Miracle-dz/UniFAR-FTUs huggingface dataset:
Environmental measurements and mid‐frequency backscatter in shallow water

To understand bottom backscattering mechanism at the mid‐frequency range (nominally 3.5 kHz), direct measurements of bottom roughness and sub‐bottom heterogeneity were made along with concurrent backscatter measurements in a shallow water site with 105 m water depth. The backscatter was recorded on a vertical line array with 31 elements with a 3.5 kHz source attached at the bottom of the array. Bottom roughness and sub‐bottom heterogeneity were measured using an in situ conductivity probe. The roughness measurements cover a one‐dimensional profile of approximately 4 m in length with vertical resolution of 4 mm and horizontal resolution of 1.5 to 2.5 cm. Heterogeneity measurements cover a depth of 5 to 10 cm. Ambient noise received by the vertical line array was used to estimate the sound speed, density, and attenuation coefficient of the surficial sediments. A Monte Carlo modeling capability was developed to extensively simulate the backscatter with different environmental inputs. The results of this effo.
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines

Dialogue models are able to generate coherent and fluent responses, but they can still be challenging to control and may produce non-engaging, unsafe results. This unpredictability diminishes user trust and can hinder the use of the models in the real world. To address this, we introduce DialGuide, a novel framework for controlling dialogue model behavior using natural language rules, or guidelines. These guidelines provide information about the context they are applicable to and what should be included in the response, allowing the models to generate responses that are more closely aligned with the developer's expectations and intent. We evaluate DialGuide on three tasks in open-domain dialogue response generation: guideline selection, response generation, and response entailment verification. Our dataset contains 10,737 positive and 15,467 negative dialogue context-response-guideline triplets across two domains - chit-chat and safety. We provide baseline models for the tasks and benchmark their performance. We also demonstrate that DialGuide is effective in the dialogue safety domain, producing safe and engaging responses that follow developer guidelines.
Ohmic H-modes in the TCV tokamak

The TCV tokamak has obtained ohmic H-modes in virtually all diverted plasmas with the ion del B drift directed towards an X-point and in several elongated limiter plasmas. Troyon factors (beta(tor)aB/l(p)) up to 2 and line average densities up to 2.2 x 10(20) m(-3), corresponding to the Greenwald limit, have been obtained in diverted ELM-free H-modes. Quasi-stationary H-modes lasting for the entire current flat top (1.5 s) have been obtained in the presence of regular ELMs. The occurrence and magnitude of ELMs have been found to depend on configurational parameters such as the position of the 'active' X-point in unbalanced double-null discharges and the plasma-wall separation in single-null discharges. These dependencies have permitted active control of ELM behaviour in TCV. A continuous spectrum of ELM amplitudes and frequencies has been observed, ranging from clearly identifiable type III ELMs to large, low-frequency ELMs which expel up to 12% of the stored energy and up to 7% of the particle content and are reminiscent of type I ELMs. A previously unknown, benign kind of ELM, with a maximum amplitude in the divertor region, has also been observed.
Drastic Enlargement of Grain Size of Excimer-Laser-Crystallized Polysilicon Films

Polysilicon thin films with extremely large grains (grain size more than 50 µm, i.e., three orders of magnitude more than the typical value) have been formed using an excimer-laser crystallization method for the first time. This drastic enlargement was achieved by the reduction of the solidification rate of molten silicon by the small heat capacitance effect of the thin silicon-dioxide membrane used as a substrate. Crystallinity and film properties have been evaluated from Raman spectroscopy, resistivity, Hall effect and transistor characteristics. The Hall mobility of electrons was as high as 610 cm2/Vs.
A Measure-Theoretic Characterization of Tight Language Models

Language modeling, a central task in natural language processing, involves estimating a probability distribution over strings. In most cases, the estimated distribution sums to 1 over all finite strings. However, in some pathological cases, probability mass can “leak” onto the set of infinite sequences. In order to characterize the notion of leakage more precisely, this paper offers a measure-theoretic treatment of language modeling. We prove that many popular language model families are in fact tight, meaning that they will not leak in this sense. We also generalize characterizations of tightness proposed in previous works.
A robust dynamic mode decomposition methodology for an airfoil undergoing transonic shock buffet

Dynamic mode decomposition (DMD) is a data-driven technique widely used to analyze and model fluid problems including transonic buffet flows. Despite its strengths, DMD is known to suffer from sensitivities to the selected settings and the characteristics of the employed data. In this work, we closely examine the aforementioned sensitivities, identify possible pitfalls, and provide best practices to robustly perform DMD on a flow exhibiting transonic shock buffet. Specifically, we assess various DMD variants and test their sensitivity to the POD rank truncation and the sampling rate. A critical enabler to our analysis is a new presentation of the DMD algorithm as a modular framework consisting of five distinct steps. The tests also highlight the existing dangers of aliasing, when the sampling rate is too low. Finally, a list of practical recommendations and guidelines on how to accurately and robustly perform DMD on a transonic buffet flow is provided.
Kinetic theory of quasi-stationary collisionless axisymmetric plasmas in the presence of strong rotation phenomena

The problem of formulating a kinetic treatment for quasi-stationary collisionless plasmas in axisymmetric systems subject to the possibly independent presence of local strong velocity-shear and supersonic rotation velocities is posed. The theory is developed in the framework of the Vlasov-Maxwell description for multi-species non-relativistic plasmas. Applications to astrophysical accretion discs arising around compact objects and to plasmas in laboratory devices are considered. Explicit solutions for the equilibrium kinetic distribution function (KDF) are constructed based on the identification of the relevant particle adiabatic invariants. These are shown to be expressed in terms of generalized non-isotropic Gaussian distributions. A suitable perturbative theory is then developed which allows for the treatment of non-uniform strong velocity-shear/supersonic plasmas. This yields a series representation for the equilibrium KDF in which the leading-order term depends on both a finite set of fluid fields as.
Forced Waves in a Baroclinic Shear Flow. Part 2: Damped and Undamped Response to Weak Near-Resonant Forcing

Abstract This paper is a continuation of a study of the response of a two-layer baroclinic fluid to weak, near resonant planetary wave forcing. Unlike Part I (Plumb, 1979), the fluid is baroclinically stable and viscous damping is incorporated into the model. With no damping, periodic solutions are found which are similar to but simpler than those of Part 1. With the addition of damping, multiple steady solutions are possible, in agreement with results of earlier studies, notably Charney and DeVore (1979). The importance of nonlinear resonance, the relationships between inviscid and viscous dynamics, and the time scales on which these processes act are investigated by numerical integration of a series of initial value problems.
Approximate Query Processing via Tuple Bubbles

—We propose a versatile approach to lightweight, ap- proximate query processing by creating compact but tunably pre-cise representations of larger quantities of original tuples, coined bubbles. Instead of working with tables of tuples, the query processing then operates on bubbles but leaves the traditional query processing paradigms conceptually applicable. We believe this is a natural and viable approach to render approximate query processing feasible for large data in disaggregated cloud settings or in resource-limited scenarios. Bubbles are tunable regarding the compactness of enclosed tuples as well as the granularity of statistics and the way they are instantiated. For improved accuracy, we put forward a ﬁrst working solution that represents bubbles via Bayesian networks, per table, or along foreign-key joins. To underpin the viability of the approach, we report on an experimental evaluation considering the state-of-the- art competitors, where we show clear beneﬁts when assessing the estimation accuracy, execution time, and required disk space.
Passivity-based control of a class of Blondel-Park transformable electric machines

In this paper we study the viability of extending, to the general rotating electric machine 's model, the passivity-based controller method that we have developed for induction motors. In this approach the passivity (energy dissipation) properties of the motor are taken advantage of at two different levels. First, we prove that the motor model can be decomposed as the feedback interconnection of two passive subsystems, which can essentially be identified with the electrical and mechanical dynamics. Then, we design a torque tracking controller that preserves passivity for the electrical subsystem, and leave the mechanical part as a passive disturbance. In position or speed control applications this procedure naturally leads to the well known cascaded controller structure which is typically analyzed invoking time-scale separation assumptions. A key feature of the new cascaded control paradigm is that the latter arguments are obviated in the stability analysis. Our objective in this paper is to characterize a class of machines for which such a passivity-based controller solves the output feedback torque tracking problem. Roughly speaking, the class consists of machines whose nonactuated dynamics are well damped and whose electrical and mechanical dynamics can be suitably decoupled via a coordinate transformation. The first condition translates into the requirement of approximate knowledge of the rotor resistances to avoid the need of injecting high gain into the loop. The latter condition is known in the electric machines literature as Blondel-Park transformability, and in practical terms it requires that the air-gap magnetomotive force must be suitably approximated by the first harmonic in its Fourier expansion. These conditions, stemming from the construction of the machine, have a clear physical interpretation in terms of the couplings between its electrical, magnetic and mechanical dynamics, and are satisfied by a large number of practical machines. The passivity-based controller presented here reduces to the well known indirect vector controller for current-fed induction machines. Our developments constitute an extension, to voltage-fed machines, of this de facto standard in industrial applications. Furthermore, our analysis provides it with a solid theoretical foundation.
Quantifying Local Extrinsic Curvature in Neural Manifolds

The neural manifold hypothesis postulates that the activity of a neural population forms a low-dimensional manifold within the larger neural state space, whose structure reﬂects the structure of the encoded task variables. Many dimensionality reduction techniques have been used to study the structure of neural manifolds, but these methods do not provide an explicit parameterization of the manifold, and fail to capture the global structure of topologically nontrivial manifolds. Topological data analysis methods can reveal the shared topological structure between neural manifolds and the task variables they represent, but fail to capture much of the geometric information including distance, angles, and curvature. In this work, we leverage tools from Riemannian geometry and topologically-aware deep generative models to introduce a novel approach for studying the geometry of neural manifolds. This approach (1) computes an explicit parameterization of the manifolds and (2) estimates their local extrinsic curvature. Our approach correctly estimates the geometry of synthetic neural manifolds generated from smooth deformations of circles, spheres, and tori. We expect this approach to open new avenues of inquiry exploring geometric neural correlates of perception and behavior, and provide a new means to compare representations in biological and artiﬁcial neural systems.
Debiasing NLP Models Without Demographic Information

Models trained from real-world data tend to imitate and amplify social biases. Although there are many methods suggested to mitigate biases, they require a preliminary information on the types of biases that should be mitigated (e.g., gender or racial bias) and the social groups associated with each data sample. In this work, we propose a debiasing method that operates without any prior knowledge of the demographics in the dataset, detecting biased examples based on an auxiliary model that predicts the main model’s success and down-weights them during the training process. Re-sults on racial and gender bias demonstrate that it is possible to mitigate social biases without having to use a costly demographic annotation process.
Statistical Comparison among Brain Networks with Popular Network Measurement Algorithms

In this research, a number of popular network measurement algorithms have been applied to several brain networks (based on applicability of algorithms) for finding out statistical correlation among these popular network measurements which will help scientists to understand these popular network measurement algorithms and their applicability to brain networks. By analyzing the results of correlations among these network measurement algorithms, statistical comparison among selected brain networks has also been summarized. Besides that, to understand each brain network, the visualization of each brain network and each brain network’s degree distribution histogram have been extrapolated. Six network measurement algorithms have been chosen to apply time to time on sixteen brain networks based on applicability of these network measurement algorithms and the results of these network measurements are put into a correlation method to show the relationship among these six network measurement algorithms for each brain network. At the end, the results of the correlations have been summarized to show the statistical comparison among these sixteen brain networks
Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers

Large pretrained language models have shown surprising In-Context Learning (ICL) ability. With a few demonstration input-label pairs, they can predict the label for an unseen input without additional parameter updates. Despite the great success in performance, the working mechanism of ICL still remains an open problem. In order to better understand how ICL works, this paper explains language models as meta-optimizers and understands ICL as a kind of implicit ﬁnetuning. Theoretically, we ﬁgure out that the Transformer attention has a dual form of gradient descent based optimization. On top of it, we understand ICL as follows: GPT ﬁrst produces meta-gradients according to the demonstration examples, and then these meta-gradients are applied to the original GPT to build an ICL model. Experimentally, we comprehensively compare the behavior of ICL and explicit ﬁnetuning based on real tasks to provide empirical evidence that supports our understanding. The results prove that ICL behaves similarly to explicit ﬁnetuning at the prediction level, the representation level, and the attention behavior level. Further, inspired by our understanding of meta-optimization, we design a momentum-based attention by analogy with the momentum-based gradient descent algorithm. Its consistently better performance over vanilla attention supports our understanding again from another aspect, and more impor-tantly, it shows the potential to utilize our understanding for future model designing.
Learning Subgrid-scale Models with Neural Ordinary Differential Equations

We propose a new approach to learning the subgrid-scale model when simulating partial differential equations (PDEs) solved by the method of lines and their representation in chaotic ordinary differential equations, based on neural ordinary differential equations (NODEs). Solving systems with fine temporal and spatial grid scales is an ongoing computational challenge, and closure models are generally difficult to tune. Machine learning approaches have increased the accuracy and efficiency of computational fluid dynamics solvers. In this approach neural networks are used to learn the coarse- to fine-grid map, which can be viewed as subgrid-scale parameterization. We propose a strategy that uses the NODE and partial knowledge to learn the source dynamics at a continuous level. Our method inherits the advantages of NODEs and can be used to parameterize subgrid scales, approximate coupling operators, and improve the efficiency of low-order solvers. Numerical results with the two-scale Lorenz 96 ODE, the convection-diffusion PDE, and the viscous Burgers' PDE are used to illustrate this approach.
viscous pulsational instability of the transonic region of isothermal geometrically thin accretion discs – I. Analytical results

Viscous instability of the transonic region of the conventional geometrically thin alpha-type accretion disks is examined analytically. For simplicity, isothermal disks and isothermal perturbations are assumed. It is found that when the value of alpha is larger than a critical value the disk is unstable against two types of perturbations. One is local propagating perturbations of inertial acoustic waves. Results suggest the possibility that unstable perturbations develop to overstable global oscillations which are restricted only in the innermost region of the disk. The other is standing growing perturbations localized just at the transonic point. The cause of these instabilities is that the azimuthal component of the Lagrangian velocity variation associated with the perturbations becomes in phase with the variation of the viscous stress force. Because of this phase matching work is done on perturbations, and they are amplified.
Semantically-informed Hierarchical Event Modeling

Prior work has shown that coupling sequential latent variable models with semantic ontological knowledge can improve the representational capabilities of event modeling approaches. In this work, we present a novel, doubly hierarchical, semi-supervised event modeling framework that provides structural hierarchy while also accounting for ontological hierarchy. Our approach consistsof multiple layers of structured latent variables, where each successive layer compresses and abstracts the previous layers. We guide this compression through the injection of structured ontological knowledge that is defined at the type level of events: importantly, our model allows for partial injection of semantic knowledge and it does not depend on observing instances at any particular level of the semantic ontology. Across two different datasets and four different evaluation metrics, we demonstrate that our approach is able to out-perform the previous state-of-the-art approaches by up to 8.5%, demonstrating the benefits of structured and semantic hierarchical knowledge for event modeling.
A positivity-preserving Eulerian two-phase approach with thermal relaxation for compressible flows with a liquid and gases

A positivity-preserving fractional algorithm is presented for solving the four-equation homogeneous relaxation model (HRM) with an arbitrary number of ideal gases and a liquid governed by the stiffened gas equation of state. The fractional algorithm consists of a time step of the hyperbolic five-equation model by Allaire et al. and an algebraic numerical thermal relaxation step at an infinite relaxation rate. Interpolation and flux limiters are proposed for the use of high-order Cartesian finite difference or finite volume schemes in a general form such that the positivity of the partial densities and squared sound speed, as well as the boundedness of the volume fractions and mass fractions, are preserved with the algorithm. A conservative solution update for the four-equation HRM is also guaranteed by the algorithm which is advantageous for certain applications such as those with phase transition. The accuracy and robustness of the algorithm with a high-order explicit finite difference weighted compact nonlinear scheme (WCNS) using the incremental-stencil weighted essentially nonoscillatory (WENO) interpolation, are demonstrated with various numerical tests.
Tangential stress beneath wind-driven air–water interfaces

The detailed structure of the aqueous surface sublayer flow immediately adjacent to the wind-driven air–water interface is investigated in a laboratory wind-wave flume using particle image velocimetry (PIV) techniques. The goal is to investigate quantitatively the character of the flow in this crucial, very thin region which is often disrupted by microscale breaking events. In this study, we also examine critically the conclusions of Okuda, Kawai & Toba (1977), who argued that for very short, strongly forced wind-wave conditions, shear stress is the dominant mechanism for transmitting the atmospheric wind stress into the water motion – waves and surface drift currents. In strong contrast, other authors have more recently observed very substantial normal stress contributions on the air side. The availability of PIV and associated image technology now permits a timely re-examination of the results of Okuda et al., which have been influential in shaping present perceptions of the physics of this dynamically important region. The PIV technique used in the present study overcomes many of the inherent shortcomings of the hydrogen bubble measurements, and allows reliable determination of the fluid velocity and shear within 200 μm of the instantaneous wind-driven air–water interface. The results obtained in this study are not in accord with the conclusions of Okuda et al. that the tangential stress component dominates the wind stress. It is found that prior to the formation of wind waves, the tangential stress contributes the entire wind stress, as expected. With increasing distance downwind, the mean tangential stress level decreases marginally, but as the wave field develops, the total wind stress increases significantly. Thus, the wave form drag, represented by the difference between the total wind stress and the mean tangential stress, also increases systematically with wave development and provides the major proportion of the wind stress once the waves have developed beyond their early growth stage. This scenario reconciles the question of relative importance of normal and tangential stresses at an air–water interface. Finally, consideration is given to the extrapolation of these detailed laboratory results to the field, where the present findings suggest that the sea surface is unlikely to become fully aerodynamically rough, at least for moderate to strong winds.
Surface kinetics: Step-facet barriers

This letter presents a concept of surface kinetic barrier: The step–facet barrier. This concept is demonstrated for two face-centered-cubic metals, aluminum and copper, through molecular statics calculations. Our numerical results show that the step–facet barrier is substantially larger than step–step or diffusion barriers on flat surfaces; true for both metals. Based on the relative magnitudes of kinetic barriers, we discuss implications of the step–facet barrier on surface processing, particularly the step flow. This discussion shows that the kinetic barrier potentially may enable us to pattern nanowires on a metal surface.