Miracle-dz/UniFAR-FTUs huggingface dataset:
Instability of an active fluid jet

The breakup of a fluid jet into droplets has long fascinated natural scientists, with early research dating back to the 19th century. Infinitesimal perturbations to a jet grow because of surface tension, which eventually leads to breakup of the jet into droplets (Rayleigh-Plateau instability). Although this classical phenomenon has long been studied, it is not clear how it is modified when the fluid is replaced by an active fluid. In this study, we investigate instabilities of a jet of an active fluid. The active fluid is modelled by a suspension of microswimmers that propel themselves by generating surface tangential velocities, i.e. squirmers. The squirmers are assumed to be bottom-heavy and heavier than the surrounding fluid, so that a downward jet of the active fluid self-assembles under gravity. Hydrodynamic interactions between squirmers are computed using the Stokesian dynamics method, in which near-field hydrodynamics are accurately calculated. We find that jets of active fluids are unstable, with different unstable modes between pullers and pushers. For an active fluid of pullers, the jet breaks up into droplets in a varicose manner reminiscent of a Newtonian fluid. For pushers, on the other hand, the jet buckles and undergoes a waving (sinuous) instability. The physical mechanisms for these two instabilities can be understood by an inspection of the stress fields in the jets and parametric study reveals the importance of hydrodynamic interactions in the instabilities. Although both gravity and bottom-heaviness play an essential role in realizing the downward jet, their influence on the instability is found to be limited. Our findings help reveal new features of the collective properties of active fluids.
Pseudo-Conformal Sound Speed in the Core of Compact Stars

By implementing the putative “hadron-quark continuity" conjectured in QCD in terms of skyrmion-half-skyrmion topological change in an effective field theory for dense matter, we argue that (quasi-)baryons could “masquerade" deconfined quarks in the interior of compact stars. We interpret this phenomenon as a consequence of possible interplay between hidden scale symmetry and hidden local symmetry at high density. A surprising spin-off of the emerging symmetry that we call “pseudo-conformality" is that the long-standing puzzle of the quenched gA≈1 in nuclei can be given a simple resolution by the way the hidden symmetries impact nuclear dynamics at low density.
Orthogonal Layers of Parallelism in Large-Scale Eigenvalue Computations

We address the communication overhead of distributed sparse matrix-(multiple)-vector multiplication in the context of large-scale eigensolvers, using filter diagonalization as an example. The basis of our study is a performance model, which includes a communication metric that is computed directly from the matrix sparsity pattern without running any code. The performance model quantifies to which extent scalability and parallel efficiency are lost due to communication overhead. To restore scalability, we identify two orthogonal layers of parallelism in the filter diagonalization technique. In the horizontal layer the rows of the sparse matrix are distributed across individual processes. In the vertical layer bundles of multiple vectors are distributed across separate process groups. An analysis in terms of the communication metric predicts that scalability can be restored if, and only if, one implements the two orthogonal layers of parallelism via different distributed vector layouts. Our theoretical analysis is corroborated by benchmarks for application matrices from quantum and solid state physics, road networks, and nonlinear programming. We finally demonstrate the benefits of using orthogonal layers of parallelism with two exemplary application cases—an exciton and a strongly correlated electron system—which incur either small or large communication overhead.
Nonlinear transport in the presence of a local dissipation

We characterize the particle transport, particle loss, and nonequilibrium steady states in a dissipative one-dimensional lattice connected to reservoirs at both ends. The free-fermion reservoirs are fixed at different chemical potentials, giving rise to particle transport. The dissipation is due to a local particle loss acting on the center site. We compute the conserved current and loss current as functions of voltage in the nonlinear regime using a Keldysh description. The currents show step-like features which are affected differently by the local loss: The steps are either smoothened, nearly unaffected, or even enhanced, depending on the spatial symmetry of the single-particle eigenstate giving rise to the step. Additionally, we compute the particle density and momentum distributions in the chain. At a finite voltage, two Fermi momenta can occur, connected to different wavelengths of Friedel oscillations on either side of the lossy site. We find that the wavelengths are determined by the chemical potentials in the reservoirs rather than the average density in the lattice.
Annual Parallax of the K-Type Star System IRAS 22480+6002 Measured with VERA

We present astrometric VLBI observations of H2O masers associated with IRAS 22480+6002 (= IRC +60370, hereafter I22480) with the VLBI Exploration of Radio Astrometry (VERA). The stellar type of I22480 looks unusual in stellar maser sources, and has been debated since the 1970’s. We successfully determined that an annual parallax of a group of H2O maser spots is � = 0.400 ˙ 0.025 mas, corresponding to a distance to I22480 of D = 2.50 +0:17 � 0:15 kpc. This suggests that an estimated bolometric luminosity of I22480 should be revised to 35000 Lˇ, preferring a K-type supergiant to an RV Tau-type variable star, previously suggested. Although its spectral type is unusual in stellar maser sources, internal motions of H2O maser features suggest that the H2O masers are associated —
Spatial correlations of dark energy from quantum fluctuations during inflation

This paper contains a detailed study of the properties of a simple model attempting to explain dark energy as originated from quantum fluctuations of a light spectator scalar field in inflation. In [1] we recently outlined how Starobinsky’s stochastic formalism can be used to study the spatial correlations imprinted on dark energy by its quantum origin in this model and we studied their possible role in relieving the Hubble tension. Here we provide a more comprehensive derivation of the results in [1] and we refine some of our estimates, comparing to the approximate results obtained previously. Among the main results, we analyze the non-coincident correlators predicted by a full field theoretical treatment and their relation with those computed within the stochastic formalism. We find that in the region where stochastic theory predicts significant sub-Hubble correlators it is in disagreement with field theoretical predictions. However, agreement can be restored by introducing a reduced speed of sound for the scalar field. We also discuss an alternative approach to the problem of studying correlators within the stochastic formalism based directly on the evolution of probability distributions. Remarkably we find that the two approaches give the same answer for 2-point functions of the field, but not for 4-point functions relevant to density correlators and we discuss the behaviour of the two methods with respect to Wick’s theorem. e-mail: e.belgacem@uu.nl e-mail: t.prokopec@uu.nl ar X iv :2 20 9. 01 60 1v 1 [ gr -q c] 4 S ep 2 02 2
Subtracting compact binary foregrounds to search for subdominant gravitational-wave backgrounds in next-generation ground-based observatories

Stochastic gravitational-wave backgrounds (SGWBs) derive from the superposition of numerous individually unresolved gravitational-wave (GW) signals. Detecting SGWBs provides us with invaluable information about astrophysics, cosmology, and fundamental physics. In this paper, we study SGWBs from binary black-hole (BBH) and binary neutron-star (BNS) coalescences in a network of next-generation ground-based GW observatories (Cosmic Explorer and Einstein Telescope) and determine how well they can be measured; this then limits how well we can observe other subdominant astrophysical and cosmological SGWBs. We simulate all-Universe populations of BBHs and BNSs and calculate the corresponding SGWBs, which consist of a superposition of (i) undetected signals, and (ii) the residual background from imperfect removal of resolved sources. The sum of the two components sets the sensitivity for observing other SGWBs. Our results show that, even with next-generation observatories, the residual background is large and limits the sensitivity to other SGWBs. The main contributions to the residual background arise from uncertainties in inferring the coalescence phase and luminosity distance of the detected signals. Alternative approaches to signal subtraction would need to be explored to minimize the BBH and BNS foreground in order to observe SGWBs from other subdominant astrophysical and cosmological sources.
Analyzing Transformers in Embedding Space

Understanding Transformer-based models has attracted significant attention, as they lie at the heart of recent technological advances across machine learning. While most interpretability methods rely on running models over inputs, recent work has shown that a zero-pass approach, where parameters are interpreted directly without a forward/backward pass is feasible for some Transformer parameters, and for two-layer attention networks. In this work, we present a theoretical analysis where all parameters of a trained Transformer are interpreted by projecting them into the embedding space, that is, the space of vocabulary items they operate on. We derive a simple theoretical framework to support our arguments and provide ample evidence for its validity. First, an empirical analysis showing that parameters of both pretrained and fine-tuned models can be interpreted in embedding space. Second, we present two applications of our framework: (a) aligning the parameters of different models that share a vocabulary, and (b) constructing a classifier without training by “translating” the parameters of a fine-tuned classifier to parameters of a different model that was only pretrained. Overall, our findings open the door to interpretation methods that, at least in part, abstract away from model specifics and operate in the embedding space only.
Advancing Reacting Flow Simulations with Data-Driven Models

The use of machine learning algorithms to predict behaviors of complex systems is booming. However, the key to an effective use of machine learning tools in multi-physics problems, including combustion, is to couple them to physical and computer models. The performance of these tools is enhanced if all the prior knowledge and the physical constraints are embodied. In other words, the scientific method must be adapted to bring machine learning into the picture, and make the best use of the massive amount of data we have produced, thanks to the advances in numerical computing. The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems. Examples of feature extraction in turbulent combustion data, empirical low-dimensional manifold (ELDM) identification, classification, regression, and reduced-order modeling are provided.
Extremely Inclined Orbit of the S-type Planet γ Cep Ab Induced by the Eccentric Kozai–Lidov Mechanism

γ Cep Ab is a typical S-type planet, which occupies a nearly perpendicular planetary orbit relative to the binary. Here, we use a Markov Chain Monte Carlo sampler to conduct a full N-body fitting and derive self-consistent orbital solutions for this hierarchical system. We then employ the eccentric Kozai–Lidov (EKL) mechanism to explain the extremely inclined orbit of the S-type planet γ Cep Ab. The EKL mechanism plays an essential part in our exploration of the significant oscillations of the mutual inclination i mut between the planet and the secondary star. We perform a qualitative analysis and use extensive numerical integrations to investigate the flip conditions and timescales of γ Cep Ab’s orbit. When the planetary mass is 15 M Jup, the planet can reach i mut ∼ 113° given the critical initial conditions of i mut < 60° and e 1 < 0.7. The timescale for the first orbital flip decreases with the increase of the perturbation Hamiltonian. The flipping orbits of γ Cep Ab are confirmed to have a large possibility of remaining stable, based on surfaces of section and the secular stability criterion. Furthermore, we extend the application of EKL to general S-type planetary systems with a 1/a 2 ≤ 0.1, where the most intense excitation of i mut occurs when a 1/a 2 = 0.1 and e 2 ∼ 0.8, and the variation in planetary mass mainly affects the flip possibility where e 1 ≤ 0.3.
vieCap4H-VLSP 2021: Vietnamese Image Captioning for Healthcare Domain using Swin Transformer and Attention-based LSTM

This study presents our approach on the automatic Vietnamese image captioning for healthcare domain in text processing tasks of Vietnamese Language and Speech Processing (VLSP) Challenge 2021, as shown in Figure~\ref{fig:example}. In recent years, image captioning often employs a convolutional neural network-based architecture as an encoder and a long short-term memory (LSTM) as a decoder to generate sentences. These models perform remarkably well in different datasets. Our proposed model also has an encoder and a decoder, but we instead use a Swin Transformer in the encoder, and a LSTM combined with an attention module in the decoder. The study presents our training experiments and techniques used during the competition. Our model achieves a BLEU4 score of 0.293 on the vietCap4H dataset, and the score is ranked the 3$^{rd}$ place on the private leaderboard. Our code can be found at \url{https://git.io/JDdJm}.
Learning from a Biased Sample

The empirical risk minimization approach to data-driven decision making assumes that we can learn a decision rule from training data drawn under the same conditions as the ones we want to deploy it in. However, in a number of settings, we may be concerned that our training sample is biased, and that some groups (characterized by either observable or unobservable attributes) may be under- or over-represented relative to the general population; and in this setting empirical risk minimization over the training set may fail to yield rules that perform well at deployment. We propose a model of sampling bias called $\Gamma$-biased sampling, where observed covariates can affect the probability of sample selection arbitrarily much but the amount of unexplained variation in the probability of sample selection is bounded by a constant factor. Applying the distributionally robust optimization framework, we propose a method for learning a decision rule that minimizes the worst-case risk incurred under a family of test distributions that can generate the training distribution under $\Gamma$-biased sampling. We apply a result of Rockafellar and Uryasev to show that this problem is equivalent to an augmented convex risk minimization problem. We give statistical guarantees for learning a model that is robust to sampling bias via the method of sieves, and propose a deep learning algorithm whose loss function captures our robust learning target. We empirically validate our proposed method in simulations and a case study on ICU length of stay prediction.
Instance attack: an explanation-based vulnerability analysis framework against DNNs for malware detection

Deep neural networks (DNNs) are increasingly being used in malware detection and their robustness has been widely discussed. Conventionally, the development of an adversarial example generation scheme for DNNs involves either detailed knowledge concerning the model (i.e., gradient-based methods) or a substantial quantity of data for training a surrogate model. However, under many real-world circumstances, neither of these resources is necessarily available. Our work introduces the concept of the instance-based attack, which is both interpretable and suitable for deployment in a black-box environment. In our approach, a specific binary instance and a malware classifier are utilized as input. By incorporating data augmentation strategies, sufficient data are generated to train a relatively simple and interpretable model. Our methodology involves providing explanations for the detection model, which entails displaying the weights assigned to different components of the specific binary. Through the analysis of these explanations, we discover that the data subsections have a significant impact on the identification of malware. In this study, a novel function preserving transformation algorithm designed specifically for data subsections is introduced. Our approach involves leveraging binary diversification techniques to neutralize the effects of the most heavily-weighted section, thus generating effective adversarial examples. Our algorithm can fool the DNNs in certain cases with a success rate of almost 100%. Instance attack exhibits superior performance compared to the state-of-the-art approach. Notably, our technique can be implemented in a black-box environment and the results can be verified utilizing domain knowledge. The model can help to improve the robustness of malware detectors.
Dynamic Optical Path Provisioning for Alien Access Links: Architecture, Demonstration, and Challenges

With the spread of datacenter interconnect (DCI) and private 5G, there is a growing demand for dynamically established connections between customer locations via high-capacity optical links. However, link parameters, such as signal power profile and amplifier gains, are often unknown and have to be measured by experts, which prevents dynamic path provisioning because of time-consuming manual measurements. Several techniques have been proposed for estimating the unknown parameters of such alien access links; however, no work has presented the architecture and protocol driving the estimation techniques to establish an optical path between the customer locations. This study aims to automatically connect customer-owned transceivers via alien access links with maximum data rate. First, we propose an architecture and protocol for cooperative optical path design between a customer and carrier, utilizing a state-of-the-art technique for estimating link parameters. Second, we implement the proposed protocol in a soft-ware-defined networking controller and white-box transponders using an open API. The experiments demonstrate that the optical path is dynamically established via alien access links in 137 seconds from the transceiver's cold start. Finally, we discuss the possible reduction of required OSNR margin with this method and the remaining issues.
The Stellar-mass Function of Long Gamma-Ray Burst Host Galaxies

Long gamma-ray bursts (GRBs) have been discussed as a potential tool to probe the cosmic star formation rate (SFR) for a long time. Some studies found an enhancement in the GRB rate relative to the galaxy-inferred SFR at high redshifts, which indicates that GRBs may not be good tracers of star formation. However, in these studies, the GRB rate measured at any redshift is an average over all galaxies at that epoch. A deep understanding of the connection between GRB production and environment also needs to characterize the population of GRB host galaxies directly. Based on a complete sample of GRB hosts, we constrain the stellar-mass function (SMF) of GRB hosts, and examine redshift evolution in the GRB host population. Our results confirm that a strong redshift evolution in energy (with an evolution index of δ=2.47−0.89+0.73 ) or in density ( δ=1.82−0.59+0.22 ) is needed in order to account for the observations. The GRB host SMF can be well described by the Schechter function with a power-law index ξ ≈ −1.10 and a break mass M b,0 ≈ 4.9 × 1010 M ⊙, independent of the assumed evolutionary effects. This is the first formulation of the GRB host SMF. The observed discrepancy between the GRB rate and the galaxy-inferred SFR may also be explained by an evolving SMF.
Graphon-valued processes with vertex-level fluctuations

We consider a class of graph-valued stochastic processes in which each vertex has a type that fluctuates randomly over time. Collectively, the paths of the vertex types up to a given time determine the probabilities that the edges are active or inactive at that time. Our focus is on the evolution of the associated empirical graphon in the limit as the number of vertices tends to infinity, in the setting where fluctuations in the graph-valued process are more likely to be caused by fluctuations in the vertex types than by fluctuations in the states of the edges given these types. We derive both sample-path large deviation principles and convergence of stochastic processes. We demonstrate the flexibility of our approach by treating a class of stochastic processes where the edge probabilities depend not only on the fluctuations in the vertex types but also on the state of the graph itself.
Semantic Image Synthesis with Semantically Coupled VQ-Model

Semantic image synthesis enables control over unconditional image generation by allowing guidance on what is being generated. We conditionally synthesize the latent space from a vector quantized model (VQ-model) pre-trained to autoencode images. Instead of training an autoregressive Transformer on separately learned conditioning latents and image latents, we find that jointly learning the conditioning and image latents significantly improves the modeling capabilities of the Transformer model. While our jointly trained VQ-model achieves a similar reconstruction performance to a vanilla VQ-model for both semantic and image latents, tying the two modalities at the autoencoding stage proves to be an important ingredient to improve autoregressive modeling performance. We show that our model improves semantic image synthesis using autoregressive models on popular semantic image datasets ADE20k, Cityscapes and COCO-Stuff.
Ultrahigh-Resolution Wireless Capacitance Readout Based on Single Real Mode in Perturbed Parity-Time-Symmetric Sandwich-Type Electronic Trimer

High-performance interrogation of inductor-capacitor (LC) microsensor has been a long-standing challenge due to the limited size of the sensor. Parity-time (PT) symmetry, an intriguing concept originated from quantum physics, has been utilized to improve the spectral resolution and sensitivity of the conventional readout circuit, while the PT symmetry condition has to be satisﬁed. In this work, a sandwich-type wireless capacitance readout mechanism based on perturbed PT-symmetric electronic trimer without manual tuning of the reader circuit is proposed. Theoretical eigenvalue analysis by solving the system equations shows that the system exhibits single real mode in weak coupling regime whose eigenfrequency changes in response to the capacitance of the neutral resonator. Furthermore, the proposed readout system exhibits wider readout capacitance range compared to standard PT-symmetry-based system while retaining higher Q -factor compared to conventional readout method, which has been validated with the experimental prototype based on printed circuit board. Our work not only enriches the underlying theory of non-Hermitian physics, but also shows potential applications in scenarios where longer interrogation distance is required, such as implanted medical devices, parameter detection in harsh environment, etc .