the partial wave expansions in the high-frequency regime (i.e., in the short-wavelength regime), and to describe semiclassically with very good agreement both black hole glory and a large part of the orbiting oscillations, thus unifying these two phenomena from a purely wave point of view.
Online conspiracy communities are more resilient to deplatforming

Abstract Online social media foster the creation of active communities around shared narratives. Such communities may turn into incubators for conspiracy theories—some spreading violent messages that could sharpen the debate and potentially harm society. To face these phenomena, most social media platforms implemented moderation policies, ranging from posting warning labels up to deplatforming, i.e. permanently banning users. Assessing the effectiveness of content moderation is crucial for balancing societal safety while preserving the right to free speech. In this article, we compare the shift in behavior of users affected by the ban of two large communities on Reddit, GreatAwakening and FatPeopleHate, which were dedicated to spreading the QAnon conspiracy and body-shaming individuals, respectively. Following the ban, both communities partially migrated to Voat, an unmoderated Reddit clone. We estimate how many users migrate, finding that users in the conspiracy community are much more likely to leave Reddit altogether and join Voat. Then, we quantify the behavioral shift within Reddit and across Reddit and Voat by matching common users. While in general the activity of users is lower on the new platform, GreatAwakening users who decided to completely leave Reddit maintain a similar level of activity on Voat. Toxicity strongly increases on Voat in both communities. Finally, conspiracy users migrating from Reddit tend to recreate their previous social network on Voat. Our findings suggest that banning conspiracy communities hosting violent content should be carefully designed, as these communities may be more resilient to deplatforming.
NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation

In this paper, we propose NUWA-XL, a novel Diffusion over Diffusion architecture for eXtremely Long video generation. Most current work generates long videos segment by segment sequentially, which normally leads to the gap between training on short videos and inferring long videos, and the sequential generation is inefficient. Instead, our approach adopts a “coarse-to-fine” process, in which the video can be generated in parallel at the same granularity. A global diffusion model is applied to generate the keyframes across the entire time range, and then local diffusion models recursively fill in the content between nearby frames. This simple yet effective strategy allows us to directly train on long videos (3376 frames) to reduce the training-inference gap and makes it possible to generate all segments in parallel. To evaluate our model, we build FlintstonesHD dataset, a new benchmark for long video generation. Experiments show that our model not only generates high-quality long videos with both global and local coherence, but also decreases the average inference time from 7.55min to 26s (by 94.26%) at the same hardware setting when generating 1024 frames. The homepage link is [NUWA-XL](https://msra-nuwa.azurewebsites.net)
A simulation framework of procurement operations in the container logistics industry

This study proposes a simulation framework of procurement operations in the container logistics industry that can support the development of dynamic procurement strategies. The idea is inspired by the success of Passenger Origin-Destination Simulator (PODS) in the field of airline revenue management. By and large, research in procurement has focused on the optimisation of purchasing decisions, i.e., when-to-order and supplier selection, but a principled approach to procurement operations is lacking. We fill this gap by developing a probabilistic model of a procurement system. A discrete-event simulation logic is used to drive the evolution of the system. In a small case study, we use the simulation to deliver insights by comparing different supplier selection policies in a dynamic spot market environment. Policies based on contextual multi-armed bandits are seen to be robust to limited access to the information that determines the distribution of the outcome. This paper provides a pool of modelling ideas for simulation and observational studies. Moreover, the probabilistic formulation paves the way for advanced machine learning techniques and data-driven optimisation in procurement.
Quantum and Classical Contributions to Entropy Production in Fermionic and Bosonic Gaussian Systems

As previously demonstrated, the entropy production -- a key quantity characterizing the irreversibility of thermodynamic processes -- is related to generation of correlations between degrees of freedom of the system and its thermal environment. This raises the question of whether such correlations are of a classical or quantum nature, namely, whether they are accessible through local measurements on the correlated degrees of freedom. We address this problem by considering fermionic and bosonic Gaussian systems. We show that for fermions the entropy production is mostly quantum due to the parity superselection rule which restricts the set of physically allowed measurements to projections on the Fock states, thus significantly limiting the amount of classically accessible correlations. In contrast, in bosonic systems a much larger amount of correlations can be accessed through Gaussian measurements. Specifically, while the quantum contribution may be important at low temperatures, in the high temperature limit the entropy production corresponds to purely classical position-momentum correlations. Our results demonstrate an important difference between fermionic and bosonic systems regarding the existence of a quantum-to-classical transition in the microscopic formulation of the entropy production. They also show that entropy production can be mainly caused by quantum correlations even in the weak coupling limit, which admits a description in terms of classical rate equations for state populations, as well as in the low particle density limit, where the transport properties of both bosons and fermions converge to those of classical particles.
Phonon-Assisted Tunneling through Quantum Dot Systems Connected to Majorana Bound States

We theoretically analyze phonon-assisted tunneling transport in a quantum dot side connected to a Majorana bound state in a topological superconducting nanowire. We investigate the behavior of the current through the dot, for a range of experimentally relevant parameters, in the presence of one long-wave optical phonon mode. We consider the current-gate voltage, the current-bias voltage and the current-dot–Majorana coupling characteristics under the influence of the electron–phonon coupling. In the absence of electron–phonon interaction, the Majorana bound states suppress the current when the gate voltage matches the Fermi level, but the increase in the bias voltage counteracts this effect. In the presence of electron–phonon coupling, the current behaves similarly as a function of the renormalized gate voltage. As an added feature at large bias voltages, it presents a dip or a plateau, depending on the size of the dot–Majorana coupling. Lastly, we show that the currents are most sensitive to, and depend non-trivially on the parameters of the Majorana circuit element, in the regime of low temperatures combined with low voltages. Our results provide insights into the complex physics of quantum dot devices used to probe Majorana bound states.
3D Radiation-hydrodynamic Simulations Resolving Interior of Rapidly Accreting Primordial Protostar

Direct collapse of supermassive stars is a possible pathway to form supermassive black hole seeds at high redshifts. Whereas previous three-dimensional (3D) simulations demonstrate that supermassive stars form via rapid mass accretion, those resolving the stellar interior have been limited. Here, we report 3D radiation-hydrodynamic (RHD) simulations following the evolution of rapidly accreting protostars resolving the stellar interior. We use an adaptive mesh refinement code with our newly developed RHD solver employing an explicit M1 closure method. We follow the early evolution until the stellar mass reaches ∼10 M ⊙ from two different initial configurations of spherical and turbulent clouds. We demonstrate that, in both cases, a swollen protostar whose radius is 100–1000 R ⊙ appears, as predicted by the stellar evolution calculations. Its effective temperature remains a few thousand Kelvin, and the radiative feedback by ionizing photons is too weak to disturb the accretion flow up to the epoch examined in this work. In the turbulent case, the protostar rotates rapidly at more than 0.4 times the Keplerian velocity owing to the angular momentum provided by the initial turbulence. The protostar approximates an oblate spheroid, and its equatorial radius is more than twice the polar radius. Our results suggest that we need to consider the rapid stellar rotation to elucidate the realistic 3D protostellar evolution in the supermassive star formation.
Revisiting DeepFool: generalization and improvement

Deep neural networks have been known to be vulnerable to adversarial examples, which are inputs that are modified slightly to fool the network into making incorrect predictions. This has led to a significant amount of research on evaluating the robustness of these networks against such perturbations. One particularly important robustness metric is the robustness to minimal l2 adversarial perturbations. However, existing methods for evaluating this robustness metric are either computationally expensive or not very accurate. In this paper, we introduce a new family of adversarial attacks that strike a balance between effectiveness and computational efficiency. Our proposed attacks are generalizations of the well-known DeepFool (DF) attack, while they remain simple to understand and implement. We demonstrate that our attacks outperform existing methods in terms of both effectiveness and computational efficiency. Our proposed attacks are also suitable for evaluating the robustness of large models and can be used to perform adversarial training (AT) to achieve state-of-the-art robustness to minimal l2 adversarial perturbations.
Challenges and opportunities for machine learning in multiscale computational modeling

Many mechanical engineering applications call for multiscale computational modeling and simulation. However, solving for complex multiscale systems remains computationally onerous due to the high dimensionality of the solution space. Recently, machine learning (ML) has emerged as a promising solution that can either serve as a surrogate for, accelerate or augment traditional numerical methods. Pioneering work has demonstrated that ML provides solutions to governing systems of equations with comparable accuracy to those obtained using direct numerical methods, but with significantly faster computational speed. These high-speed, high-fidelity estimations can facilitate the solving of complex multiscale systems by providing a better initial solution to traditional solvers. This paper provides a perspective on the opportunities and challenges of using ML for complex multiscale modeling and simulation. We first outline the current state-of-the-art ML approaches for simulating multiscale systems and highlight some of the landmark developments. Next, we discuss current challenges for ML in multiscale computational modeling, such as the data/discretization dependence, interpretability, data sharing and collaborative platform development. Finally, we suggest several potential research directions for the future.
Sparks of Artificial General Intelligence: Early experiments with GPT-4

Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.
Maximizing the Information Transfer in a Quantum Limited Light Scattering System

The purpose of the light scattering systems considered here is to obtain information about the macroscopic movements of the scatterer. We shall here be confined to systems based on quasi-elastic scattering - i.e. systems where any change in the frequency of the scattered light is caused only by possible macroscopic motions of the scatterer. A light scattering system can conveniently be divided into three parts (Fig. 1): a transmitter, a receiver, and a signal processor. The transmitter generates a characteristic field (or intensity) distribution in the measuring volume. The receiver collects the scattered light and converts it to an electronic signal (or signals). On the basis of the temporal characteristics of the electronic signal the signal processor will give estimates of the dynamics (e.g. velocity) of the scatterer. The spatial intensity distribution as seen by the receiver when a point scatterer (or set of scatterers) moves through the measuring volume defines the code of the instrument. The code defines how the information about the dynamics of the scatterer is encoded.
New Results on the Identification of Normal Modes from Experimental Complex Modes

Abstract Complex modes are associated to first-order representations of structural dynamics and normal (real) modes to undamped second-order models. As normal modes are needed for comparison with undamped finite element models, their determination is a key issue. Damping introduces coupling between the normal modes so that, in general there is no simple relation between normal and complex modes. The complex modes of a second-order model are said to be complete. It is shown that a set of complex modes is complete if it verifies a defined properness condition which is used to find complete approximations of identified complex modes. The possibility of finding a complete approximation of a set of complex modes is linked to damping properties. It is shown that the traditional assumption of proportional damping can be extended to groups of vectors and that the associated separation criterion enables the selection of groups of complex modes for which a complete approximation can be found. Co-ordinate systems used to represent the mass, damping and stiffness properties determined by the proposed approach are also discussed. An experimental application of the proposed methodology is made using six independent tests of the MIT/SERC interferometer testbed. The results clearly demonstrate applicability to real structures with high modal densities and significant non-proportional damping.
Evolutionary dynamics of a lattice dimer: a toy model for stability vs. affinity trade-offs in proteins

Understanding how a stressor applied on a biological system shapes its evolution is key to achieving targeted evolutionary control. Here we present a toy model of two interacting lattice proteins to quantify the response to the selective pressure defined by the binding energy. We generate sequence data of proteins and study how the sequence and structural properties of dimers are affected by the applied selective pressure, both during the evolutionary process and in the stationary regime. In particular we show that internal contacts of native structures lose strength, while inter-structure contacts are strengthened due to the folding-binding competition. We discuss how dimerization is achieved through enhanced mutability on the interacting faces, and how the designability of each native structure changes upon introduction of the stressor.
Topological Hall effect in CeAlGe

The Weyl semimetal CeAlGe is a promising material to study nontrivial topologies in real and momentum space due to the presence of a topological magnetic phase. Our results at ambient pressure show that the electronic properties of CeAlGe are extremely sensitive to small stoichiometric variations. In particular, the topological Hall effect (THE) present in CeAlGe is absent in some samples of almost identical chemical composition. The application of external pressure favors the antiferromagnetic ground state. It also induces a THE where it was not visible at ambient pressure. Furthermore, a small pressure is sufficient to drive the single region of the THE in magnetic fields into two different ones. Our results reveal an extreme sensitivity of the electronic properties of CeAlGe to tiny changes in its chemical composition, leading to a high tunability by external stimuli. We can relate this sensitivity to a shift in the Fermi level and to domain walls.