Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Dynamics Descriptors for Metal Halide Perovskites

Metal halide perovskites have shown extraordinary performance in solar energy conversion technologies. They have been classified as “soft semiconductors” due to their flexible corner-sharing octahedral networks and polymorphous nature. Understanding the local and average structures continues to be challenging for both modeling and experiments. Here, we report the quantitative analysis of structural dynamics in time and space from molecular dynamics simulations of perovskite crystals. The compact descriptors provided cover a wide variety of structural properties, including octahedral tilting and distortion, local lattice parameters, molecular orientations, as well as their spatial correlation. To validate our methods, we have trained a machine learning force field (MLFF) for methylammonium lead bromide (CH3NH3PbBr3) using an on-the-fly training approach with Gaussian process regression. The known stable phases are reproduced, and we find an additional symmetry-breaking effect in the cubic and tetragonal phases close to the phase-transition temperature. To test the implementation for large trajectories, we also apply it to 69,120 atom simulations for CsPbI3 based on an MLFF developed using the atomic cluster expansion formalism. The structural dynamics descriptors and Python toolkit are general to perovskites and readily transferable to more complex compositions.
Buoyant displacement flows in slightly non-uniform channels

We consider displacement flows in slightly diverging or converging plane channels. The two fluids are miscible and buoyancy is significant. We assume that the channel is oriented close to horizontal. Employing a classical lubrication approximation, we simplify the governing equations to furnish a semi-analytical solution for the flux functions. Then, we demonstrate how the non-uniformity of the displacement flow geometry can affect the propagation of the interface between the heavy and light fluids in time, for various parameters studied, e.g. the viscosity ratio, a buoyancy number and rheological features. By setting the molecular diffusion effects to zero, certain solution behaviours at longer times can be practically predicted through the associated hyperbolic problem, using which it becomes possible to directly compute the interfacial features of interest, e.g. leading and trailing front heights and speeds. For a Newtonian displacement flow in a converging or uniform channel, as the buoyancy number increases from zero, we are able to classify three flow regimes based on the behaviour of the trailing front near the top of the channel: a no-back-flow regime, a stationary interface flow regime, and a sustained back-flow regime. For the case of a diverging channel flow, the sustained back-flow regime is replaced by an eventually stationary interface flow regime. In addition, as the displacement flow progresses, the leading front speed typically increases (decreases) in a converging (diverging) channel, while the opposite is usually true for the front height. For the no-back-flow regime (i.e. with small buoyancy), the solution of the displacement flow at long times in all the geometries considered converges to a similarity form, while no similarity form is found for the other flow regimes. As the displacement flow develops, frontal diffusive effects are reduced (enhanced) in a converging (diverging) channel and multiple fronts are progressively less (more) present in a converging (diverging) channel. Regarding non-Newtonian effects, a shear-thinning fluid displacing a Newtonian fluid exhibits an increasingly fast front that has a short height in a converging channel. When a yield stress is present in the displaced fluid, it is possible to find residual wall layers of displaced fluid that are completely static. These layers disappear at a certain critical downstream distance in a converging channel while they appear at a critical distance in a diverging channel. Finally, the combination of strong buoyant and yield-stress effects can modify the destiny of a second front that follows the leading front.
RAMiT: Reciprocal Attention Mixing Transformer for Lightweight Image Restoration

Although many recent works have made advancements in the image restoration (IR) field, they often suffer from an excessive number of parameters. Another issue is that most Transformer-based IR methods focus only on either local or global features, leading to limited receptive fields or deficient parameter issues. To address these problems, we propose a lightweight IR network, Reciprocal Attention Mixing Transformer (RAMiT). It employs our proposed dimensional reciprocal attention mixing Transformer (D-RAMiT) blocks, which compute bi-dimensional (spatial and channel) self-attentions in parallel with different numbers of multi-heads. The bi-dimensional attentions help each other to complement their counterpart's drawbacks and are then mixed. Additionally, we introduce a hierarchical reciprocal attention mixing (H-RAMi) layer that compensates for pixel-level information losses and utilizes semantic information while maintaining an efficient hierarchical structure. Furthermore, we revisit and modify MobileNet V1 and V2 to attach efficient convolutions to our proposed components. The experimental results demonstrate that RAMiT achieves state-of-the-art performance on multiple lightweight IR tasks, including super-resolution, color denoising, grayscale denoising, low-light enhancement, and deraining. Codes are available at https://github.com/rami0205/RAMiT.
Patterns in Docker Compose Multi-Container Orchestration

Software design patterns present general code solutions to common software design problems. Modern software systems rely heavily on containers for organizing and orchestrating their constituent service components. Yet, despite the prevalence of ready-to-use Docker service images ready to participate in multi-container orchestration, developers do not have much guidance on how to develop their own multi-container Docker orchestrations. Thus in this work, we curate a dataset of successful projects that employ Docker Compose as an orchestration tool; then, we engage in qualitative and quantitative analysis of Docker Compose configurations. The collection of data and analysis enables the identification and naming of repeating patterns of deployment and orchestration employed by numerous successful open-source projects, much like software design patterns. These patterns highlight how software systems are orchestrated in the wild and can give examples to anybody wishing to develop their container orchestrations. These contributions also advance empirical research in software engineering patterns as evidence is provided about how Docker Compose is used.
TrustSER: On the Trustworthiness of Fine-tuning Pre-trained Speech Embeddings For Speech Emotion Recognition

Recent studies have explored the use of pre-trained embeddings for speech emotion recognition (SER), achieving comparable performance to conventional methods that rely on low-level knowledge-inspired acoustic features. These embeddings are often generated from models trained on large-scale speech datasets using self-supervised or weakly-supervised learning objectives. Despite the significant advancements made in SER through the use of pre-trained embeddings, there is a limited understanding of the trustworthiness of these methods, including privacy breaches, unfair performance, vulnerability to adversarial attacks, and computational cost, all of which may hinder the real-world deployment of these systems. In response, we introduce TrustSER, a general framework designed to evaluate the trustworthiness of SER systems using deep learning methods, with a focus on privacy, safety, fairness, and sustainability, offering unique insights into future research in the field of SER. Our code is publicly available under: https://github.com/usc-sail/trust-ser.
Boundary regularity for anisotropic minimal Lipschitz graphs

We prove that $m$-dimensional Lipschitz graphs in any codimension with $C^{1,\alpha}$ boundary and anisotropic mean curvature bounded in $L^p$, $p>m$, are regular at every boundary point with density bounded above by $1/2 +\sigma$, provided the anisotropic energy satisfies the uniform scalar atomic condition.
Pseudo-Label Training and Model Inertia in Neural Machine Translation

Like many other machine learning applications, neural machine translation (NMT) benefits from over-parameterized deep neural models. However, these models have been observed to be brittle: NMT model predictions are sensitive to small input changes and can show significant variation across re-training or incremental model updates. This work studies a frequently used method in NMT, pseudo-label training (PLT), which is common to the related techniques of forward-translation (or self-training) and sequence-level knowledge distillation. While the effect of PLT on quality is well-documented, we highlight a lesser-known effect: PLT can enhance a model's stability to model updates and input perturbations, a set of properties we call model inertia. We study inertia effects under different training settings and we identify distribution simplification as a mechanism behind the observed results.
MALM: Mask Augmentation based Local Matching for Food-Recipe Retrieval

Image-to-recipe retrieval is a challenging vision-to-language task of significant practical value. The main challenge of the task lies in the ultra-high redundancy in the long recipe and the large variation reflected in both food item combination and food item appearance. A de-facto idea to address this task is to learn a shared feature embedding space in which a food image is aligned better to its paired recipe than other recipes. However, such supervised global matching is prone to supervision collapse, i.e., only partial information that is necessary for distinguishing training pairs can be identified, while other information that is potentially useful in generalization could be lost. To mitigate such a problem, we propose a mask-augmentation-based local matching network (MALM), where an image-text matching module and a masked self-distillation module benefit each other mutually to learn generalizable cross-modality representations. On one hand, we perform local matching between the tokenized representations of image and text to locate fine-grained cross-modality correspondence explicitly. We involve representations of masked image patches in this process to alleviate overfitting resulting from local matching especially when some food items are underrepresented. On the other hand, predicting the hidden representations of the masked patches through self-distillation helps to learn general-purpose image representations that are expected to generalize better. And the multi-task nature of the model enables the representations of masked patches to be text-aware and thus facilitates the lost information reconstruction. Experimental results on Recipe1M dataset show our method can clearly outperform state-of-the-art (SOTA) methods. Our code will be available at https://github.com/MyFoodChoice/MALM_Mask_Augmentation_based_Local_Matching-_for-_Food_Recipe_Retrieval
Massively Scalable Inverse Reinforcement Learning in Google Maps

Inverse reinforcement learning (IRL) offers a powerful and general framework for learning humans' latent preferences in route recommendation, yet no approach has successfully addressed planetary-scale problems with hundreds of millions of states and demonstration trajectories. In this paper, we introduce scaling techniques based on graph compression, spatial parallelization, and improved initialization conditions inspired by a connection to eigenvector algorithms. We revisit classic IRL methods in the routing context, and make the key observation that there exists a trade-off between the use of cheap, deterministic planners and expensive yet robust stochastic policies. This insight is leveraged in Receding Horizon Inverse Planning (RHIP), a new generalization of classic IRL algorithms that provides fine-grained control over performance trade-offs via its planning horizon. Our contributions culminate in a policy that achieves a 16-24% improvement in route quality at a global scale, and to the best of our knowledge, represents the largest published study of IRL algorithms in a real-world setting to date. We conclude by conducting an ablation study of key components, presenting negative results from alternative eigenvalue solvers, and identifying opportunities to further improve scalability via IRL-specific batching strategies.
CN and CO features: key indicators of red giant evolutionary phase in moderate-resolution X-shooter spectra

Data-driven analysis methods can help to infer physical properties of red giant stars where ‘gold-standard’ asteroseismic data are not available. The study of optical and infrared spectra of red giant stars with data-driven analyses has revealed that differences in oscillation frequencies and their separations are imprinted in said spectra. This makes it possible to confidently differentiate core helium burning red clump (RC) stars from those that are still on their first ascent of the red giant branch (RGB). We extend these studies to a tenfold larger wavelength range of 0.33–2.5 µm with the moderate-resolution VLT/X-shooter spectrograph. Our analysis of 49 stars with asteroseismic data from the K2 mission confirms that CN, CO, and CH features are indeed the primary carriers of spectroscopic information on the evolutionary stages of red giant stars. We report 215 informative features for differentiating the RC from the RGB within the range of 0.33–2.5 µm. This makes it possible for existing and future spectroscopic surveys to optimize their wavelength regions to deliver both a large variety of elemental abundances and reliable age estimates of luminous red giant stars.
Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation

Transformer-based speech self-supervised learning (SSL) models, such as HuBERT, show surprising performance in various speech processing tasks. However, huge number of parameters in speech SSL models necessitate the compression to a more compact model for wider usage in academia or small companies. In this study, we suggest to reuse attention maps across the Transformer layers, so as to remove key and query parameters while retaining the number of layers. Furthermore, we propose a novel masking distillation strategy to improve the student model's speech representation quality. We extend the distillation loss to utilize both masked and unmasked speech frames to fully leverage the teacher model's high-quality representation. Our universal compression strategy yields the student model that achieves phoneme error rate (PER) of 7.72% and word error rate (WER) of 9.96% on the SUPERB benchmark.
Moments of Colored Permutation Statistics on Conjugacy Classes

In this paper, we consider the moments of statistics on conjugacy classes of the colored permutation groups $\mathfrak{S}_{n,r}=\mathbb{Z}_r\wr \mathfrak{S}_n$. We first show that any fixed moment coincides on all conjugacy classes where all cycles have sufficiently long length. Additionally, for permutation statistics that can be realized via a process we call symmetric extensions, these moments are polynomials in $n$. Finally, for the descent statistic on the hyperoctahedral group $B_n\cong \mathfrak{S}_{n,2}$, we show that its distribution on conjugacy classes without short cycles satisfies a central limit theorem. Our results build on and generalize previous work of Fulman (\textit{J. Comb. Theory Ser. A.}, 1998), Hamaker and Rhoades (arXiv, 2022), and Campion Loth, Levet, Liu, Stucky, Sundaram, and Yin (arXiv, 2023). In particular, our techniques utilize the combinatorial framework introduced by Campion Loth, Levet, Liu, Stucky, Sundaram, and Yin.
Aum: The Primordial Signal

Motivated by the increasing significance of Indian Spirituality and its cornerstone, the Pranava Aum, the present article explores the results of a signal based nonlinear analysis of Aum recital. Aum is seen to possess a rich frequency profile, with an array of integral and non-integral harmonic components. The phase portrait of Aum is rich, dense and ornamental, typical of chaotic systems. Polar plots of Aum show a remarkable uniformity in phase distribution. The bispectrum shows ordered pattern of scintillations, ruling out random processes. Quantitative analyses of Aum assert the presence of chaos, while also revealing a high degree of uncertainty, instability and self-similarity in the underlying process. In comparisons with chaos observed in solar cell open circuit output voltage, the similarities between Aum and solar processes become apparent. It is hoped that the results discussed in the present article throw light on Aum, as an audio signal, helping to understand the underlying features and characteristics of this primordial signal. The chaotic nature of Aum speaks volumes about the intricacies and complexities underlying the universal primordial sound, suggesting that it indeed possesses the necessary complexity to be the universal wave function. The similarity with solar cell based signals indicates the ubiquity of this primordial chaos in natural systems, further accentuating the omnipresent nature of Aum.
On the validity of the bipolaron model for undoped and AlCl 4-doped PEDOT

The conducting polymer poly(3,4-ethylenedioxythiophene) (PEDOT) is one of the most highly researched materials, yet electronic structure investigations of conducting polymers are still uncommon. The bipolaron model has traditionally been the dominant attempt to explain the electronic structure of PEDOT. Though recent theoretical studies have begun to move away from this model, some aspects remain commonplace, such as the concepts of bipolarons or polaron pairs. In this work, we use density functional theory to investigate the electronic structure of undoped and AlCl 4-doped PEDOT oligomers. By considering the influence of oligomer length, oxidation or doping level and spin state, we find no evidence for self-localisation of positive charges in PEDOT as predicted by the bipolaron model. Instead, we find that a single or twin peak structural distortion can occur at any oxidation or doping level. Rather than representing bipolarons or polaron pairs, these are electron distributions driven by a range of factors, which also disproves the concept of polaron pairs. Localisation of distortions does occur in the doped case, although distortions can span an arbitrary number of nearby anions. Furthermore, conductivity in conducting polymers has been experimentally observed to reduce at very high doping levels. We show that at high anion concentrations, the non-bonding orbitals of the anions cluster below the HOMO-LUMO gap and begin to mix into the HOMO of the overall system. We propose that this mixing of highly localised anionic orbitals into the HOMO reduces the conductivity of the polymer and contributes to the reduced conductivity previously observed.
Moment Matching Denoising Gibbs Sampling

Energy-Based Models (EBMs) offer a versatile framework for modeling complex data distributions. However, training and sampling from EBMs continue to pose significant challenges. The widely-used Denoising Score Matching (DSM) method for scalable EBM training suffers from inconsistency issues, causing the energy model to learn a `noisy' data distribution. In this work, we propose an efficient sampling framework: (pseudo)-Gibbs sampling with moment matching, which enables effective sampling from the underlying clean model when given a `noisy' model that has been well-trained via DSM. We explore the benefits of our approach compared to related methods and demonstrate how to scale the method to high-dimensional datasets.
Survival probability of unstable states in coupled-channels: Nonexponential decay of threshold cusp

We investigate the survival probability of unstable states, the time-dependence of an initial state, in coupled channels. First, we extend the formulation of the survival probability from single channel to coupled channels (two channels). We derive an exact general expression of the two-channel survival probability using uniformization, a method which makes the coupled-channel S matrix single-valued, and the Mittag-Leffler expansion, i.e. a pole expansion. Second, we calculate the time dependence of the two-channel survival probability by employing the derived expression. It is the minimal distance between the pole and the physical region in the complex energy plane, not the imaginary part of the pole energy, which determines not only the energy spectrum of the Green's function but also the survival probability. The survival probability of the"threshold-cusp"caused by a pole on the unusual complex-energy Riemann sheet is shown to decay, not grow in time though the imaginary part of the pole energy is positive. We also show that the decay of the"threshold-cusp"is non-exponential. Thus, the"threshold-cusp"is shown to be a new type of unstable mode, which is found only in coupled channels.
Interfacial waves and the dynamics of backflow in falling liquid films

Abstract By studying the dynamics of the streamwise pressure gradient at the wavefront of travelling interfacial waves, we investigate the formation and evolution of backflow regions for the sinusoidal and teardrop-shaped surface wave regimes of laminar falling liquid films. The magnitude of the wavefront streamwise pressure gradient grows as the flow inlet disturbance increases in amplitude and steepness. At large enough values, the adverse pressure gradient induces flow separation and subsequently backflow at the large-amplitude wavefront. The backflow region evolves from a closed circulation to an open vortex as the wave grows to saturation. The dynamics of the streamwise pressure gradient at the sinusoidal wavefront approaches a stable fixed point at saturation. Thus, the open vortex retains its structure as the wave continues downstream. The streamwise pressure gradient at the wavefront of the teardrop-shaped pulse evolves similarly to a time-periodic function with multiple minima/maxima. This phenomenon is a consequence of the interaction between the teardrop-shaped wave and newly formed preceding capillary waves. The nature of the teardrop pulse–capillary wave interaction is such that a decrease in magnitude of the streamwise pressure gradient at the teardrop-shaped wavefront is followed by an increase at the capillary wavefront and vice versa. The increased adverse pressure gradient at the capillary wavefront induces a second open vortex backflow, while the teardrop-shaped wavefront’s open vortex reverts to a closed circulation. This interaction between the waves continues as the teardrop pulse–capillary wavetrain travels downstream, leading to multiple capillary waves and backflow regions.
Complexity of Deep Neural Networks from the Perspective of Functional Equivalence

In this paper, we investigate the complexity of feed-forward neural networks by examining the concept of functional equivalence, which suggests that different network parameterizations can lead to the same function. We utilize the permutation invariance property to derive a novel covering number bound for the class of feedforward neural networks, which reveals that the complexity of a neural network can be reduced by exploiting this property. We discuss the extensions to convolutional neural networks, residual networks, and attention-based models. We demonstrate that functional equivalence benefits optimization, as overparameterized networks tend to be easier to train since increasing network width leads to a diminishing volume of the effective parameter space. Our findings offer new insights into overparameterization and have significant implications for understanding generalization and optimization in deep learning.