Miracle-dz/UniFAR-FTUs huggingface dataset:
Flow characteristics and scaling past highly porous wall-mounted 1 fences

7 An extensive characterization of the flow past wall-mounted highly porous fences based on singleand multi-scale 8 geometries has been performed using hot-wire anemometry in a low-speed wind tunnel. Whilst drag properties 9 (estimated from the momentum equation) seem to be mostly dependent on the grids’ blockage ratio; wakes of 10 different size and orientation bars seem to generate distinct behaviours regarding turbulence properties. Far 11 from the near-grid region, the flow is dominated by the presence of two well-differentiated layers: one close to 12 the wall dominated by the near-wall behaviour and another one corresponding to the grid’s wake and shear 13 layer, originating from between this and the freestream. It is proposed that the effective thickness of the wall 14 layer can be inferred from the wall-normal profile of root-mean-square streamwise velocity or, alternatively, from 15 the wall-normal profile of streamwise velocity correlation. Using these definitions of wall-layer thickness enables 16 us to collapse different trends of the turbulence behaviour inside this layer. In particular, the root-mean-square 17 level of the wall shear stress fluctuations, longitudinal integral length scale and spanwise turbulent structure 18 are shown to display a satisfactory scaling with this thickness rather than with the whole thickness of the grid’s 19 wake. Moreover, it is shown that certain grids destroy the spanwise arrangement of large turbulence structures 20 in the logarithmic region which are then re-formed after a particular streamwise extent. It is finally shown that 21 for fences subject to a boundary layer of thickness comparable to their height the effective thickness of the wall 22 layer scales with the incoming boundary layer thickness. Analogously, it is hypothesized that the growth rate 23 of the internal layer is also partly dependent on the incoming boundary layer thickness. 24
Survival strategies of eelgrass in reduced light

xvi CHAPTER PAGE I. GENERAL INTRODUCTION 1 LITERATURE CITED 7 II. REVIEW: LIGHT REDUCTION AND ITS EFFECTS ON EELGRASS . 12 LIGHT AS A REQUIREMENT AND A LIMITING FACTOR FOR EELGRASS GROWTH 13 Light and eelgrass an introduction 13 The minimum light requirement concept 14 Approaches to investigating minimum light requirements 16 Minimum light requirements concluding remarks 30 FACTORS REDUCING LIGHT AVAILABILITY TO EELGRASS 33 The nature of light reduction 33 Natural factors that influence light availability 35 Anthropogenic factors that influence light availability 41
Modelling Rotating Detonative Combustion Fueled by Partially Pre-vaporized n-Heptane Sprays

Eulerian-Lagrangian simulations are conducted for two-dimensional Rotating Detonative Combustion (RDC) fueled by partially prevaporized n-heptane sprays. The influences of droplet diameter and total equivalence ratio on detonation combustion and droplet dynamics are studied. It is found that small n-heptane droplets (e.g. 5 micrometer) are completely vaporized around the detonation wave, while intermediate n-heptane droplets (e.g. 20 micrometer) are consumed in or behind the detonation wave, with the escaped ones be continuously evaporated and deflagrated. The detonated fuel fraction is high when the droplet diameters are small or large, reaching its minimal value with diameter being 20 micrometer. The detonation propagation speed decreases with increased droplet diameter and is almost constant when the diameter is larger (> 30 micrometer). The velocity deficits are 2-18% compared to the respective gaseous cases. Moreover, the propagation speed increases as the total equivalence ratio increases for the same droplet diameter. It is also found that the detonation propagation speed and detonated fuel fraction are considerably affected by the pre-vaporized gas equivalence ratio. Mixed premixed and non-premixed combustion modes are seen in two-phase RDC. The droplet distributions in the RDE combustor are significantly affected by the droplet evaporation behaviors.
Monte Carlo Tree Search Based Tactical Maneuvering

In this paper we explore the application of simultaneous move Monte Carlo Tree Search (MCTS) based online framework for tactical maneuvering between two unmanned aircrafts. Compared to other techniques, MCTS enables efficient search over long horizons and uses self-play to select best maneuver in the current state while accounting for the opponent aircraft tactics. We explore different algorithmic choices in MCTS and demonstrate the framework numerically in a simulated 2D tactical maneuvering application.
Towards Behavior-Level Explanation for Deep Reinforcement Learning

While Deep Neural Networks (DNNs) are becoming the state-of-the-art for many tasks including reinforcement learning (RL), they are especially resistant to human scrutiny and understanding. Input attributions have been a foundational building block for DNN expalainabilty but face new challenges when applied to deep RL. We address the challenges with two novel techniques. We define a class of \emph{behaviour-level attributions} for explaining agent behaviour beyond input importance and interpret existing attribution methods on the behaviour level. We then introduce \emph{$\lambda$-alignment}, a metric for evaluating the performance of behaviour-level attributions methods in terms of whether they are indicative of the agent actions they are meant to explain. Our experiments on Atari games suggest that perturbation-based attribution methods are significantly more suitable to deep RL than alternatives from the perspective of this metric. We argue that our methods demonstrate the minimal set of considerations for adopting general DNN explanation technology to the unique aspects of reinforcement learning and hope the outlined direction can serve as a basis for future research on understanding Deep RL using attribution.
GrateTile: Efficient Sparse Tensor Tiling for CNN Processing

We propose GrateTile, an efficient, hardware-friendly data storage scheme for sparse CNN feature maps (activations). It divides data into uneven-sized subtensors and, with small indexing overhead, stores them in a compressed yet randomly accessible format. This design enables modern CNN accelerators to fetch and decompressed sub-tensors on-the-fly in a tiled processing manner. GrateTile is suitable for architectures that favor aligned, coalesced data access, and only requires minimal changes to the overall architectural design. We simulate GrateTile with state-of-the-art CNNs and show an average of 55% DRAM bandwidth reduction while using only 0.6% of feature map size for indexing storage.
Hierarchical Multi-Grained Generative Model for Expressive Speech Synthesis

This paper proposes a hierarchical generative model with a multi-grained latent variable to synthesize expressive speech. In recent years, fine-grained latent variables are introduced into the text-to-speech synthesis that enable the fine control of the prosody and speaking styles of synthesized speech. However, the naturalness of speech degrades when these latent variables are obtained by sampling from the standard Gaussian prior. To solve this problem, we propose a novel framework for modeling the fine-grained latent variables, considering the dependence on an input text, a hierarchical linguistic structure, and a temporal structure of latent variables. This framework consists of a multi-grained variational autoencoder, a conditional prior, and a multi-level auto-regressive latent converter to obtain the different time-resolution latent variables and sample the finer-level latent variables from the coarser-level ones by taking into account the input text. Experimental results indicate an appropriate method of sampling fine-grained latent variables without the reference signal at the synthesis stage. Our proposed framework also provides the controllability of speaking style in an entire utterance.
ALMA observations of envelopes around first hydrostatic core candidates

We present ALMA 3 mm molecular line and continuum observations with a resolution of ~3.5" towards five first hydrostatic core (FHSC) candidates (L1451-mm, Per-bolo 58, Per-bolo 45, L1448-IRS2E and Cha-MMS1). Our goal is to characterize their envelopes and identify the most promising sources that could be bona fide FHSCs. We identify two candidates which are consistent with an extremely young evolutionary state (L1451-mm and Cha-MMS1), with L1451-mm being the most promising FHSC candidate. Although our envelope observations cannot rule out Cha-MMS1 as a FHSC yet, the properties of its CO outflow and SED published in recent studies are in better agreement with the predictions for a young protostar. For the remaining three sources, our observations favor a prestellar nature for Per-bolo 45 and rule out the rest as FHSC candidates. Per-bolo 58 is fully consistent with being a Class 0, while L1448 IRS2E shows no emission of high-density tracers (NH2D and N2H+) at the location of the previously identified compact continuum source, which is also undetected in our observations. Thus we argue that there is no embedded source at the presumptive location of the FHSC candidate L1448 IRS2E. We propose instead, that what was thought to be emission from the presumed L1448 IRS2E outflow corresponds to outflow emission from a nearby Class 0 system, deflected by the dense ambient material. We compare the properties of the FHSC candidates studied in this work and the literature, which shows that L1451-mm appears as possibly the youngest source with a confirmed outflow.
A Visual Language for Composable Inductive Programming

We present Zoea Visual which is a visual programming language based on the Zoea composable inductive programming language. Zoea Visual allows users to create software directly from a specification that resembles a set of functional test cases. Programming with Zoea Visual involves the definition of a data flow model of test case inputs, optional intermediate values, and outputs. Data elements are represented visually and can be combined to create structures of any complexity. Data flows between elements provide additional information that allows the Zoea compiler to generate larger programs in less time. This paper includes an overview of the language. The benefits of the approach and some possible future enhancements are also discussed.
Excitation and acceleration of molecular outflows in LIRGs: The extended ESO 320-G030 outflow on 200-pc scales

We used high-spatial resolution (70 pc; 0$ {{\overset{\prime\prime}{.}}} $3) CO multi-transition (J = 1–0, 2–1, 4–3, and 6–5) ALMA data to study the physical conditions and kinematics of the cold molecular outflow in the local luminous infrared galaxy (LIRG) ESO 320-G030 (d = 48 Mpc, LIR/L⊙ = 1011.3). ESO 320-G030 is a double-barred isolated spiral, but its compact and obscured nuclear starburst (SFR ∼ 15 M⊙ yr−1; AV ∼ 40 mag) resembles those of ultra-luminous infrared galaxies (LIR/L⊙ > 1012). In the outflow, the CO(1–0)/CO(2–1) ratio is enhanced with respect to the rest of the galaxy and the CO(4–3) transition is undetected. This indicates that the outflowing molecular gas is less excited than the molecular gas in the nuclear starburst (i.e., outflow launching site) and in the galaxy disk. Non-local thermodynamic equilibrium radiative transfer modeling reveals that the properties of the molecular clouds in the outflow differ from those of the nuclear and disk clouds: The kinetic temperature is lower (Tkin ∼ 9 K) in the outflow, and the outflowing clouds have lower column densities. Assuming a 10−4 CO abundance, the large internal velocity gradients, 60−45+250 km s−1 pc−1, imply that the outflowing molecular clouds are not bound by self-gravity. All this suggests that the life-cycle (formation, collapse, dissipation) of the galaxy disk molecular clouds might differ from that of the outflowing molecular clouds which might not be able to form stars. The low kinetic temperature of the molecular outflow remains constant at radial distances between 0.3 and 1.7 kpc. This indicates that the heating by the hotter ionized outflow phase is not efficient and may favor the survival of the molecular gas phase in the outflow. The spatially resolved velocity structure of the outflow shows a 0.8 km s−1 pc−1 velocity gradient between 190 pc and 560 pc and then a constant maximum outflow velocity of about 700–800 km s−1 up to 1.7 kpc. This could be compatible with a pure gravitational evolution of the outflow, which would require coupled variations of the mass outflow rate and the outflow launching velocity distribution. Alternatively, a combination of ram pressure acceleration and cloud evaporation could explain the observed kinematics and the total size of the cold molecular phase of the outflow.
Quickest Change Detection with Privacy Constraint

This paper considers Lorden's minimax quickest change detection (QCD) problem with a privacy constraint. The goal is to sanitize a signal to satisfy inference privacy requirements while being able to detect a change quickly. We show that the Generalized Likelihood Ratio (GLR) CuSum achieves asymptotic optimality with a properly designed sanitization channel. We formulate the design of this sanitization channel as an optimization problem, which is however challenging to solve. We propose relaxations to the optimization problem and develop algorithms to obtain a solution. We also consider the privacy-aware QCD problem under a decentralized framework and propose algorithms to solve the relaxed channel design problem under this framework.
A Social Network of Russian "Kompromat"

"Kompromat" (the Russian word for "compromising material") has been efficiently used to harass Russian political and business elites since the days of the USSR. Online crowdsourcing projects such as "RuCompromat" made it possible to catalog and analyze kompromat using quantitative techniques -- namely, social network analysis. In this paper, we constructed a social network of 11,000 Russian and foreign nationals affected by kompromat in Russia in 1991 -- 2020. The network has an excellent modular structure with 62 dense communities. One community contains prominent American officials, politicians, and entrepreneurs (including President Donald Trump) and appears to concern Russia's controversial interference in the 2016 U.S. presidential elections. Various network centrality measures identify seventeen most central kompromat figures, with President Vladimir Putin solidly at the top. We further reveal four types of communities dominated by entrepreneurs, politicians, bankers, and law enforcement officials ("siloviks"), the latter disjointed from the first three.
On the Enumeration of Sweep-Covers and Their Relation to Raney Numbers

I define a collections of nodes in trees which are called "Sweep-Covers" for their 'covering' of all the nodes in the tree by some ancestor-descendent relationship. Then, I analyze an algorithm for finding all sweep covers of a given size in any tree. The complexity of the algorithm is analyzed on a class of infinite $\Delta$-ary trees with constant path lengths between the $\Delta$-star internal nodes. The upper bound on the enumeration is analyzed with respect to maximum out-degree $\Delta$, size of sweep cover $n$, and path length $\gamma$. I prove that the Raney numbers are a strict lower bound for enumerating sweep-covers on these infinite trees.
Physiologically valid 3D-0D closed loop model of the heart and circulation -- Modeling the acute response to altered loading and contractility

Computer models of cardiac electro-mechanics (EM) have the potential to enhance our understanding of underlying mechanisms in the analysis of normal and dysfunctional hearts and show promise as a guidance in designing and developing new treatment. Here, pre- and afterload conditions play a critical role in governing and regulating heart function under both normal and pathological conditions. The common approach of imposing loads upon the heart by isolated pre- and afterload models does not fully address all properties of the circulatory system as important feedback mechanisms are missing. Closed-loop models are capable of modeling these features and also guarantee the conservation of blood volume throughout the circulatory system over multiple cardiac cycles. In this study, we present the coupling of a 3D bi-ventricular EM model to the sophisticated 0D closed-loop model CircAdapt. We show the capabilities of this advanced framework by comparing a control case of a healthy patient to different pathological scenarios. These simulations setups are not feasible using isolated pre- and afterload or simple closed-loop models and are hitherto unprecedented in the literature.
Towards simulating a realistic data analysis with an optimised angular power spectrum of spectroscopic galaxy surveys

Abstract The angular power spectrum is a natural tool to analyse the observed galaxy number count fluctuations. In a standard analysis, the angular galaxy distribution is sliced into concentric redshift bins and all correlations of its harmonic coefficients between bin pairs are considered—a procedure referred to as ‘tomography’. However, the unparalleled quality of data from oncoming spectroscopic galaxy surveys for cosmology will render this method computationally unfeasible, given the increasing number of bins. Here, we put to test against synthetic data a novel method proposed in a previous study to save computational time. According to this method, the whole galaxy redshift distribution is subdivided into thick bins, neglecting the cross-bin correlations among them; each of the thick bin is, however, further subdivided into thinner bins, considering in this case all the cross-bin correlations. We create a simulated data set that we then analyse in a Bayesian framework. We confirm that the newly proposed method saves computational time and gives results that surpass those of the standard approach.
DVE: Dynamic Variational Embeddings with Applications in Recommender Systems

Embedding is a useful technique to project a high-dimensional feature into a low-dimensional space, and it has many successful applications including link prediction, node classification and natural language processing. Current approaches mainly focus on static data, which usually lead to unsatisfactory performance in applications involving large changes over time. How to dynamically characterize the variation of the embedded features is still largely unexplored. In this paper, we introduce a dynamic variational embedding (DVE) approach for sequence-aware data based on recent advances in recurrent neural networks. DVE can model the node's intrinsic nature and temporal variation explicitly and simultaneously, which are crucial for exploration. We further apply DVE to sequence-aware recommender systems, and develop an end-to-end neural architecture for link prediction.
3-D Virtual Seismology

We create virtual sources and receivers in a 3-D subsurface using the previously derived single-sided homogeneous Green’s function representation. We employ Green’s functions and focusing functions that are obtained using reflection data at the Earth’s surface, a macrovelocity model, and the Marchenko method. The homogeneous Green’s function is Green’s function superposed with its time-reversal. Unlike the classical homogeneous Green’s function representation, our approach requires no receivers on an enclosing boundary; however, it does require the source signal to be symmetric in time. We demonstrate that, in 3-D, the single-sided representation is an improvement over the classical representation by applying the representations to numerical data. We retrieve responses to virtual point sources with an isotropic and with a double-couple radiation pattern and compare the results to a directly modeled reference result. We also demonstrate the application of the single-sided representation for retrieving the response to a virtual rupture that consists of a superposition of double-couple point sources. This is achieved by obtaining the homogeneous Green’s function for each source separately before they are transformed to the causal Green’s function, time-shifted, and superposed. The single-sided representation is also used to monitor the complete wavefield that is caused by a numerically modeled rupture. However, the source signal of an actual rupture is not symmetric in time, and the single-sided representation can, therefore, only be used to obtain the causal Green’s function. This approach leaves artifacts in the final result; however, these artifacts are limited in space and time.
Shadowed triplet pairings in Hund's metals with spin-orbit coupling

Hund's coupling in multiorbital systems allows for the possibility of even-parity orbital-antisymmetric spin-triplet pairing, which can be stabilized by spin-orbit coupling (SOC). While this pairing expressed in the orbital basis is uniform and spin-triplet, it appears in the band basis as a pseudospin-singlet, with the momentum dependence determined by the SOC and the underlying triplet character remaining in the form of interband pairing active away from the Fermi energy. Here, we examine the role of momentum-dependent SOC in generating nontrivial pairing symmetries, as well as the hidden triplet nature associated with this interorbital pairing, which we dub a "shadowed triplet". Applying this concept to Sr$_{2}$RuO$_{4}$, we first derive several forms of SOC with $d$-wave form factors from a microscopic model, and subsequently we show that for a range of SOC parameters, a pairing state with $s+id_{xy}$ symmetry can be stabilized. Such a pairing state is distinct from pure spin-singlet and -triplet pairings due to its unique character of pseudospin-energy locking. We discuss experimental probes to differentiate the shadowed triplet pairing from conventional pseudospin-triplet and -singlet pairings.
Review of machine learning methods for RNA secondary structure prediction

Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.
Accelerating Search on Binary Codes in Weighted Hamming Space

Compared to Hamming distance, weighted Hamming distance as a similarity measure between binary codes and the binary query point can provide superior accuracy in the search tasks. However, how to efficiently find $K$ binary codes in the dataset that have the smallest weighted Hamming distance with the query is still an open issue. In this paper, a non-exhaustive search framework is proposed to accelerate the search speed and guarantee the search accuracy on the binary codes in weighted Hamming space. By separating the binary codes into multiple disjoint substrings as the bucket indices, the search framework iteratively probes the buckets until the query's nearest neighbors are found. The framework consists of two modules, the search module and the decision module. The search module successively probes the buckets and takes the candidates according to a proper probing sequence generated by the proposed search algorithm. And the decision module decides whether the query's nearest neighbors are found or more buckets should be probed according to a designed decision criterion. The analysis and experiments indicate that the search framework can solve the nearest neighbor search problem in weighted Hamming space and is orders of magnitude faster than the linear scan baseline.