Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational visualization of physics and topology in unsteady flow

Vector fields play an important role in many areas in research and engineering. This thesis originated in the context of a project with two industry partners active in turbo-machinery and is hence oriented toward this area. Nevertheless, its first part addresses generic fluid flows whereas its second part is even more universal: its subject are vector fields in general. Design of turbo-machinery looks back on a long history. Although high efficiency is already achieved in many areas, there is still high effort in pushing efficiency some percent further. Although this seems to be a worthless undertaking at first sight, it can substantially account into the return of these machines. Especially in the field of hydropower plants, it pays off due to their typically very long operation duration. The operation of turbines depends on various factors, many of which address to the field of fluid dynamics. For the design, there is a clear trend from experiments toward computational fluid dynamics simulations. This is especially true for these decades since computing power and simulation techniques are coming to a level where experiments are almost dispensable. This leads to the situation that reliable numerical data are easily available but often hard to interpret because of their size and intricacy. This makes computational flow visualization and analysis increasingly important. The main objective of this thesis is to push computational flow visualization further toward the needs and possibilities of modern turbo-machinery design. This thesis focuses on two fields in computational flow visualization: vortices and global structure of flows. Although vortices are beneficial in many areas of nature, from the birth of solar systems over the flight of dragonflies to the physics of our metabolism, they tend to affect technology. Although there are fields in technology that benefit from vortices such as chemical production, they lower performance and lifetime in many others, especially where relatively high velocities are involved, such as automotive engineering and energy conversion. Two approaches are presented in this thesis for the analysis of vortex creation and vortex dynamics in general. The first one bases solely on the instantaneous structure of the curl of the velocity field, the so-called vorticity, whereas the second one examines its transport over time by advection and diffusion. This can help both, the production and avoidance of vortices as well as their control in each sense, depending on the requirements and possibilities. Understanding the global and semi-global structure of flows, or vector fields in general, gives deeper insight into many mechanisms such as transport, mixing, and residence time and therefore allows for more control and hence more focused
The Helios Faraday Rotation Data Archive

The Helios Faraday Rotation (FR) Experiment, a passive radio science investigation requiring no on‐board hardware other than the existing spacecraft radio subsystem, was designed to study the dynamic and quiescent structure of the magnetic fields and electron density in the solar corona. Measurements of coronal Faraday rotation were derived from the linearly polarized S‐band downlink carrier signal, which probed otherwise inaccessible regions of the corona in the radial range from 2 to 15 solar radii during the regularly recurring solar conjunctions. More than 1250 hours of Helios FR data were recorded over the duration of the Helios 1 (1974–84) and Helios 2 (1976–80) missions. The time scales of FR variations provide information on various physical phenomena: (a) slowly‐varying rise and fall associated with the changing ray path offset, combined with the rotation of the quasi‐static corona; (b) ubiquitous random oscillations with higher fluctuation amplitude at smaller solar offset distances, probably ca.
Vertical Federated Linear Contextual Bandits

In this paper, we investigate a novel problem of building contextual bandits in the vertical federated setting, i.e., contextual information is vertically distributed over different departments. This problem remains largely unexplored in the research community. To this end, we carefully design a customized encryption scheme named orthogonal matrix-based mask mechanism(O3M) for encrypting local contextual information while avoiding expensive conventional cryptographic techniques. We further apply the mechanism to two commonly-used bandit algorithms, LinUCB and LinTS, and instan-tiate two practical protocols for online recommendation under the vertical federated setting. The proposed protocols can perfectly recover the service quality of centralized bandit algorithms while achieving a satisfactory runtime efficiency, which is theoretically proved and analyzed in this paper. By conducting extensive experiments on both synthetic and real-world datasets, we show the superiority of the proposed method in terms of privacy protection and recommendation performance.
Discovering Many Diverse Solutions with Bayesian Optimization

Bayesian optimization (BO) is a popular approach for sample-efficient optimization of black-box objective functions. While BO has been successfully applied to a wide range of scientific applications, traditional approaches to single-objective BO only seek to find a single best solution. This can be a significant limitation in situations where solutions may later turn out to be intractable. For example, a designed molecule may turn out to violate constraints that can only be reasonably evaluated after the optimization process has concluded. To address this issue, we propose Rank-Ordered Bayesian Optimization with Trust-regions (ROBOT) which aims to find a portfolio of high-performing solutions that are diverse according to a user-specified diversity metric. We evaluate ROBOT on several real-world applications and show that it can discover large sets of high-performing diverse solutions while requiring few additional function evaluations compared to finding a single best solution.
Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages

Hate speech is a global phenomenon, but most hate speech datasets so far focus on English-language content. This hinders the development of more effective hate speech detection models in hundreds of languages spoken by billions across the world. More data is needed, but annotating hateful content is expensive, time-consuming and potentially harmful to annotators. To mitigate these issues, we explore data-efficient strategies for expanding hate speech detection into under-resourced languages. In a series of experiments with mono- and multilingual models across five non-English languages, we find that 1) a small amount of target-language fine-tuning data is needed to achieve strong performance, 2) the benefits of using more such data decrease exponentially, and 3) initial fine-tuning on readily-available English data can partially substitute target-language data and improve model generalisability. Based on these findings, we formulate actionable recommendations for hate speech detection in low-resource language settings.
Meeting Decision Tracker: Making Meeting Minutes with De-Contextualized Utterances

Meetings are a universal process to make decisions in business and project collaboration. The capability to automatically itemize the decisions in daily meetings allows for extensive tracking of past discussions. To that end, we developed Meeting Decision Tracker, a prototype system to construct decision items comprising decision utterance detector (DUD) and decision utterance rewriter (DUR). We show that DUR makes a sizable contribution to improving the user experience by dealing with utterance collapse in natural conversation. An introduction video of our system is also available at https://youtu.be/TG1pJJo0Iqo.
Quantum simulators at negative absolute temperatures

We propose that negative absolute temperatures in ultracold atomic clouds in optical lattices can be used to simulate quantum systems in new regions of phase diagrams. First we discuss how the attractive SU(3) Hubbard model in three dimensions can be realized using repulsively interacting 173-Yb atoms, then we consider how an antiferromagnetic S=1 spin chain could be simulated using spinor 87-Rb or 23-Na atoms. The general idea to achieve negative absolute temperatures is to reverse the sign of the external harmonic potential. Energy conservation in a deep optical lattice imposes a constraint on the dynamics of the cloud, which can relax toward a T<0 state. As the process is strongly non-adiabatic, we estimate the change of the entropy.
Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization

We propose a framework to enable multipurpose assistive mobile robots to autonomously wipe tables to clean spills and crumbs. This problem is challenging, as it requires planning wiping actions while reasoning over uncertain latent dynamics of crumbs and spills captured via high-dimensional visual observations. Simultaneously, we must guarantee constraints satisfaction to enable safe deployment in unstructured cluttered environments. To tackle this problem, we first propose a stochastic differential equation to model crumbs and spill dynamics and absorption with a robot wiper. Using this model, we train a vision-based policy for planning wiping actions in simulation using reinforcement learning (RL). To enable zero-shot sim-to-real deployment, we dovetail the RL policy with a whole-body trajectory optimization framework to compute base and arm joint trajectories that execute the desired wiping motions while guaranteeing constraints satisfaction. We extensively validate our approach in simulation and on hardware. Video of experiments: https://youtu.be/inORKP4F3EI
Federated Unlearning for On-Device Recommendation

The increasing data privacy concerns in recommendation systems have made federated recommendations attract more and more attention. Existing federated recommendation systems mainly focus on how to effectively and securely learn personal interests and preferences from their on-device interaction data. Still, none of them considers how to efficiently erase a user's contribution to the federated training process. We argue that such a dual setting is necessary. First, from the privacy protection perspective, "the right to be forgotten (RTBF)" requires that users have the right to withdraw their data contributions. Without the reversible ability, federated recommendation systems risk breaking data protection regulations. On the other hand, enabling a federated recommender to forget specific users can improve its robustness and resistance to malicious clients' attacks. To support user unlearning in federated recommendation systems, we propose an efficient unlearning method FRU (Federated Recommendation Unlearning), inspired by the log-based rollback mechanism of transactions in database management systems. It removes a user's contribution by rolling back and calibrating the historical parameter updates and then uses these updates to speed up federated recommender reconstruction. However, storing all historical parameter updates on resource-constrained personal devices is challenging and even infeasible. In light of this challenge, we propose a small-sized negative sampling method to reduce the number of item embedding updates and an importance-based update selection mechanism to store only important model updates. To evaluate the effectiveness of FRU, we propose an attack method to disturb federated recommenders via a group of compromised users. Then, we use FRU to recover recommenders by eliminating these users' influence. Finally, we conduct extensive experiments on two real-world recommendation datasets (i.e. MovieLens-100k and Steam-200k) with two widely used federated recommenders to show the efficiency and effectiveness of our proposed approaches.
Quantum non-demolition photon counting in a 2d Rydberg atom array

Rydberg arrays merge the collective behavior of ordered atomic arrays with the controllability and optical nonlinearities of Rydberg systems, resulting in a powerful platform for realizing photonic many-body physics. As an application of this platform, we propose a protocol for quantum non-demolition (QND) photon counting. Our protocol involves photon storage in the Rydberg array, an observation phase consisting of a series of Rabi flops to a Rydberg state and measurements, and retrieval of the stored photons. The Rabi frequency experiences a $\sqrt{n}$ collective enhancement, where $n$ is the number of photons stored in the array. Projectively measuring the presence or absence of a Rydberg excitation after oscillating for some time is thus a weak measurement of photon number. We demonstrate that the photon counting protocol can be used to distill Fock states from arbitrary pure or mixed initial states and to perform photonic state discrimination. We confirm that the protocol still works in the presence of experimentally realistic noise.
Cosmic bubble and domain wall instabilities II: fracturing of colliding walls

We study collisions between nearly planar domain walls including the effects of small initial nonplanar fluctuations. These perturbations represent the small fluctuations that must exist in a quantum treatment of the problem. In a previous paper, we demonstrated that at the linear level a subset of these fluctuations experience parametric amplification as a result of their coupling to the planar symmetric background. Here we study the full three-dimensional nonlinear dynamics using lattice simulations, including both the early time regime when the fluctuations are well described by linear perturbation theory as well as the subsequent stage of fully nonlinear evolution. We find that the nonplanar fluctuations have a dramatic effect on the overall evolution of the system. Specifically, once these fluctuations begin to interact nonlinearly the split into a planar symmetric part of the field and the nonplanar fluctuations loses its utility. At this point the colliding domain walls dissolve, with the endpoint of this being the creation of a population of oscillons in the collision region. The original (nearly) planar symmetry has been completely destroyed at this point and an accurate study of the system requires the full three-dimensional simulation.
Prospects for a lattice computation of rare kaon decay amplitudes: $K\to\pi\ell^+\ell^-$ decays

The rare kaon decays $K\to\pi\ell^+\ell^-$ and $K\to\pi\nu\bar{\nu}$ are flavor changing neutral current (FCNC) processes and hence promising channels with which to probe the limits of the standard model and to look for signs of new physics. In this paper we demonstrate the feasibility of lattice calculations of $K\to\pi\ell^+\ell^-$ decay amplitudes for which long-distance contributions are very significant. We show that the dominant finite-volume corrections (those decreasing as powers of the volume) are negligibly small and that, in the four-flavor theory, no new ultraviolet divergences appear as the electromagnetic current $J$ and the effective weak Hamiltonian $H_W$ approach each other. In addition, we demonstrate that one can remove the unphysical terms which grow exponentially with the range of the integration over the time separation between $J$ and $H_W$. We will now proceed to exploratory numerical studies with the aim of motivating further experimental measurements of these decays. Our work extends the earlier study by Isidori, Turchetti and Martinelli which focussed largely on the renormalization of ultraviolet divergences. In a companion paper we discuss the evaluation of the long-distance contributions to $K\to\pi\nu\bar{\nu}$ decays; these contributions are expected to be at the level of a few percent for $K^+$ decays.
DEER: Descriptive Knowledge Graph for Explaining Entity Relationships

We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) - an open and informative form of modeling entity relationships. In DEER, relationships between entities are represented by free-text relation descriptions. For instance, the relationship between entities of machine learning and algorithm can be represented as “Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. ” To construct DEER, we propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions with a transformer-based relation description synthesizing model, where no human labeling is required. Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships. The results suggest that we can build an open and informative knowledge graph without human annotation.
R&R: Metric-guided Adversarial Sentence Generation

Adversarial examples are helpful for analyzing and improving the robustness of text classifiers. Generating high-quality adversarial examples is a challenging task as it requires generating fluent adversarial sentences that are semantically similar to the original sentences and preserve the original labels, while causing the classifier to misclassify them. Existing methods prioritize misclassification by maximizing each perturbation's effectiveness at misleading a text classifier; thus, the generated adversarial examples fall short in terms of fluency and similarity. In this paper, we propose a rewrite and rollback (R&R) framework for adversarial attack. It improves the quality of adversarial examples by optimizing a critique score which combines the fluency, similarity, and misclassification metrics. R&R generates high-quality adversarial examples by allowing exploration of perturbations that do not have immediate impact on the misclassification metric but can improve fluency and similarity metrics. We evaluate our method on 5 representative datasets and 3 classifier architectures. Our method outperforms current state-of-the-art in attack success rate by +16.2%, +12.8%, and +14.0% on the classifiers respectively. Code is available at https://github.com/DAI-Lab/fibber
Theoretical Exploration of Flexible Transmitter Model

Neural network models generally involve two important components, i.e., network architecture and neuron model. Although there are abundant studies about network architectures, only a few neuron models have been developed, such as the MP neuron model developed in 1943 and the spiking neuron model developed in the 1950s. Recently, a new bio-plausible neuron model, flexible transmitter (FT) model (Zhang and Zhou, 2021), has been proposed. It exhibits promising behaviors, particularly on temporal–spatial signals, even when simply embedded into the common feedforward network architecture. This article attempts to understand the properties of the FT network (FTNet) theoretically. Under mild assumptions, we show that: 1) FTNet is a universal approximator; 2) the approximation complexity of FTNet can be exponentially smaller than those of commonly used real-valued neural networks with feedforward/recurrent architectures and is of the same order in the worst case; and 3) any local minimum of FTNet is the global minimum, implying that it is possible to identify global minima by local search algorithms.
Dynamics of spin helices in the one-dimensional 
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 model

Motivated by cold-atom experiments and a desire to understand far-from-equilibrium quantum transport, we analytically study the dynamics of spin helices in the one-dimensional $XX$ model. We use a Jordan-Wigner transformation to map the spin chain onto a non-interacting Fermi gas with simple equations of motion. The resulting dynamics are nontrivial, however, as the spin-helix initial condition corresponds to a highly nonequilibrium distribution of the fermions. We find a separation of timescales between the in-plane and out-of-plane spin dynamics. We gain insights from analyzing the case of a uniform spin chain and from a semiclassical model. One of our key findings is that the spin correlation functions decay as $t^{-1/2}$ at long time, in contrast to the experimentally observed exponential decay.
Exact Inference for Stochastic Epidemic Models via Uniformly Ergodic Block Sampling

Stochastic epidemic models provide an interpretable probabilistic description of the spread of a disease through a population. Yet, fitting these models to partially observed data is a notoriously difficult task due to intractability of the likelihood for many classical models. To remedy this issue, this article introduces a novel data-augmented MCMC algorithm for exact Bayesian inference under the stochastic SIR model, given only discretely observed counts of infection. In a Metropolis-Hastings step, the latent data are jointly proposed from a surrogate process carefully designed to closely resemble the SIR model, from which we can efficiently generate epidemics consistent with the observed data. This yields a method that explores the high-dimensional latent space efficiently, and scales to outbreaks with hundreds of thousands of individuals. We show that the Markov chain underlying the algorithm is uniformly ergodic, and validate its performance via thorough simulation experiments and a case study on the 2013-2015 outbreak of Ebola Haemorrhagic Fever in Western Africa.
Information Rate Loss due to Radiation Decoherence

Department of Electrical Engineering, Technische Universiteit Eindhoven, Eindhoven, The Netherlands(Dated: February 1, 2008)The information rates achievable by using electromagnetic radiation aﬀected by thermal noiseand signal decoherence are studied. The standard coherent Gaussian model is compared with analternative photon gas model which represents lack of a shared phase reference between transmitterand receiver. At any frequency, information rates over the photon gas model essentially coincidewith those over the Gaussian model when the signal-to-noise ratio is below a threshold. Only abovethe threshold does decoherence cause a loss in information rates; the loss can amount to half of thecapacity. The threshold exceeds 40dB for radio frequencies and vanishes at optical frequencies.I. INTRODUCTION