Miracle-dz/UniFAR-FTUs huggingface dataset:
Simplified Algorithm for Trigonometry Meshes with Attributes Based on Quadric Error Metric

A simple and fast algorithm is presented, which is based on iterative edge collapse and quadric error metric to simplify mesh models with attributs. The algorithm uses quadric of geometry to measure errors in geometry and assigns a separate quadric to each attribute to measure errors in attribute. The sum of geometry error and attribute error is used to control the order of simplification. Edge collapse sorts all the edges by their collapse cost and every time chooses the edge with minimal cost to collapse iteratively until meeting the final conditions given. Quadric error metric takes the square distance between point and plane as error metric. Practical examples show that the algorithm can preserve attribute detail of the original mesh and guarantee that the simplified mesh matches well with the original one in color and texture attributes.
Determination of the strong coupling constant using available experimental data

We determine the strong coupling constant αs up to 4-loop in perturbative QCD. Testing QCD requires the measurement of αs over ranges of energy scales. In this analysis, the value of αs is determined from the unpolarized structure functions data points by minimizing the χ2 function between the theory result and experimental data. Using perturbative QCD calculations from threshold corrections, we obtain αs(M2Z) = 0.1139±0.0020 at N3LO which is in good agreement with the very recently results from the inclusive jet cross section in pp̄ collisions at √s = 1.96 TeV.
The Thermodynamic Limit of Indoor Photovoltaics Based on Energetically‐Disordered Molecular Semiconductors

Due to their tailorable optical properties, organic semiconductors show considerable promise for use in indoor photovoltaics (IPVs), which present a sustainable route for powering ubiquitous “Internet‐of‐Things” devices in the coming decades. However, owing to their excitonic and energetically disordered nature, organic semiconductors generally display considerable sub‐gap absorption and relatively large non‐radiative losses in solar cells. To optimize organic semiconductor‐based photovoltaics, it is therefore vital to understand how energetic disorder and non‐radiative recombination limit the performance of these devices under indoor light sources. In this work, we explore how energetic disorder, sub‐optical gap absorption, and non‐radiative open‐circuit voltage losses detrimentally affect the upper performance limits of organic semiconductor‐based IPVs. Based on these considerations, we provide realistic upper estimates for the power conversion efficiency. Energetic disorder, inherently present in molecular semiconductors, is generally found to shift the optimal optical gap from 1.83 to ≈1.9 eV for devices operating under light emitting diode spectra. Finally, we also describe a methodology (accompanied by a computational tool with a graphical user interface) for predicting IPV performance under arbitrary illumination conditions. Using this methodology, we estimate the indoor power conversion efficiencies of several photovoltaic materials, including the state‐of‐the‐art systems PM6:Y6 and PM6:BTP‐eC9.
Reduced Contraction Costs of Corner-Transfer Methods for PEPS

We propose a pair of approximations that allows the leading order computational cost of contracting an infinite projected entangled-pair state (iPEPS) to be reduced from $\mathcal{O}(\chi^3D^6)$ to $\mathcal{O}(\chi^3D^3)$ when using a corner-transfer approach. The first approximation involves (i) reducing the environment needed for truncation of the boundary tensors (ii) relies on the sequential contraction and truncation of bra and ket indices, rather than doing both together as with the established algorithm. To verify the algorithm, we perform benchmark simulations over square lattice Heisenberg model and obtain results that are comparable to the standard iPEPS algorithm. The improvement in computational cost enables us to perform large bond dimension calculations, extending its potential to solve challenging problems.
HOSSnet: an Efficient Physics-Guided Neural Network for Simulating Crack Propagation

Hybrid Optimization Software Suite (HOSS), which is a combined finite-discrete element method (FDEM), is one of the advanced approaches to simulating high-fidelity fracture and fragmentation processes but the application of pure HOSS simulation is computationally expensive. At the same time, machine learning methods, shown tremendous success in several scientific problems, are increasingly being considered promising alternatives to physics-based models in the scientific domains. Thus, our goal in this work is to build a new data-driven methodology to reconstruct the crack fracture accurately in the spatial and temporal fields. We leverage physical constraints to regularize the fracture propagation in the long-term reconstruction. In addition, we introduce perceptual loss and several extra pure machine learning optimization approaches to improve the reconstruction performance of fracture data further. We demonstrate the effectiveness of our proposed method through both extrapolation and interpolation experiments. The results confirm that our proposed method can reconstruct high-fidelity fracture data over space and time in terms of pixel-wise reconstruction error and structural similarity. Visual comparisons also show promising results in long-term
Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training

Hyperparameter tuning of deep learning models can lead to order-of-magnitude performance gains for the same amount of compute. Despite this, systematic tuning is uncommon, particularly for large models, which are expensive to evaluate and tend to have many hyperparameters, necessitating difficult judgment calls about tradeoffs, budgets, and search bounds. To address these issues and propose a practical method for robustly tuning large models, we present Cost-Aware Pareto Region Bayesian Search (CARBS), a Bayesian optimization algorithm that performs local search around the performance-cost Pareto frontier. CARBS does well even in unbounded search spaces with many hyperparameters, learns scaling relationships so that it can tune models even as they are scaled up, and automates much of the"black magic"of tuning. Among our results, we effectively solve the entire ProcGen benchmark just by tuning a simple baseline (PPO, as provided in the original ProcGen paper). We also reproduce the model size vs. training tokens scaling result from the Chinchilla project (Hoffmann et al. 2022), while simultaneously discovering scaling laws for every other hyperparameter, via an easy automated process that uses significantly less compute and is applicable to any deep learning problem (not just language models).
Improving Image Tracing with Convolutional Autoencoders by High-Pass Filter Preprocessing

The process of transforming a raster image into a vector representation is known as image tracing. This study looks into several processing methods that include high-pass filtering, autoencoding, and vectorization to extract an abstract representation of an image. According to the findings, rebuilding an image with autoencoders, high-pass filtering it, and then vectorizing it can represent the image more abstractly while increasing the effectiveness of the vectorization process.
Analysis of parameter combinations for optimal soliton microcomb generation efficiency in a simple single-cavity scheme

Dissipative Kerr solitons generated in high-Q optical microresonators provide unique opportunities for different up-to-date applications. Increasing the generation efficiency of of such signals is a problem of paramount importance. We provided comprehensive analysis and found optimal conditions providing maximal pump-to-comb conversion efficiency (up to 100\%) for the cases of a free-running and self-injection-locked pump lasers. The dependence of the optimal coupling rate on the pump power was revealed, in addition to the trade-off relations balancing the efficiency and the comb line number. The methods to increase the total comb power were also discussed.
GBSD: Generative Bokeh with Stage Diffusion

The bokeh effect is an artistic technique that blurs out-of-focus areas in a photograph and has gained interest due to recent developments in text-to-image synthesis and the ubiquity of smart-phone cameras and photo-sharing apps. Prior work on rendering bokeh effects have focused on post hoc image manipulation to produce similar blurring effects in existing photographs using classical computer graphics or neural rendering techniques, but have either depth discontinuity artifacts or are restricted to reproducing bokeh effects that are present in the training data. More recent diffusion based models can synthesize images with an artistic style, but either require the generation of high-dimensional masks, expensive fine-tuning, or affect global image characteristics. In this paper, we present GBSD, the first generative text-to-image model that synthesizes photorealistic images with a bokeh style. Motivated by how image synthesis occurs progressively in diffusion models, our approach combines latent diffusion models with a 2-stage conditioning algorithm to render bokeh effects on semantically defined objects. Since we can focus the effect on objects, this semantic bokeh effect is more versatile than classical rendering techniques. We evaluate GBSD both quantitatively and qualitatively and demonstrate its ability to be applied in both text-to-image and image-to-image settings.
Vectorization of the density matrix and quantum simulation of the von Neumann equation of time-dependent Hamiltonians

Based oh the properties of Lie algebras, in this work we develop a general framework to linearize the von-Neumann equation rendering it in a suitable form for quantum simulations. We show that one of these linearizations of the von-Neumann equation corresponds to the standard case in which the state vector becomes the column stacked elements of the density matrix and the Hamiltonian superoperator takes the form $I\otimes H-H^\top \otimes I$ where $I$ is the identity matrix and $H$ is the standard Hamiltonian. It is proven that this particular form belongs to a wider class of ways of linearizing the von Neumann equation that can be categorized by the algebra from which they originated. Particular attention is payed to Hermitian algebras that yield real density matrix coefficients substantially simplifying the quantum tomography of the state vector. Based on this ideas, a quantum algorithm to simulate the dynamics of the density matrix is proposed. It is shown that this method, along with the unique properties of the algebra formed by Pauli strings allows to avoid the use of Trotterization hence considerably reducing the circuit depth. Even though we have used the special case of the algebra formed by the Pauli strings, the algorithm can be readily adapted to other algebras. The algorithm is demonstrated for two toy Hamiltonians using the IBM noisy quantum circuit simulator.
Better Generalization with Semantic IDs: A case study in Ranking for Recommendations

Training good representations for items is critical in recommender models. Typically, an item is assigned a unique randomly generated ID, and is commonly represented by learning an embedding corresponding to the value of the random ID. Although widely used, this approach have limitations when the number of items are large and items are power-law distributed -- typical characteristics of real-world recommendation systems. This leads to the item cold-start problem, where the model is unable to make reliable inferences for tail and previously unseen items. Removing these ID features and their learned embeddings altogether to combat cold-start issue severely degrades the recommendation quality. Content-based item embeddings are more reliable, but they are expensive to store and use, particularly for users' past item interaction sequence. In this paper, we use Semantic IDs, a compact discrete item representations learned from content embeddings using RQ-VAE that captures hierarchy of concepts in items. We showcase how we use them as a replacement of item IDs in a resource-constrained ranking model used in an industrial-scale video sharing platform. Moreover, we show how Semantic IDs improves the generalization ability of our system, without sacrificing top-level metrics.
Depinning transition of self-propelled particles

A depinning transition is observed in a variety of contexts when a certain threshold force must be applied to drive a system out of an immobile state. A well-studied example is the depinning of colloidal particles from a corrugated landscape, whereas its active-matter analogue has remained unexplored. We discuss how active noise due to self-propulsion impacts the nature of the transition: it causes a change of the critical exponent from 1/2 for quickly reorienting particles to 3/2 for slowly reorienting ones. In between these analytically tractable limits, the drift velocity exhibits a superexponential behavior as is corroborated by high-precision data. Giant diffusion phenomena occur in the two different regimes. Our predictions appear amenable to experimental tests, lay foundations for insight into the depinning of collective variables in active matter, and are relevant for any system with a saddle-node bifurcation in the presence of a bounded noise.
Exploring Stellar Cluster and Feedback-driven Star Formation in the Galactic Mid-infrared Bubble [HKS2019] E70

We present a comprehensive analysis of the Galactic mid-infrared bubble [HKS2019] E70 (E70) by adopting a multiwavelength approach to understand the physical environment and star formation scenario around it. We identified a small (radius ∼1.7 pc) stellar cluster inside the E70 bubble, and its distance is estimated as 3.26 ± 0.45 kpc. This cluster is embedded in the molecular cloud and hosts massive stars, as well as young stellar objects (YSOs), suggesting active star formation in the region. The spectral type of the brightest star (M1) of the E70 cluster is estimated as O9V, and a circular ring/shell of gas and dust is found around it. The diffuse radio emission inside this ring/shell, the excess pressure exerted by the massive star M1 at the YSO’s core, and the distribution of photodissociation regions, a class I YSO, and two ultracompact H ii regions on the rim of this ring/shell clearly suggest positive feedback of the massive star M1 in the region. We also found a low-density shell-like structure in the 12CO(J = 1–0) molecular emission along the perimeter of the E70 bubble. The velocity structure of the 12CO emission suggests that the feedback from the massive star appears to have expelled the molecular material, and the subsequent swept-up material is what appears as the E70 bubble.
Contrastive Loss is All You Need to Recover Analogies as Parallel Lines

While static word embedding models are known to represent linguistic analogies as parallel lines in high-dimensional space, the underlying mechanism as to why they result in such geometric structures remains obscure. We find that an elementary contrastive-style method employed over distributional information performs competitively with popular word embedding models on analogy recovery tasks, while achieving dramatic speedups in training time. Further, we demonstrate that a contrastive loss is sufficient to create these parallel structures in word embeddings, and establish a precise relationship between the co-occurrence statistics and the geometric structure of the resulting word embeddings.
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks

The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years. In NLP, especially Large Language Models (LLMs) have experienced a rapid series of breakthroughs very recently. We envision that the two fields can radically push the boundaries of each other through tight integration. To showcase this vision, we explore the potential of a symbiotic relationship between AutoML and LLMs, shedding light on how they can benefit each other. In particular, we investigate both the opportunities to enhance AutoML approaches with LLMs from different perspectives and the challenges of leveraging AutoML to further improve LLMs. To this end, we survey existing work, and we critically assess risks. We strongly believe that the integration of the two fields has the potential to disrupt both fields, NLP and AutoML. By highlighting conceivable synergies, but also risks, we aim to foster further exploration at the intersection of AutoML and LLMs.
Geometric Wide-Angle Camera Calibration: A Review and Comparative Study

Wide-angle cameras are widely used in photogrammetry and autonomous systems which rely on the accurate metric measurements derived from images. To find the geometric relationship between incoming rays and image pixels, geometric camera calibration (GCC) has been actively developed. Aiming to provide practical calibration guidelines, this work surveys the existing GCC tools and evaluates the representative ones for wide-angle cameras. The survey covers camera models, calibration targets, and algorithms used in these tools, highlighting their properties and the trends in GCC development. The evaluation compares six target-based GCC tools, namely, BabelCalib, Basalt, Camodocal, Kalibr, the MATLAB calibrator, and the OpenCV-based ROS calibrator, with simulated and real data for wide-angle cameras described by four parametric projection models. These tests reveal the strengths and weaknesses of these camera models, as well as the repeatability of these GCC tools. In view of the survey and evaluation, future research directions of wide-angle GCC are also discussed.
Noise variance estimators in state space models based on the method of moments

In this paper we suggest distribution-free estimators for the noise variances of models in state space representation for use with the Kalman Filter. They are based on the method of moments applied to the variances of transformations of the observations sample and can be used both in univariate and multivariate models. It is shown that these estimators are unbiased and consistent and, for the univariate case, their variances are evaluated. They can be used, also, when the measurement equation has time varying coefficients.
Branching laws for spherical harmonics on superspaces in exceptional cases

It turns out that harmonic analysis on the superspace Rm|2n is quite parallel to the classical theory on the Euclidean space Rm unless the superdimension M:=m-2n is even and non-positive. The underlying symmetry is given by the orthosymplectic superalgebra osp(m|2n). In this paper, when the symmetry is reduced to osp(m-1|2n) we describe explicitly the corresponding branching laws for spherical harmonics on Rm|2n also in exceptional cases, i.e, when M − 1 ∈ −2N0. In unexceptional cases, these branching laws are well-known and quite analogous as in the Euclidean framework.
On the $p$-adic limit of class numbers along a pro-$p$-extension

Let $K/k$ be a pro-$p$-extension over a number field $k$ whose Galois group is finitely generated and $k_0\subseteq k_1\subseteq\cdots\subseteq k_n\subseteq\cdots$ an ascending sequence of intermediate fields of $K/k$ such that $k_n/k$ is normal, $[k_n:k]<\infty$ and $\bigcup_{n\ge 0} k_n=K$. We will show by using representation theory of finite groups that the non-$p$-part $h_n(p')$ of the class number of $k_n$ converges $p$-adically as $n\rightarrow\infty$, and the limit is independent to the choice of $k_n$'s. Also, in the case where $K/k$ is the cyclotomic $\mathbb{Z}_p$-extension over an abelian number field $k$, we will take an analytic approach and obtain certain enigmatic relationships between the $p$-adic limits of vaious arithmetic invariants along $K/k$, namely, the class number, the ratio of $p$-adic regulator and the square root of the discriminant, and the order of the algebraic $K_2$-group of the ring of integers.
Learning to Stabilize High-dimensional Unknown Systems Using Lyapunov-guided Exploration

Designing stabilizing controllers is a fundamental challenge in autonomous systems, particularly for high-dimensional, nonlinear systems that cannot be accurately modeled using differential equations. Lyapunov theory offers a robust solution for stabilizing control systems. Still, current methods relying on Lyapunov functions require access to complete dynamics or samples of system executions throughout the entire state space. Consequently, they are impractical for high-dimensional systems. In this paper, we introduce a novel framework, LYGE, for learning stabilizing controllers specifically tailored to high-dimensional, unknown systems. LYGE employs Lyapunov theory to iteratively guide the search for samples during exploration while simultaneously learning the local system dynamics, control policy, and Lyapunov functions. We demonstrate its scalability on highly complex systems, including a high-fidelity F-16 jet model from the Air Force featuring a 16D state space and a 4D input space. Experimental results indicate that, compared to prior works in reinforcement learning, imitation learning, and neural certificates, LYGE reduces the distance to the goal by 50% while requiring only 5% to 32% of the samples. Furthermore, we demonstrate that our algorithm can be extended to learn controllers guided by alternative certificate functions for unknown systems.
LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming

Open-domain dialogue systems have made promising progress in recent years. While the state-of-the-art dialogue agents are built upon large-scale social media data and large pre-trained models, there is no guarantee these agents could also perform well in fast-growing scenarios, such as live streaming, due to the bounded transferability of pre-trained models and biased distributions of public datasets from Reddit and Weibo, etc. To improve the essential capability of responding and establish a benchmark in the live open-domain scenario, we introduce the LiveChat dataset, composed of 1.33 million real-life Chinese dialogues with almost 3800 average sessions across 351 personas and fine-grained profiles for each persona. LiveChat is automatically constructed by processing numerous live videos on the Internet and naturally falls within the scope of multi-party conversations, where the issues of Who says What to Whom should be considered. Therefore, we target two critical tasks of response modeling and addressee recognition and propose retrieval-based baselines grounded on advanced techniques. Experimental results have validated the positive effects of leveraging persona profiles and larger average sessions per persona. In addition, we also benchmark the transferability of advanced generation-based models on LiveChat and pose some future directions for current challenges.
Quasigeostrophic Dynamics of the Tropopause

Abstract The dynamical properties of potential temperature anomalies on the tropopause are analyzed for quasigeo-strophic flow on an f plane. The potential vorticity is taken to he piecewise constant, with a single discontinuity at the tropopause. The tropopause potential temperature, on scales too small to feel the lower boundary, is found to be proportional to the tropopause geopotential height. The constant of proportionality is the geometric mean of the stratospheric and tropospheric lapse rates. Results from a general circulation model are found to be in agreement with this prediction. The streamfunction associated with a combination of anomalies on the lower boundary and tropopause is also derived. The solution, determined completely by the potential temperature distributions, in general has a nonzero velocity at the lower boundary. Applying the theory to the time-mean zonal-mean jets, which must have a near-zero velocity at the ground, imposes a constraint on parameters defining the jet. The dynami.