Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Interventions in Language Models with Differentiable Masking

There has been a lot of interest in understanding what information is captured by hidden representations of language models (LMs). Typically, interpretation methods i) do not guarantee that the model actually uses the information found to be encoded, and ii) do not discover small subsets of neurons responsible for a considered phenomenon. Inspired by causal mediation analysis, we propose a method that discovers a small subset of neurons within a neural LM responsible for a particular linguistic phenomenon, i.e., subsets causing a change in the corresponding token emission probabilities. We use a differentiable relaxation to approximately search through the combinatorial space. An L_0 regularization term ensures that the search converges to discrete and sparse solutions. We apply our method to analyze subject-verb number agreement and gender bias detection in LSTMs. We observe that it is fast and finds better solutions than alternatives such as REINFORCE and Integrated Gradients. Our experiments confirm that each of these phenomena is mediated through a small subset of neurons that do not play any other discernible role.
Simultaneous Observations of Chromospheric Evaporation and Condensation during a C-class Flare

We explored simultaneous observations of chromospheric evaporation and condensation during the impulsive phase of a C6.7 flare on 2019 May 9. The solar flare was simultaneously observed by multiple instruments, i.e., the New Vacuum Solar Telescope (NVST), the Interface Region Imaging Spectrograph, the Atmospheric Imaging Assembly (AIA), Fermi, the Mingantu Spectral Radioheliograph, and the Nobeyama Radio Polarimeters. Using the single Gaussian fitting and the moment analysis technique, redshifted velocities at slow speeds of 15–19 km s−1 are found in the cool lines of C ii and Si iv at one flare footpoint location. Redshifts are also seen in the Hα line-of-sight velocity image measured by NVST at double footpoints. Those redshifts with slow speeds can be regarded as the low-velocity downflows driven by the chromospheric condensation. Meanwhile, the converging motions from double footpoints to the loop top are found in the high-temperature EUV images, such as AIA 131, 94, and 335 Å. Their apparent speeds are estimated to be roughly 126–210 km s−1, which could be regarded as the high-velocity upflows caused by the chromospheric evaporation. The nonthermal energy flux is estimated to be about 5.7 × 1010 erg s−1 cm−2. The characteristic timescale is roughly equal to 1 minute. All these observational results suggest an explosive chromospheric evaporation during the flare impulsive phase. While a hard X-ray/microwave pulse and a type III radio burst are found simultaneously, indicating that the explosive chromospheric evaporation is driven by the nonthermal electron.
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias

Variational Autoencoders are one of the most commonly used generative models, particularly for image data. A prominent difficulty in training VAEs is data that is supported on a lower-dimensional manifold. Recent work by Dai and Wipf (2020) proposes a two-stage training algorithm for VAEs, based on a conjecture that in standard VAE training the generator will converge to a solution with 0 variance which is correctly supported on the ground truth manifold. They gave partial support for that conjecture by showing that some optima of the VAE loss do satisfy this property, but did not analyze the training dynamics. In this paper, we show that for linear encoders/decoders, the conjecture is true-that is the VAE training does recover a generator with support equal to the ground truth manifold-and does so due to an implicit bias of gradient descent rather than merely the VAE loss itself. In the nonlinear case, we show that VAE training frequently learns a higher-dimensional manifold which is a superset of the ground truth manifold.
Gravitational form factors and mechanical properties of a quark at one loop in light-front Hamiltonian QCD

We calculate the gravitational form factors (GFFs) and pressure, shear and energy distributions for a quark state dressed with a gluon at one loop in QCD. We use the light-front Hamiltonian approach. In the light-front gauge, we use a two-component formalism to eliminate the constrained fields. The state may be thought of as a perturbative model for a relativistic spin $1/2$ composite system having a gluonic degree of freedom. We compare the results with model calculations for a nucleon.
Hypergrid subgraphs and the origin of scarred quantum walks in many-body Hilbert space

Following the recent observation of wave function revivals in large Rydberg atom quantum simulators, much effort has focused on understanding the emergence of many-body scars in non-integrable quantum systems. Here we explore the origin of scarred wavefunction revivals in a family of models obtained by deforming the graph adjacency matrix of the PXP model - the effective model of Rydberg atoms in the strong Rydberg blockade regime. We consider deformations that either enhance the Rydberg constraint, ultimately resulting in an effective tight-binding model of two hypercubes joined at a single vertex, or relax the constraint until reaching the free spin-1/2 model. In the former case, we argue that the model of two joined hypercubes captures the essential features of many-body scarring present in the PXP model. On the other hand, relaxing the constraint leads to a sequence of new scarred models, some with more robust scarring signatures than the PXP model, as can be understood from the graph-theoretic viewpoint. Our results shed light on the nature of scarring in the PXP model by identifying its simple parent model, while also highlighting its distinction from the free-spin precession.
Merger Rate Density of Binary Black Holes through Isolated Population I, II, III and Extremely Metal-poor Binary Star Evolution

We investigate the formation of merging binary black holes (BHs) through isolated binary evolution, performing binary population synthesis calculations covering an unprecedentedly wide metallicity range of Population (Pop) I, II, III, and extremely metal-poor (EMP) binary stars. We find that the predicted merger rate density and primary BH mass (m 1) distribution are consistent with the gravitational wave (GW) observations. Notably, Population III and EMP (<10−2 Z ⊙) binary stars yield most of the pair instability (PI) mass gap events with m 1 = 65–130 M ⊙. Population III binary stars contribute more to the PI mass gap events with increasing redshift, and all the PI mass gap events have the Population III origin at redshifts ≳8. Our result can be assessed by future GW observations in the following two points. First, there are no binary BHs with m 1 = 100–130 M ⊙ in our result, and thus the m 1 distribution should suddenly drop in the range of m 1 = 100–130 M ⊙. Second, the PI mass gap event rate should increase toward higher redshift up to ∼11, since those events mainly originate from the Population III binary stars. We find that the following three assumptions are needed to reproduce the current GW observations: a top-heavy stellar initial mass function and the presence of close binary stars for Population III and EMP binary stars, and inefficient convective overshoot in the main-sequence phase of stellar evolution. Without any of the above, the number of PI mass gap events becomes too low to reproduce current GW observations.
An Investigation of the Relationships Between Internal Integration, Antecedents and Functional Performance in Vietnam

Supply chain integration has been considered to be a source of competitive advantage for firms as it improves relationships and the flow of information and resources both between internal functions in an organization and between supply chain partners. This study examines the relationship between internal integration and functional performance. It also examines the three key antecedents to internal integration: communication, leader support and conflict. A questionnaire survey is used to collect data from a sample of 152 production managers. The data are analyzed using regression. The results indicate that communication, conflict and leader support are antecedents of internal integration and also impact functional performance. Internal integration is also found to mediate the relationships between communication and leader support with functional performance, but not for the relationships between conflict and functional performance. This is also the contribution of the paper, which is the first to examine and suggest these mediating effects between communication, leader support, internal integration and functional performance.
A Fragile Points Method, with an interface debonding model, to simulate damage and fracture of U‐notched structures

Notched components are commonly used in engineering structures, where stress concentration may easily lead to crack initiation and development. The main goal of this work is to develop a simple numerical method to predict the strength and crack-growth-path of U-notched specimens made of brittle materials. For this purpose, the Fragile Points Method (FPM), as previously proposed by the authors, has been augmented by an interface debonding model at the interfaces of the FPM domains, to simulate crack initiation and development. The formulations of FPM are based on a discontinuous Galerkin weak form where point-based piece-wise-continuous polynomial test and trial functions are used instead of element-based basis functions. In this work, the numerical fluxes introduced across interior interfaces between subdomains are postulated as the tractions acting on the interface derived from an interface debonding model. The interface damage is triggered when the numerical flux reaches the interface strength, and the process of crack-surface separation is governed by the fracture energy. In this way, arbitrary crack initiation and propagation can be naturally simulated without the need for knowing the fracture-patch before-hand. Additionally, a small penalty parameter is sufficient to enforce the weak-form continuity condition before damage initiation, without causing problems such as artificial compliance and numerical ill-conditioning. As validations, the * Email address of ccorresponding authors: limingjing@buaa.edu. cn (M. Li) and ltdong@buaa.edu. cn (L. Dong)
Robust Implementation with Costly Information

We construct mechanisms that can robustly implement any desired social choice function when (i) agents may incur a cost to learn the state of the world, (ii) with small probability, agents’ preferences can be arbitrarily different from some baseline known to the mechanism designer, and (iii) the mechanism designer does not know agents’ beliefs and higher-order beliefs about one another's preferences. The mechanisms we propose have a natural interpretation and do not require the mechanism designer to be able to verify the state ex post. We also establish impossibility results for stronger notions of robust implementation.
Evaluation of Circular Texture Meter for Measuring Surface Texture of Pavements

The circular texture (CT) meter is a laser-based device for measuring the mean profile depth (MPD) of pavement at a static location. MPD measurements from the CT meter and mean texture depth measurements from the sand patch test were obtained in five random locations in each of 45 sections of the 2000 National Center for Asphalt Technology (NCAT) test track. The NCAT test track provides a wide range of surface types, including coarse and fine dense graded Superpave® mixes, Hveem mixes, stone mastic asphalt, and Novachip. Testing indicated that the CT meter produced results comparable with the ASTM E965 sand patch test. When open-graded mixtures were excluded, this study indicated that the offset was nonsignificant between CT meter and sand patch test results. Previously developed equations to predict macrotexture were found to be inadequate for the wide range of mix types and aggregate types found at the NCAT test track. An equation was developed to relate fineness modulus to macrotexture. This equation was validated with independent data collected by the Virginia Transportation Research Council. Testing conducted as part of a mini round robin indicated that two readings should be averaged to represent a single CT meter measurement. The within-lab coefficient of variation for the CT meter is estimated to be 2.3%. The between-lab coefficient of variation for the CT meter is estimated to be 4.2%. Both estimates are based on the average of two tests being reported as a single measurement.
Enhancing Multi-Scale Implicit Learning in Image Super-Resolution with Integrated Positional Encoding

Is the center position fully capable of representing a pixel? There is nothing wrong to represent pixels with their centers in a discrete image representation, but it makes more sense to consider each pixel as the aggregation of signals from a local area in an image super-resolution (SR) context. Despite the great capability of coordinate-based implicit representation in the field of arbitrary-scale image SR, this area's nature of pixels is not fully considered. To this end, we propose integrated positional encoding (IPE), extending traditional positional encoding by aggregating frequency information over the pixel area. We apply IPE to the state-of-the-art arbitrary-scale image super-resolution method: local implicit image function (LIIF), presenting IPE-LIIF. We show the effectiveness of IPE-LIIF by quantitative and qualitative evaluations, and further demonstrate the generalization ability of IPE to larger image scales and multiple implicit-based methods. Code will be released.
N‐Cloth: Predicting 3D Cloth Deformation with Mesh‐Based Networks

We present a novel mesh‐based learning approach (N‐Cloth) for plausible 3D cloth deformation prediction. Our approach is general and can handle cloth or obstacles represented by triangle meshes with arbitrary topologies. We use graph convolution to transform the cloth and object meshes into a latent space to reduce the non‐linearity in the mesh space. Our network can predict the target 3D cloth mesh deformation based on the initial state of the cloth mesh template and the target obstacle mesh. Our approach can handle complex cloth meshes with up to 100K triangles and scenes with various objects corresponding to SMPL humans, non‐SMPL humans or rigid bodies. In practice, our approach can be used to generate plausible cloth simulation at 30 – 45 fps on an NVIDIA GeForce RTX 3090 GPU. We highlight its benefits over prior learning‐based methods and physically‐based cloth simulators.
CryptoEval: Evaluating the Risk of Cryptographic Misuses in Android Apps with Data-Flow Analysis

The misunderstanding and incorrect configurations of cryptographic primitives have exposed severe security vulnerabilities to attackers. Due to the pervasiveness and diversity of cryptographic misuses, a comprehensive and accurate understanding of how cryptographic misuses can undermine the security of an Android app is critical to the subsequent mitigation strategies but also challenging. Although various approaches have been proposed to detect cryptographic misuse in Android apps, studies have yet to focus on estimating the security risks of cryptographic misuse. To address this problem, we present an extensible framework for deciding the threat level of cryptographic misuse in Android apps. Firstly, we propose a general and unified specification for representing cryptographic misuses to make our framework extensible and develop adapters to unify the detection results of the state-of-the-art cryptographic misuse detectors, resulting in an adapter-based detection tool chain for a more comprehensive list of cryptographic misuses. Secondly, we employ a misuse-originating data-flow analysis to connect each cryptographic misuse to a set of data-flow sinks in an app, based on which we propose a quantitative data-flow-driven metric for assessing the overall risk of the app introduced by cryptographic misuses. To make the per-app assessment more useful for app vetting at the app-store level, we apply unsupervised learning to predict and classify the top risky threats to guide more efficient subsequent mitigation. In the experiments on an instantiated implementation of the framework, we evaluate the accuracy of our detection and the effect of data-flow-driven risk assessment of our framework. Our empirical study on over 40,000 apps and the analysis of popular apps reveal important security observations on the real threats of cryptographic misuse in Android apps.
A Structured Method for Compilation of QAOA Circuits in Quantum Computing

Quantum Approximation Optimization Algorithm (QAOA) is a highly advocated variational algorithm for solving the combinatorial optimization problem. One critical feature in the quantum circuit of QAOA algorithm is that it consists of two-qubit operators that commute. The flexibility in reordering the two-qubit gates allows compiler optimizations to generate circuits with better depths, gate count, and fidelity. However, it also imposes significant challenges due to additional freedom exposed in the compilation. Prior studies lack the following: (1) Performance guarantee, (2) Scalability, and (3) Awareness of regularity in scalable hardware. We propose a structured method that ensures linear depth for any compiled QAOA circuit on multi-dimensional quantum architectures. We also demonstrate how our method runs on Google Sycamore and IBM Non-linear architectures in a scalable manner and in linear time. Overall, we can compile a circuit with up to 1024 qubits in 10 seconds with a 3.8X speedup in depth, 17% reduction in gate count, and 18X improvement for circuit ESP.
Parametrized post-Newtonian formalism in higher-order Teleparallel Gravity

We study the parametrized post-Newtonian (PPN) limit of higher-derivative-torsion Modified Teleparallel Gravity. We start from the covariant formulation of modified Teleparallel Gravity by restoring the spin connection of the theory. Then, we perform the post-Newtonian expansion of the tetrad field around the Minkowski background and find the perturbed field equations. We compute the PPN metric for the higher-order Teleparallel Gravity theories which allows us to show that at the post-Newtonian limit this more general class of theories are fully conservative and indistinguishable from General Relativity . In this way, we extend the results that were already found for F(T) gravity in previous works. Furthermore, our calculations reveal the importance of considering a second post-Newtonian (2PN) order approximation or a parametrized post-Newtonian cosmology (PPNC) framework where additional perturbative modes coming from general modifications of Teleparallel Gravity could lead to new observable imprints.
You Only Need End-to-End Training for Long-Tailed Recognition

The generalization gap on the long-tailed data sets is largely owing to most categories only occupying a few training samples. Decoupled training achieves better performance by training backbone and classifier separately. What causes the poorer performance of end-to-end model training (e.g., logits margin-based methods)? In this work, we identify a key factor that affects the learning of the classifier: the channel-correlated features with low entropy before inputting into the classifier. From the perspective of information theory, we analyze why cross-entropy loss tends to produce highly correlated features on the imbalanced data. In addition, we theoretically analyze and prove its impacts on the gradients of classifier weights, the condition number of Hessian, and logits margin-based approach. Therefore, we firstly propose to use Channel Whitening to decorrelate ("scatter") the classifier's inputs for decoupling the weight update and reshaping the skewed decision boundary, which achieves satisfactory results combined with logits margin-based method. However, when the number of minor classes are large, batch imbalance and more participation in training cause over-fitting of the major classes. We also propose two novel modules, Block-based Relatively Balanced Batch Sampler (B3RS) and Batch Embedded Training (BET) to solve the above problems, which makes the end-to-end training achieve even better performance than decoupled training. Experimental results on the long-tailed classification benchmarks, CIFAR-LT and ImageNet-LT, demonstrate the effectiveness of our method.
Quantum Interior Point Methods for Semidefinite Optimization

We present two quantum interior point methods for semidefinite optimization problems, building on recent advances in quantum linear system algorithms. The first scheme, more similar to a classical solution algorithm, computes an inexact search direction and is not guaranteed to explore only feasible points; the second scheme uses a nullspace representation of the Newton linear system to ensure feasibility even with inexact search directions. The second is a novel scheme that might seem impractical in the classical world, but it is well-suited for a hybrid quantum-classical setting. We show that both schemes converge to an optimal solution of the semidefinite optimization problem under standard assumptions. By comparing the theoretical performance of classical and quantum interior point methods with respect to various input parameters, we show that our second scheme obtains a speedup over classical algorithms in terms of the dimension of the problem n, but has worse dependence on other numerical parameters.
A sea surface-based drag model for Large Eddy Simulation of wind-wave interaction

Monin-Obukhov similarity theory (MOST) is a well tested approach for specifying the fluxes when the roughness surfaces are homogeneous. For flow over waves (inhomogeneous surfaces), phase-averaged roughness length scales are often prescribed through models based on the wave characteristics and the wind speed. However, such approaches lack generalizability over different wave ages and steepnesses due to the reliance on model coefficients tuned to specific datasets. In this paper, a sea surface-based hydrodynamic drag model applicable to moving surfaces is developed to model the pressure-based surface drag felt by the wind due to the waves. The model is based on the surface gradient approach of Anderson and Meneveau (2010) applicable to stationary obstacles and extended here to the wind-wave problem. Thewave drag model proposed specifies the hydrodynamic force based on the incoming momentum flux, wave phase speed, and the surface frontal area. The drag coefficient associated with the wind-wave momentum exchange is determined based on the wave steepness. The wave drag model is used to simulate turbulent airflow above a monochromatic wave train with different wave ages and wave steepnesses. The mean velocity profiles and model form stresses are validated with available lab-scale experimental data and show good agreement across a wide range of wave steepnesses and wave ages. The drag force is correlated with thewave surface gradient and out-of-phase with thewave height distribution by a factor π/2 for the sinusoidal wave-train considered. These results demonstrate that the current approach is sufficiently general over a wide parameter space compared to wave phase-averaged models with a minimal increase in computational cost.