Miracle-dz/UniFAR-FTUs huggingface dataset:
MixFlows: principled variational inference via mixed flows

This work presents mixed variational flows (MixFlows), a new variational family that consists of a mixture of repeated applications of a map to an initial reference distribution. First, we provide efficient algorithms for i.i. d. sampling, density evaluation, and unbiased ELBO estimation. We then show that MixFlows have MCMC-like convergence guarantees when the flow map is ergodic and measure-preserving, and provide bounds on the accumulation of error for practical implementations where the flow map is approximated. Finally, we develop an implementation of MixFlows based on uncorrected discretized Hamiltonian dynamics combined with deterministic momentum refreshment. Simulated and real data experiments show that MixFlows can provide more reliable posterior approximations than several black-box normalizing flows, as well as samples of comparable quality to those obtained from state-of-the-art MCMC methods.
Convergence and Generalization of Wide Neural Networks with Large Bias

This work studies training one-hidden-layer overparameterized ReLU networks via gradient descent in the neural tangent kernel (NTK) regime, where the networks' biases are initialized to some constant rather than zero. The tantalizing benefit of such initialization is that the neural network will provably have sparse activation through the entire training process, which enables fast training procedures. The first set of results characterizes the convergence of gradient descent training. Surprisingly, it is shown that the network after sparsification can achieve as fast convergence as the dense network, in comparison to the previous work indicating that the sparse networks converge slower. Further, the required width is improved to ensure gradient descent can drive the training error towards zero at a linear rate. Secondly, the networks' generalization is studied: a width-sparsity dependence is provided which yields a sparsity-dependent Rademacher complexity and generalization bound. To our knowledge, this is the first sparsity-dependent generalization result via Rademacher complexity. Lastly, this work further studies the least eigenvalue of the limiting NTK. Surprisingly, while it is not shown that trainable biases are necessary, trainable bias, which is enabled by our improved analysis scheme, helps to identify a nice data-dependent region where a much finer analysis of the NTK's smallest eigenvalue can be conducted. This leads to a much sharper lower bound on the NTK's smallest eigenvalue than the one previously known and, consequently, an improved generalization bound.
JDSR-GAN: Constructing an Efficient Joint Learning Network for Masked Face Super-Resolution

With the growing importance of preventing the COVID-19 virus in cyber-manufacturing security, face images obtained in most video surveillance scenarios are usually low resolution together with mask occlusion. However, most of the previous face super-resolution solutions can not efficiently handle both tasks in one model. In this work, we consider both tasks simultaneously and construct an efficient joint learning network, called JDSR-GAN, for masked face super-resolution tasks. Given a low-quality face image with mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a high-quality high-resolution face image. The discriminator utilizes some carefully designed loss functions to ensure the quality of the recovered face images. Moreover, we incorporate the identity information and attention mechanism into our network for feasible correlated feature expression and informative feature learning. By jointly performing denoising and face super-resolution, the two tasks can complement each other and attain promising performance. Extensive qualitative and quantitative results show the superiority of our proposed JDSR-GAN over some competitive methods.
LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain

Lately, propelled by the phenomenal advances around the transformer architecture, the legal NLP field has enjoyed spectacular growth. To measure progress, well curated and challenging benchmarks are crucial. However, most benchmarks are English only and in legal NLP specifically there is no multilingual benchmark available yet. Additionally, many benchmarks are saturated, with the best models clearly outperforming the best humans and achieving near perfect scores. We survey the legal NLP literature and select 11 datasets covering 24 languages, creating LEXTREME. To provide a fair comparison, we propose two aggregate scores, one based on the datasets and one on the languages. The best baseline (XLM-R large) achieves both a dataset aggregate score a language aggregate score of 61.3. This indicates that LEXTREME is still very challenging and leaves ample room for improvement. To make it easy for researchers and practitioners to use, we release LEXTREME on huggingface together with all the code required to evaluate models and a public Weights and Biases project with all the runs.
Data-driven intelligent computational design: Method, techniques, and applications

: Data-driven intelligent computational design (DICD) is a research hotspot emerged under the context of fast-developing artificial intelligence. It emphasizes on utilizing deep learning algorithms to extract and represent the design features hidden in historical or fabricated design process data, and then learn the combination and mapping patterns of these design features for the purposes of design solution retrieval, generation, optimization, evaluation, etc. Due to its capability of automatically and efficiently generating design solutions and thus supporting human-in-the-loop intelligent and innovative design activities, DICD has drawn the attentions from both academic and industrial fields. However, as an emerging research subject, there are still many unexplored issues that limit the theorical development and industrial application of DICD, such as specific dataset building, engineering design related feature engineering, systematic methods and techniques for DICD implementation, more entry points for DICD application in the entire product design life cycle, etc. In this regard, a systematic theorical reference for DICD implementation is established, including a general workflow for DICD project planning, an overall framework for DICD project implementation, the computing mechanisms for DICD implementation, key enabling technologies for detailed DICD implementation, and three application scenarios of DICD. The works provide a brief research status, key research topics, and more importantly a general road map for DICD implementation.
Locating multiple magnetized anomalies by geomagnetic monitoring

The presence of magnetized anomalies in the shell of the Earth interrupts its geomagnetic field. We consider the inverse problem of identifying the anomalies by monitoring the variation of the geomagnetic field. Motivated by the theoretical unique identifiability result in [5], we develop a novel numerical scheme of locating multiple magnetized anomalies. In our study, we do not assume that the source that generates the geomagnetic field, and the medium configurations of the Earth's core and the magnetized anomalies are a-priori known. The core of the reconstruction scheme is a novel imaging functional whose quantitative behaviours can be used to identify the anomalies. Both rigorous analysis and extensive numerical experiments are provided to verify the effectiveness and promising features of the proposed reconstruction scheme.
Audio-Visual Segmentation with Semantics

We propose a new problem called audio-visual segmentation (AVS), in which the goal is to output a pixel-level map of the object(s) that produce sound at the time of the image frame. To facilitate this research, we construct the first audio-visual segmentation benchmark, i.e., AVSBench, providing pixel-wise annotations for sounding objects in audible videos. It contains three subsets: AVSBench-object (Single-source subset, Multi-sources subset) and AVSBench-semantic (Semantic-labels subset). Accordingly, three settings are studied: 1) semi-supervised audio-visual segmentation with a single sound source; 2) fully-supervised audio-visual segmentation with multiple sound sources, and 3) fully-supervised audio-visual semantic segmentation. The first two settings need to generate binary masks of sounding objects indicating pixels corresponding to the audio, while the third setting further requires generating semantic maps indicating the object category. To deal with these problems, we propose a new baseline method that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process. We also design a regularization loss to encourage audio-visual mapping during training. Quantitative and qualitative experiments on AVSBench compare our approach to several existing methods for related tasks, demonstrating that the proposed method is promising for building a bridge between the audio and pixel-wise visual semantics. Code is available at https://github.com/OpenNLPLab/AVSBench. Online benchmark is available at http://www.avlbench. opennlplab.cn.
The origin of Definite Article Reduction in northern English dialects: evidence from dialect allomorphy

The phenomenon of Definite Article Reduction (DAR) is the realization of the definite article in northern British English dialects in a range of vowel-less forms, usually written t' in literature. The origin of DAR is assumed to be the assimilation of the initial fricative of the Middle English definite article þe to produce a te form, a sound change recorded for many dialects of Middle English. This article examines the validity of this hypothesis by analysing the distribution of fricative allomorphs in the modern dialects in comparison with the details of the Middle English change. The predicted distribution of fricative forms is not found at most localities, indicating that the development hypothesis is incorrect, but the available data are too scanty to suggest an alternative model.
Unlocking Deterministic Robustness Certification on ImageNet

Despite the promise of Lipschitz-based methods for provably-robust deep learning with deterministic guarantees, current state-of-the-art results are limited to feed-forward Convolutional Networks (ConvNets) on low-dimensional data, such as CIFAR-10. This paper investigates strategies for expanding certifiably robust training to larger, deeper models. A key challenge in certifying deep networks is efficient calculation of the Lipschitz bound for residual blocks found in ResNet and ViT architectures. We show that fast ways of bounding the Lipschitz constant for conventional ResNets are loose, and show how to address this by designing a new residual block, leading to the \emph{Linear ResNet} (LiResNet) architecture. We then introduce \emph{Efficient Margin MAximization} (EMMA), a loss function that stabilizes robust training by simultaneously penalizing worst-case adversarial examples from \emph{all} classes. Together, these contributions yield new \emph{state-of-the-art} robust accuracy on CIFAR-10/100 and Tiny-ImageNet under $\ell_2$ perturbations. Moreover, for the first time, we are able to scale up fast deterministic robustness guarantees to ImageNet, demonstrating that this approach to robust learning can be applied to real-world applications. We release our code on Github: \url{https://github.com/klasleino/gloro}.
MQT QMAP: Efficient Quantum Circuit Mapping

Quantum computing is an emerging technology that has the potential to revolutionize fields such as cryptography, machine learning, optimization, and quantum simulation. However, a major challenge in the realization of quantum algorithms on actual machines is ensuring that the gates in a quantum circuit (i.e., corresponding operations) match the topology of a targeted architecture so that the circuit can be executed while, at the same time, the resulting costs (e.g., in terms of the number of additionally introduced gates, fidelity, etc.) are kept low. This is known as the quantum circuit mapping problem. This summary paper provides an overview of QMAP-an open-source tool that is part of the Munich Quantum Toolkit (MQT) and offers efficient, automated, and accessible methods for tackling this problem. To this end, the paper first briefly reviews the problem. Afterwards, it shows how QMAP can be used to efficiently map quantum circuits to quantum computing architectures from both a user's and a developer's perspective. QMAP is publicly available as open-source at https://github.com/cda-tum/qmap.
Anomalous dispersion in a dipole flow geometry

The dispersion of a passive tracer in fluid flowing between a source and a sink in a Hele–Shaw geometry, characteristic of field scale flows in a layer or fracture, is considered. A combination of analytic and numerical techniques and complementary experimental measurements are employed, leading to a consistent picture. This dispersion process is found to be characterized by a power‐law decay in time of the tracer concentration, with an exponential cutoff at very long times, in strong contrast to the Gaussian behavior associated with the widely used quasi‐one‐dimensional (1‐D) models.
Minimizing Trajectory Curvature of ODE-based Generative Models

Recent ODE/SDE-based generative models, such as diffusion models, rectified flows, and flow matching, define a generative process as a time reversal of a fixed forward process. Even though these models show impressive performance on large-scale datasets, numerical simulation requires multiple evaluations of a neural network, leading to a slow sampling speed. We attribute the reason to the high curvature of the learned generative trajectories, as it is directly related to the truncation error of a numerical solver. Based on the relationship between the forward process and the curvature, here we present an efficient method of training the forward process to minimize the curvature of generative trajectories without any ODE/SDE simulation. Experiments show that our method achieves a lower curvature than previous models and, therefore, decreased sampling costs while maintaining competitive performance. Code is available at https://github.com/sangyun884/fast-ode.
Fire and Ice: Spitzer Infrared Spectrograph (IRS) Mid-Infrared Spectroscopy of IRAS F00183–7111

We report the detection of strong absorption and weak emission features in the 4-27 μm Spitzer Infrared Spectrograph (IRS) spectrum of the distant ultraluminous infrared galaxy IRAS F00183-7111 (z = 0.327). The absorption features of CO2 and CO gas, water ice, hydrocarbons, and silicates are indicative of a strongly obscured (A9.6 ≥ 5.4; AV ≥ 90) and complex line of sight through both the hot diffuse interstellar medium and shielded cold molecular clouds toward the nuclear power source. From the profile of the 4.67 μm CO fundamental vibration mode, we deduce that the absorbing gas is dense (n ∼ 106 cm-3) and warm (720 K) and has a CO column density of ∼1019.5 cm-2, equivalent to NH ∼ 1023.5 cm-2. The high temperature and density, as well as the small inferred size (<0.03 pc), locates this absorbing gas close to the power source of this region. Weak emission features of molecular hydrogen, polycyclic aromatic hydrocarbons (PAHs), and Ne+, likely associated with star formation, are detected against the 9.7 μm silicate feature, indicating an origin away from the absorbing region. Based on the 11.2 μm PAH flux, we estimate the star formation component to be responsible for up to 30% of the IR luminosity of the system. While our mid-infrared spectrum shows no telltale signs of active galactic nucleus (AGN) activity, the similarities to the mid-infrared spectra of deeply obscured sources (e.g., NGC 4418) and AGN hot dust (e.g., NGC 1068), as well as evidence from other wavelength regions, suggest that the power source hiding behind the optically thick dust screen may well be a buried AGN.
Zero-Shot Transfer of Haptics-Based Object Insertion Policies

Humans naturally exploit haptic feedback during contact-rich tasks like loading a dishwasher or stocking a bookshelf. Current robotic systems focus on avoiding unexpected contact, often relying on strategically placed environment sensors. Recently, contact-exploiting manipulation policies have been trained in simulation and deployed on real robots. However, they require some form of real-world adaptation to bridge the sim-to-real gap, which might not be feasible in all scenarios. In this paper we train a contact-exploiting manipulation policy in simulation for the contact-rich household task of loading plates into a slotted holder, which transfers without any fine-tuning to the real robot. We investigate various factors necessary for this zero-shot transfer, like time delay modeling, memory representation, and domain randomization. Our policy transfers with minimal sim-to-real gap and significantly outperforms heuristic and learnt baselines. It also generalizes well to a cup and plates of different sizes and weights. The project website is https://sites.google. com/view/compliant-object-insertion.
Phase transition patterns for coupled complex scalar fields at finite temperature and density

The phase transition patterns displayed by a model of two coupled complex scalar fields are studied at finite temperature and chemical potential. Possible phenomena like symmetry persistence and inverse symmetry breaking at high temperatures are analyzed. The effect of finite density is also considered and studied in combination with the thermal effects. The nonperturbative optimized perturbation theory method is considered and the results contrasted with perturbation theory. Applications of the results obtained are considered in the context of an effective model for condensation of kaons at high densities, which is of importance in the understanding of the color-flavor locked phase of quantum chromodynamics.
FedEBA+: Towards Fair and Effective Federated Learning via Entropy-Based Model

Ensuring fairness is a crucial aspect of Federated Learning (FL), which enables the model to perform consistently across all clients. However, designing an FL algorithm that simultaneously improves global model performance and promotes fairness remains a formidable challenge, as achieving the latter often necessitates a trade-off with the former. To address this challenge, we propose a new FL algorithm, FedEBA+, which enhances fairness while simultaneously improving global model performance. FedEBA+ incorporates a fair aggregation scheme that assigns higher weights to underperforming clients and an alignment update method. In addition, we provide theoretical convergence analysis and show the fairness of FedEBA+. Extensive experiments demonstrate that FedEBA+ outperforms other SOTA fairness FL methods in terms of both fairness and global model performance.
EMP-EVAL: A Framework for Measuring Empathy in Open Domain Dialogues

Measuring empathy in conversation can be challenging, as empathy is a complex and multifaceted psychological construct that involves both cognitive and emotional components. Human evaluations can be subjective, leading to inconsistent results. Therefore, there is a need for an automatic method for measuring empathy that reduces the need for human evaluations. In this paper, we proposed a novel approach EMP-EVAL, a simple yet effective automatic empathy evaluation method. The proposed technique takes the inﬂuence of Emotion, Cognitive and Emotional empathy. To the best knowledge, our work is the ﬁrst to system-atically measure empathy without the human-annotated provided scores. Experimental results demonstrate that our metrics can corre-late with human preference, achieving compa-rable results with human judgments.
VELOCITY AND MAGNETIC FIELD DISTRIBUTION IN A FORMING PENUMBRA

We present results from the analysis of high-resolution spectropolarimetric and spectroscopic observations of the solar photosphere and chromosphere, obtained shortly before the formation of a penumbra in one of the leading polarity sunspots of NOAA active region 11490. The observations were performed at the Dunn Solar Telescope of the National Solar Observatory on 2012 May 28, using the Interferometric Bidimensional Spectrometer. The data set is comprised of a 1 hr time sequence of measurements in the Fe i 617.3 nm and Fe i 630.25 nm lines (full Stokes polarimetry) and in the Ca ii 854.2 nm line (Stokes I only). We perform an inversion of the Fe i 630.25 nm Stokes profiles to derive magnetic field parameters and the line-of-sight (LOS) velocity at the photospheric level. We characterize chromospheric LOS velocities by the Doppler shift of the centroid of the Ca ii 854.2 nm line. We find that, before the formation of the penumbra, an annular zone of 3″–5″ width is visible around the sunspot. In the photosphere, we find that this zone is characterized by an uncombed structure of the magnetic field although no visible penumbra has formed yet. We also find that the chromospheric LOS velocity field shows several elongated structures characterized by downflow and upflow motions in the inner and outer parts of the annular zone, respectively.
On the domain derivative for scattering by impenetrable obstacles in chiral media

For scattering of electromagnetic waves in a chiral medium by some perfectly conducting inclusions, we study the dependence of the scattered field on the boundary of the inclusions and show its Frechet differentiability in appropriate spaces. Further, we derive a characterization of the derivative as a solution to some corresponding chiral boundary value problem. Our proof contains a new approach to rigorously derive this characterization.
Parsimonious System Identification from Fragmented Quantized Measurements

Quantization is the process of mapping an input signal from an infinite continuous set to a countable set with a finite number of elements. It is a non-linear irreversible process, which makes the traditional methods of system identification no longer applicable. In this work, we propose a method for parsimonious linear time invariant system identification when only quantized observations, discerned from noisy data, are available. More formally, given a priori information on the system, represented by a compact set containing the poles of the system, and quantized realizations, our algorithm aims at identifying the least order system that is compatible with the available information. The proposed approach takes also into account that the available data can be subject to fragmentation. Our proposed algorithm relies on an ADMM approach to solve a $\ell_{p},(0<p<1),$ quasi-norm objective problem. Numerical results highlight the performance of the proposed approach when compared to the $\ell_{1}$ minimization in terms of the sparsity of the induced solution.