Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinetic approach of light-nuclei production in intermediate-energy heavy-ion collisions

We develop a kinetic approach to the production of light nuclei up to mass number $A$ $\leqslant$ $4$ in intermediate-energy heavy-ion collisions by including them as dynamic degrees of freedom. The conversions between nucleons and light nuclei during the collisions are incorporated dynamically via the breakup of light nuclei by a nucleon and their inverse reactions. We also include the Mott effect on light nuclei, i.e., a light nucleus would no longer be bound if the phase-space density of its surrounding nucleons is too large. With this kinetic approach, we obtain a reasonable description of the measured yields of light nuclei in central Au+Au collisions at energies of $0.25$ - $1.0A~\rm GeV$ by the FOPI collaboration. Our study also indicates that the observed enhancement of the $\alpha$-particle yield at low incident energies can be attributed to a weaker Mott effect on the $\alpha$-particle, which makes it more difficult to dissolve in nuclear medium, as a result of its much larger binding energy.
Linear eigenvalue statistics of XX′ matrices

This article focuses on the fluctuations of linear eigenvalue statistics of Tn×pTn×p′, where Tn×p is an n × p Toeplitz matrix with real, complex, or time-dependent entries. We show that as n → ∞ with p/n → λ ∈ (0, ∞), the linear eigenvalue statistics of these matrices for polynomial test functions converge in distribution to Gaussian random variables. We also discuss the linear eigenvalue statistics of Hn×pHn×p′, when Hn×p is an n × p Hankel matrix. As a result of our studies, we derive in-probability limit and a central limit theorem type result for the Schettan norm of rectangular Toeplitz matrices. To establish the results, we use the method of moments.
Defect theory under steady illuminations and applications

Illumination has been long known to affect semiconductor defect properties during either growth or operating process. Current theories of studying the illumination effects on defects usually have the assumption of unaffected formation energies of neutral defects as well as defect transition energy levels, and use the quasi-Fermi levels to describe behaviors of excess carriers with conclusions at variance. In this work, we first propose a method to simulate steady illumination conditions, based on which we demonstrate that formation energies of neutral defects and defect transition energy levels are insensitive to illumination. Then, we show that optical and thermal excitation of electrons can be seen equivalent with each other to reach a steady electron distribution in a homogeneous semiconductor. Consequently, the electron distribution can be characterized using just one effective temperature T' and one universal Fermi level E_F' for a homogeneous semiconductor under continuous and steady illuminations, which can be seen as a combination of quasi-equilibrium electron system with T' and a lattice system with T. Using the new concepts, we uncover the universal mechanisms of illumination effects on charged defects by treating the band edge states explicitly in the same footing as the defect states. We find that the formation energies of band edge 'defect' states shift with increased T' of electrons, thus affecting the E_F', changing defect ionic probabilities, and affecting concentrations of charged defects. We apply our theory to study the illumination effects on the doping behaviors in GaN:Mg and CdTe:Sb, obtaining results in accordance with experimental observations. More interesting experimental defect-related phenomena under steady illuminations are expected to be understood from our theory.
On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains

In this paper, we provide a strategy to determine the eigenvalue decay rate (EDR) of a large class of kernel functions defined on a general domain rather than $\mathbb S^{d}$. This class of kernel functions include but are not limited to the neural tangent kernel associated with neural networks with different depths and various activation functions. After proving that the dynamics of training the wide neural networks uniformly approximated that of the neural tangent kernel regression on general domains, we can further illustrate the minimax optimality of the wide neural network provided that the underground truth function $f\in [\mathcal H_{\mathrm{NTK}}]^{s}$, an interpolation space associated with the RKHS $\mathcal{H}_{\mathrm{NTK}}$ of NTK. We also showed that the overfitted neural network can not generalize well. We believe our approach for determining the EDR of kernels might be also of independent interests.
Estimating Tracer Emissions with a Backward Lagrangian Stochastic Technique

Common micrometeorological techniques for determining surface emissions (e.g., eddy covariance, flux gradient, integrated horizontal flux) have two important limitations. On a practical level they require substantial or complex instrumentation: e.g., multiple height measurements of concentration and windspeed, fast-response concentration sensors, etc. A more fundamental problem is the restrictions these techniques place on a measurement site. Most require a flat and homogeneous location with a spatially extensive and uniform source. In many situations these limitations are a problem. The difficulty may be the expense of field equipment, the inconvenience of placing sensors in an appropriate location, or the inability to meet site requirements. One solution might be to use surface chambers, although this technique has other weaknesses and limitations. Another alternative is a method based on a dispersion model. An atmospheric dispersion model predicts the time-average tracer concentration downwind of a source. By combining this prediction with a field measurement of concentration, one can diagnose a source emission rate. This approach is often called “inverse modeling,” since the goal is to deduce information about the source from known downwind concentration, rather than the more common modeling objective of deducing downwind concentration from a known source. Inverse dispersion modeling has been applied to a broad range of problems: from the small scale (observations at distances <10 m from the source) to the continental scale, from point sources to area sources to volume sources, from continuous emission sources to time varying sources; and from neutrally buoyant tracers to “heavy” pathogen spores (e.g., Wilson et al., 1982; Raupach, 1989; Carter et al., 1993; Seibert, 1999; Aylor & Flesch, 2001). This chapter looks at one type of application of the inverse-dispersion method: the estimation of tracer emissions from a discrete surface area source, using concentration observations taken near the source (within 1 km). This might include emissions from small soil treatment plots, feedlots, ponds, landfills, industrial grounds, etc. We focus on situations where the terrain is “tolerably” homogeneous, and amenable to a Monin-Obukhov similarity description of the surface winds. The advantages of the inverse-dispersion method for these prob-
Real-Time Neural Appearance Models

We present a complete system for real-time rendering of scenes with complex appearance previously reserved for offline use. This is achieved with a combination of algorithmic and system level innovations. Our appearance model utilizes learned hierarchical textures that are interpreted using neural decoders, which produce reflectance values and importance-sampled directions. To best utilize the modeling capacity of the decoders, we equip the decoders with two graphics priors. The first prior -- transformation of directions into learned shading frames -- facilitates accurate reconstruction of mesoscale effects. The second prior -- a microfacet sampling distribution -- allows the neural decoder to perform importance sampling efficiently. The resulting appearance model supports anisotropic sampling and level-of-detail rendering, and allows baking deeply layered material graphs into a compact unified neural representation. By exposing hardware accelerated tensor operations to ray tracing shaders, we show that it is possible to inline and execute the neural decoders efficiently inside a real-time path tracer. We analyze scalability with increasing number of neural materials and propose to improve performance using code optimized for coherent and divergent execution. Our neural material shaders can be over an order of magnitude faster than non-neural layered materials. This opens up the door for using film-quality visuals in real-time applications such as games and live previews.
Weakly-supervised Micro- and Macro-expression Spotting Based on Multi-level Consistency

Most micro- and macro-expression spotting methods in untrimmed videos suffer from the burden of video-wise collection and frame-wise annotation. Weakly-supervised expression spotting (WES) based on video-level labels can potentially mitigate the complexity of frame-level annotation while achieving fine-grained frame-level spotting. However, we argue that existing weakly-supervised methods are based on multiple instance learning (MIL) involving inter-modality, inter-sample, and inter-task gaps. The inter-sample gap is primarily from the sample distribution and duration. Therefore, we propose a novel and simple WES framework, MC-WES, using multi-consistency collaborative mechanisms that include modal-level saliency, video-level distribution, label-level duration and segment-level feature consistency strategies to implement fine frame-level spotting with only video-level labels to alleviate the above gaps and merge prior knowledge. The modal-level saliency consistency strategy focuses on capturing key correlations between raw images and optical flow. The video-level distribution consistency strategy utilizes the difference of sparsity in temporal distribution. The label-level duration consistency strategy exploits the difference in the duration of facial muscles. The segment-level feature consistency strategy emphasizes that features under the same labels maintain similarity. Experimental results on three challenging datasets -- CAS(ME)$^2$, CAS(ME)$^3$, and SAMM-LV -- demonstrate that MC-WES is comparable to state-of-the-art fully-supervised methods.
LatentAugment: Dynamically Optimized Latent Probabilities of Data Augmentation

Although data augmentation is a powerful technique for improving the performance of image classification tasks, it is difficult to identify the best augmentation policy. The optimal augmentation policy, which is the latent variable, cannot be directly observed. To address this problem, this study proposes $\textit{LatentAugment}$, which estimates the latent probability of optimal augmentation. The proposed method is appealing in that it can dynamically optimize the augmentation strategies for each input and model parameter in learning iterations. Theoretical analysis shows that LatentAugment is a general model that includes other augmentation methods as special cases, and it is simple and computationally efficient in comparison with existing augmentation methods. Experimental results show that the proposed LatentAugment has higher test accuracy than previous augmentation methods on the CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets.
Transmissive Reconfigurable Intelligent Surface Transmitter Empowered Cognitive RSMA Networks

In this letter, we investigated the downlink transmission problem of a cognitive radio network (CRN) equipped with a novel transmissive reconfigurable intelligent surface (TRIS) transmitter. In order to achieve low power consumption and high-rate multi-streams communication, time-modulated arrays (TMA) is implemented and users access the network using rate splitting multiple access (RSMA). With such a network framework, a multi-objective optimization problem with joint design of the precoding matrix and the common stream rate is constructed to achieve higher energy efficiency (EE) and spectral efficiency (SE). Since the objective function is a non-convex fractional function, we proposed a joint optimization algorithm based on difference-of-convex (DC) programming and successive convex approximation (SCA). Numerical results show that under this framework the proposed algorithm can considerably improve and balance the EE and SE.
Aligning Bird-Eye View Representation of Point Cloud Sequences using Scene Flow

Low-resolution point clouds are challenging for object detection methods due to their sparsity. Densifying the present point cloud by concatenating it with its predecessors is a popular solution to this challenge. Such concatenation is possible thanks to the removal of ego vehicle motion using its odometry. This method is called Ego Motion Compensation (EMC). Thanks to the added points, EMC significantly improves the performance of single-frame detectors. However, it suffers from the shadow effect that manifests in dynamic objects’ points scattering along their trajectories. This effect results in a misalignment between feature maps and objects’ locations, thus limiting performance improvement to stationary and slow-moving objects only. Scene flow allows aligning point clouds in 3D space, thus naturally resolving the misalignment in feature spaces. By observing that scene flow computation shares several components with 3D object detection pipelines, we develop a plug-in module that enables single-frame detectors to compute scene flow to rectify their Bird-Eye View representation. Experiments on the NuScenes dataset show that our module leads to a significant increase (up to 16%) in the Average Precision of large vehicles, which interestingly demonstrates the most severe shadow effect.
Surveying Generative AI's Economic Expectations

I introduce a survey of economic expectations formed by querying a large language model (LLM)'s expectations of various financial and macroeconomic variables based on a sample of news articles from the Wall Street Journal between 1984 and 2021. I find the resulting expectations closely match existing surveys including the Survey of Professional Forecasters (SPF), the American Association of Individual Investors, and the Duke CFO Survey. Importantly, I document that LLM based expectations match many of the deviations from full-information rational expectations exhibited in these existing survey series. The LLM's macroeconomic expectations exhibit under-reaction commonly found in consensus SPF forecasts. Additionally, its return expectations are extrapolative, disconnected from objective measures of expected returns, and negatively correlated with future realized returns. Finally, using a sample of articles outside of the LLM's training period I find that the correlation with existing survey measures persists -- indicating these results do not reflect memorization but generalization on the part of the LLM. My results provide evidence for the potential of LLMs to help us better understand human beliefs and navigate possible models of nonrational expectations.
A Chern-Simons theory for dipole symmetry

We present effective field theories for dipole symmetric topological matters that can be described by the Chern-Simons theory. Unlike most studies using higher-rank gauge theory, we develop a framework with both U(1)U(1) and dipole gauge fields. As a result, only the highest multipole symmetry can support the ’t Hooft anomaly. We show that with appropriate point group symmetries, the dipolar Chern-Simons theory can exist in any dimension and, moreover, the bulk-edge correspondence can depend on the boundary. As two applications, we draw an analogy between the dipole anomaly and the torsional anomaly and generalize particle-vortex duality to dipole phase transitions. All of the above are in the flat spacetime limit, but our framework is able to systematically couple dipole symmetry to curved spacetime. Based on that, we give a proposal about anomalous dipole hydrodynamics. Moreover, we show that the fracton-elasticity duality arises naturally from a non-abelian Chern-Simons theory in 3D.
Two-round Ramsey games on random graphs

Motivated by the investigation of sharpness of thresholds for Ramsey properties in random graphs, Friedgut, Kohayakawa, R\"odl, Ruci\'nski and Tetali introduced two variants of a single-player game whose goal is to colour the edges of a~random graph, in an online fashion, so as not to create a monochromatic triangle. In the two-round variant of the game, the player is first asked to find a triangle-free colouring of the edges of a random graph $G_1$ and then extend this colouring to a triangle-free colouring of the union of $G_1$ and another (independent) random graph $G_2$, which is disclosed to the player only after they have coloured $G_1$. Friedgut et al.\ analysed this variant of the online Ramsey game in two instances: when $G_1$ has $\Theta(n^{4/3})$ edges and when the number of edges of $G_1$ is just below the threshold above which a random graph typically no longer admits a triangle-free colouring, which is located at $\Theta(n^{3/2})$. The two-round Ramsey game has been recently revisited by Conlon, Das, Lee and M\'esz\'aros, who generalised the result of Friedgut at al.\ from triangles to all strictly $2$-balanced graphs. We extend the work of Friedgut et al.\ in an orthogonal direction and analyse the triangle case of the two-round Ramsey game at all intermediate densities. More precisely, for every $n^{-4/3} \ll p \ll n^{-1/2}$, with the exception of $p = \Theta(n^{-3/5})$, we determine the threshold density $q$ at which it becomes impossible to extend any triangle-free colouring of a typical $G_1 \sim G_{n,p}$ to a triangle-free colouring of the union of $G_1$ and $G_2 \sim G_{n,q}$. An interesting aspect of our result is that this threshold density $q$ `jumps' by a polynomial quantity as $p$ crosses a `critical' window around $n^{-3/5}$.
Quasirandom additive sets and Cayley hypergraphs

We study the interplay between notions of quasirandomness for additive sets and for hypergraphs. In particular, we show a strong connection between the notions of Gowers uniformity in the additive setting and discrepancy-type measures of quasirandomness in the hypergraph setting. Exploiting this connection, we provide a long list of disparate quasirandom properties regarding both additive sets and Cayley-type hypergraphs constructed from such sets, and show that these properties are all equivalent (in the sense of Chung, Graham and Wilson) with polynomial bounds on their interdependences.
The Direct and Spillover Effects of Large-Scale Affirmative Action at an Elite Brazilian University

We examine the effects of an affirmative action policy at an elite Brazilian university that reserved 45 percent of admission slots for Black and low-income students. We find that marginally-admitted students who enrolled through the affirmative action tracks experienced a 14 percent increase in early-career earnings. But the adoption of affirmative action also caused a large decrease in earnings for the university's most highly-ranked students. We present evidence that the negative spillover effects on highly-ranked students' earnings were driven by both a reduction in human capital accumulation and a decline in the value of networking.
The Indoctrination Game

The indoctrination game is a complete-information contest over public opinion. The players exert costly effort to manifest their private opinions in public in order to control the discussion, so that the governing opinion is similar to theirs. Our analysis provides a theoretical foundation for the silent majority and vocal minority phenomena, i.e., we show that all moderate opinions remain mute in equilibrium while allowing extremists full control of the discussion. Moreover, we prove that elevated exposure to others' opinions increases the observed polarization among individuals. Using these results, we formulate a new social-learning framework, referred to as an indoctrination process.
Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation

Controlling chatbot utterance generation with multiple attributes such as personalities, emotions and dialogue acts is a practically useful but under-studied problem. We propose a novel controllable generation framework called DASC that possesses strong controllability with weighted decoding paradigm, while improving generation quality with the grounding in an attribute semantics space. Generation with multiple attributes is then intuitively implemented with an interpolation of multiple attribute embeddings. Experiments show that DASC can achieve state-of-the-art control accuracy in 3-aspect controllable generation tasks while also producing interesting and reasonably sensible responses, even if in an out-of-distribution robustness test. Visualization of the meaningful representations learned in the attribute semantic space also supports its effectiveness.
Are VAEs Bad at Reconstructing Molecular Graphs?

Many contemporary generative models of molecules are variational auto-encoders of molecular graphs. One term in their training loss pertains to reconstructing the input, yet reconstruction capabilities of state-of-the-art models have not yet been thoroughly compared on a large and chemically diverse dataset. In this work, we show that when several state-of-the-art generative models are evaluated under the same conditions, their reconstruction accuracy is surprisingly low, worse than what was previously reported on seemingly harder datasets. However, we show that improving reconstruction does not directly lead to better sampling or optimization performance. Failed reconstructions from the MoLeR model are usually similar to the inputs, assembling the same motifs in a different way, and possess similar chemical properties such as solubility. Finally, we show that the input molecule and its failed reconstruction are usually mapped by the different encoders to statistically distinguishable posterior distributions, hinting that posterior collapse may not fully explain why VAEs are bad at reconstructing molecular graphs.
Deconvolution on graphs via linear programming

The main challenge addressed in this paper is to identify individual terms in a superposition of heat kernels on a graph. We establish geometric conditions on the vertices at which these heat kernels are centered and find bounds on the time parameter governing the evolution under the heat semigroup that guarantee a successful recovery. This result can be viewed as a type of deconvolution on a graph.
Variational problems concerning length distances in metric spaces

Given a locally compact, complete metric space $({\rm X},{\sf D})$ and an open set $\Omega\subseteq{\rm X}$, we study the class of length distances $\sf d$ on $\Omega$ that are bounded from above and below by fixed multiples of the ambient distance $\sf D$. More precisely, we prove that the uniform convergence on compact sets of distances in this class is equivalent to the $\Gamma$-convergence of several associated variational problems. Along the way, we fix some oversights appearing in the previous literature.