Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximum Mean Discrepancy for Generalization in the Presence of Distribution and Missingness Shift

Covariate shifts are a common problem in predictive modeling on real-world problems. This paper proposes addressing the covariate shift problem by minimizing Maximum Mean Discrepancy (MMD) statistics between the training and test sets in either feature input space, feature representation space, or both. We designed three techniques that we call MMD Representation, MMD Mask, and MMD Hybrid to deal with the scenarios where only a distribution shift exists, only a missingness shift exists, or both types of shift exist, respectively. We find that integrating an MMD loss component helps models use the best features for generalization and avoid dangerous extrapolation as much as possible for each test sample. Models treated with this MMD approach show better performance, calibration, and extrapolation on the test set.
START: Straggler Prediction and Mitigation for Cloud Computing Environments Using Encoder LSTM Networks

Modern large-scale computing systems distribute jobs into multiple smaller tasks which execute in parallel to accelerate job completion rates and reduce energy consumption. However, a common performance problem in such systems is dealing with straggler tasks that are slow running instances that increase the overall response time. Such tasks can significantly impact the system’s Quality of Service (QoS) and the Service Level Agreements (SLA). To combat this issue, there is a need for automatic straggler detection and mitigation mechanisms that execute jobs without violating the SLA. Prior work typically builds reactive models that focus first on detection and then mitigation of straggler tasks, which leads to delays. Other works use prediction based proactive mechanisms, but ignore heterogeneous host or volatile task characteristics. In this paper, we propose a Straggler Prediction and Mitigation Technique (START) that is able to predict which tasks might be stragglers and dynamically adapt scheduling to achieve lower response times. Our technique analyzes all tasks and hosts based on compute and network resource consumption using an Encoder Long-Short-Term-Memory (LSTM) network. The output of this network is then used to predict and mitigate expected straggler tasks. This reduces the SLA violation rate and execution time without compromising QoS. Specifically, we use the CloudSim toolkit to simulate START in a cloud environment and compare it with state-of-the-art techniques (IGRU-SD, SGC, Dolly, GRASS, NearestFit and Wrangler) in terms of QoS parameters such as energy consumption, execution time, resource contention, CPU utilization and SLA violation rate. Experiments show that START reduces execution time, resource contention, energy and SLA violations by 13%, 11%, 16% and 19%, respectively, compared to the state-of-the-art approaches.
Periodic Variable Stars Modulated by Time-varying Parameters

Many astrophysical phenomena are time-varying, in the sense that their brightness changes over time. In the case of periodic stars, previous approaches assumed that changes in period, amplitude, and phase are well described by either parametric or piecewise-constant functions. With this paper, we introduce a new mathematical model for the description of the so-called modulated light curves, as found in periodic variable stars that exhibit smoothly time-varying parameters such as amplitude, frequency, and/or phase. Our model accounts for a smoothly time-varying trend and a harmonic sum with smoothly time-varying weights. In this sense, our approach is flexible because it avoids restrictive assumptions (parametric or piecewise-constant) about the functional form of the trend and amplitudes. We apply our methodology to the light curve of a pulsating RR Lyrae star characterized by the Blazhko effect. To estimate the time-varying parameters of our model, we develop a semi-parametric method for unequally spaced time series. The estimation of our time-varying curves translates into the estimation of time-invariant parameters that can be performed by ordinary least squares, with the following two advantages: modeling and forecasting can be implemented in a parametric fashion, and we are able to cope with missing observations. To detect serial correlation in the residuals of our fitted model, we derive the mathematical definition of the spectral density for unequally spaced time series. The proposed method is designed to estimate smoothly time-varying trends and amplitudes, as well as the spectral density function of the errors. We provide simulation results and applications to real data.
The Joy of Neural Painting

Neural Painters is a class of models that follows a GAN framework to generate brushstrokes, which are then composed to create paintings. GANs are great generative models for AI Art but they are known to be notoriously difficult to train. To overcome GAN's limitations and to speed up the Neural Painter training, we applied Transfer Learning to the process reducing it from days to only hours, while achieving the same level of visual aesthetics in the final paintings generated. We report our approach and results in this work.
Gauge Enhanced Quantum Criticality Between Grand Unifications: Categorical Higher Symmetry Retraction

Prior work [1] shows that the Standard Model (SM) naturally arises near a gapless quantum critical region between Georgi-Glashow (GG) su (5) and Pati-Salam (PS) su (4) × su (2) × su (2) models of quantum vacua (in a phase diagram or moduli space), by implementing a modiﬁed so (10) Grand Uniﬁcation (GUT) with a Spin(10) gauge group plus a new discrete Wess-Zumino Witten term matching a 4d nonperturbative global mixed gauge-gravity w 2 w 3 anomaly. In this work, we include Barr’s ﬂipped u (5) model into the quantum landscape, showing these four GUT-like models arise near the quantum criticality near SM. The SM and GG models can have either 15 or 16 Weyl fermions per generation, while the PS, ﬂipped u (5) , and the modiﬁed so (10) have 16n Weyl fermions. Highlights include: First, we ﬁnd the precise GG or ﬂipped u (5) gauge group requires to redeﬁne a Lie group U(5) ˆ q ≡ (SU(5) × ˆ q U(1)) / Z ˆ q with ˆ q = 2 or 3 (instead of non-isomorphic analog ˆ q = 1 or 4), and diﬀerent ˆ q are related by multiple covering. Second, for 16n Weyl fermions, we show that the GG and ﬂipped u (5) are two diﬀerent symmetry-breaking vacua of the same order parameter separated by a ﬁrst-order Landau-Ginzburg transition. We also show that analogous 3+1d deconﬁned quantum criticalities, both between GG and PS, and between the ﬂipped u (5) and PS, are beyond Landau-Ginzburg paradigm. Third, topological quantum criticality occurs by tuning between the 15n vs 16n scenarios. Fourth, we explore the generalized higher global symmetries in the SM and GUTs. Gauging the Z 2 ﬂip symmetry between GG and ﬂipped u (5) models, leads to a potential categorical higher symmetry that is a non-invertible global symmetry: within a gauge group , the fusion rule of 2d topological surface operator splits. Even if the mixed anomaly between Z 2 ﬂip symmetry and two U(1) magnetic 1-symmetries at IR is absent, the un-Higgs Spin(10) at UV retracts this categorical symmetry.
Composite Goodness-of-fit Tests with Kernels

Model misspecification can create significant challenges for the implementation of probabilistic models, and this has led to development of a range of robust methods which directly account for this issue. However, whether these more involved methods are required will depend on whether the model is really misspecified, and there is a lack of generally applicable methods to answer this question. In this paper, we propose one such method. More precisely, we propose kernel-based hypothesis tests for the challenging composite testing problem, where we are interested in whether the data comes from any distribution in some parametric family. Our tests make use of minimum distance estimators based on the maximum mean discrepancy and the kernel Stein discrepancy. They are widely applicable, including whenever the density of the parametric model is known up to normalisation constant, or if the model takes the form of a simulator. As our main result, we show that we are able to estimate the parameter and conduct our test on the same data (without data splitting), while maintaining a correct test level. Our approach is illustrated on a range of problems, including testing for goodness-of-fit of an unnormalised non-parametric density model, and an intractable generative model of a biological cellular network.
H2 Cooling and Gravitational Collapse of Supersonically Induced Gas Objects

We study the formation and gravitational collapse of supersonically induced gas objects (SIGOs) in the early universe. We run cosmological hydrodynamics simulations of SIGOs, including relative streaming motions between baryons and dark matter. Our simulations also follow nonequilibrium chemistry and molecular hydrogen cooling in primordial gas clouds. A number of SIGOs are formed in the run with fast-streaming motions of 2 times the rms of the cosmological velocity fluctuations. We identify a particular gas cloud that condensates by H2 cooling without being hosted by a dark matter halo. The SIGO remains outside the virial radius of its closest halo, and it becomes Jeans unstable when the central gas-particle density reaches ∼100 cm−3 with a temperature of ∼200 K. The corresponding Jeans mass is ∼105 M ⊙, and thus the formation of primordial stars or a star cluster is expected in the SIGO.
Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari

Across the Arcade Learning Environment, Rainbow achieves a level of performance competitive with humans and modern RL algorithms. However, attaining this level of performance requires large amounts of data and hardware resources, making research in this area computationally expensive and use in practical applications often infeasible. This paper's contribution is threefold: We (1) propose an improved version of Rainbow, seeking to drastically reduce Rainbow's data, training time, and compute requirements while maintaining its competitive performance; (2) we empirically demonstrate the effectiveness of our approach through experiments on the Arcade Learning Environment, and (3) we conduct a number of ablation studies to investigate the effect of the individual proposed modifications. Our improved version of Rainbow reaches a median human normalized score close to classic Rainbow's, while using 20 times less data and requiring only 7.5 hours of training time on a single GPU. We also provide our full implementation including pre-trained models.
M2A: Motion Aware Attention for Accurate Video Action Recognition

Advancements in attention mechanisms have led to significant performance improvements in a variety of areas in machine learning due to its ability to enable the dynamic modeling of temporal sequences. A particular area in computer vision that is likely to benefit greatly from the incorporation of attention mechanisms in video action recognition. However, much of the current research's focus on attention mechanisms have been on spatial and temporal attention, which are unable to take advantage of the inherent motion found in videos. Motivated by this, we develop a new attention mechanism called Motion Aware Attention (M2A) that explicitly incorporates motion characteris-tics. More specifically, M2A extracts motion information between consecutive frames and utilizes attention to focus on the motion patterns found across frames to accurately recognize actions in videos. The proposed M2A mechanism is simple to implement and can be easily incorporated into any neural network backbone architecture. We show that incorporating motion mechanisms with attention mechanisms using the proposed M2A mechanism can lead to a $+15\%$ to $+26\%$ improvement in top-1 accuracy across different backbone architectures, with only a small in-crease in computational complexity. We further compared the performance of M2A with other state-of-the-art motion and at-tention mechanisms on the Something-Something V1 video action recognition benchmark. Experimental results showed that M2A can lead to further improvements when combined with other temporal mechanisms and that it outperforms other motion-only or attention-only mechanisms by as much as $+60\%$ in top-1 accuracy for specific classes in the benchmark. We make our code available at: https://github.com/gebob19/M2A.
Toward Intrinsic Force Sensing and Control in Parallel Soft Robots

With soft robotics being increasingly employed in settings demanding high and controlled contact forces, recent research has demonstrated the use of soft robots to estimate or intrinsically sense forces without requiring external sensing mechanisms. While this has mainly been shown in tendon-based continuum manipulators or deformable robots comprising of push–pull rod actuation, fluid drives still pose great challenges due to high actuation variability and nonlinear mechanical system responses. In this work, we investigate the capabilities of a hydraulic, parallel soft robot to intrinsically sense and subsequently control contact forces. A comprehensive algorithm is derived for static, quasi-static, and dynamic force sensing, which relies on fluid volume and pressure information of the system. The algorithm is validated for a single degree-of-freedom soft fluidic actuator. Results indicate that axial forces acting on a single actuator can be estimated with mean error of 0.56 $\pm$ 0.66 N within the validated range of 0–6 N in a quasi-static configuration. The force sensing methodology is applied to force control in a single actuator as well as the coupled parallel robot. It can be seen that forces are controllable for both systems, with the capability of controlling directional contact forces in case of the multidegree-of-freedom parallel soft robot.
Data-driven verification and synthesis of stochastic systems through barrier certificates

In this work, we study veriﬁcation and synthesis problems for safety speciﬁcations over unknown discrete-time stochastic systems. When a model of the system is available, barrier certiﬁcates have been successfully applied for ensuring the satisfaction of safety speciﬁcations. In this work, we formulate the computation of barrier certiﬁcates as a robust convex program (RCP). Solving the acquired RCP is hard in general because the model of the system that appears in one of the constraints of the RCP is unknown. We propose a data-driven approach that replaces the uncountable number of constraints in the RCP with a ﬁnite number of constraints by taking ﬁnitely many random samples from the trajectories of the system. We thus replace the original RCP with a scenario convex program (SCP) and show how to relate their optimizers. We guarantee that the solution of the SCP is a solution of the RCP with a priori guaranteed conﬁdence when the number of samples is larger than a pre-computed value. This provides a lower bound on the safety probability of the original unknown system together with a controller in the case of synthesis. We also discuss an extension of our veriﬁcation approach to a case where the associated robust program is non-convex and show how a similar methodology can be applied. Finally, the applicability of our proposed approach is illustrated through three case studies.
Rethinking Clustering for Robustness

This paper studies how encouraging semantically-aligned features during deep neural network training can increase network robustness. Recent works observed that Adversarial Training leads to robust models, whose learnt features appear to correlate with human perception. Inspired by this connection from robustness to semantics, we study the complementary connection: from semantics to robustness. To do so, we provide a robustness certificate for distance-based classification models (clustering-based classifiers). Moreover, we show that this certificate is tight, and we leverage it to propose ClusTR (Clustering Training for Robustness), a clustering-based and adversary-free training framework to learn robust models. Interestingly, \textit{ClusTR} outperforms adversarially-trained networks by up to $4\%$ under strong PGD attacks.
Multimodal Emotion Recognition with High-Level Speech and Text Features

Automatic emotion recognition is one of the central concerns of the Human-Computer Interaction field as it can bridge the gap between humans and machines. Current works train deep learning models on low-level data representations to solve the emotion recognition task. Since emotion datasets often have a limited amount of data, these approaches may suffer from overfitting, and they may learn based on superficial cues. To address these issues, we propose a novel cross-representation speech model, inspired by disentangle-ment representation learning, to perform emotion recognition on wav2vec 2.0 speech features. We also train a CNN-based model to recognize emotions from text features extracted with Transformer-based models. We further combine the speech-based and text-based results with a score fusion approach. Our method is evaluated on the IEMOCAP dataset in a 4-class classification problem, and it surpasses current works on speech-only, text-only, and multimodal emotion recognition.
Antenna‐height‐dependent delay spread model under indoor stair environment for small cell deployment in future mobile communications

A novel measurement‐based antenna‐height‐dependent statistical root‐mean‐square (RMS) delay spread model under indoor stair environment is proposed. In this model, the RMS delay spread is described as a log‐normal random variable with antenna‐height‐dependent parameters in both line‐of‐sight (LOS) and the non‐LOS (NLOS) areas. The environmental independence of the RMS delay spread distribution is shown first. Then, the parameters of the proposed model are extracted using least‐squares and maximum likelihood estimation. The accuracy of the proposed model is verified by the extensive measured data, and the mean values of the RMS delay spread are compared with those in other related works. Finally, the interdependence of RMS delay spread and the coherence bandwidth is investigated. The proposed model can be applied to the physical layer designs of small cells in future 5G and ubiquitous mobile communication systems. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Estimates for the variable order Riesz potential with applications

We study weak-type estimates and exponential integrability for the variable order Riesz potential. As an application we prove an exponential integrability result with respect to the Hausdorff content for functions from variable exponent Sobolev spaces. In particular, the earlier exponential integrability results are improved to a corresponding one with respect to the Choque integral whenever John domains are considered. Moreover, new exponential integrability results also for domains with outward cusps are obtained.
Terahertz light–driven coupling of antiferromagnetic spins to lattice

Description Coupling up of spins and lattice The development of spintronics and magnetic data storage relies on understanding and controlling the dynamics of magnetic excitations within a material. Of crucial importance for practical applications is how fast the magnetization can be switched. Mashkovich et al. report the use of ultrafast terahertz radiation to create magnon excitations in the antiferromagnet cobalt difluoride that can then be coupled with phonon excitations (see the Perspective by Juraschek and Narang). Using light to control coupling between the spins and the lattice provides a route to manipulate magnetization in antiferromagnetic materials on ultrafast time scales. —ISO Terahertz light pulses were used to coherently couple antiferromagnetic spins to the lattice. Understanding spin-lattice coupling represents a key challenge in modern condensed matter physics, with crucial importance and implications for ultrafast and two-dimensional magnetism. The efficiency of angular momentum and energy transfer between spins and the lattice imposes fundamental speed limits on the ability to control spins in spintronics, magnonics, and magnetic data storage. We report on an efficient nonlinear mechanism of spin-lattice coupling driven by terahertz light pulses. A nearly single-cycle terahertz pulse resonantly interacts with a coherent magnonic state in the antiferromagnet cobalt difluoride (CoF2) and excites the Raman-active terahertz phonon. The results reveal the distinctive functionality of antiferromagnets that allows ultrafast spin-lattice coupling using light.
Physics-enhanced Neural Networks in the Small Data Regime

Identifying the dynamics of physical systems requires a machine learning model that can assimilate observational data, but also incorporate the laws of physics. Neural Networks based on physical principles such as the Hamiltonian or Lagrangian NNs have recently shown promising results in generating extrapolative predictions and accurately representing the system's dynamics. We show that by additionally considering the actual energy level as a regularization term during training and thus using physical information as inductive bias, the results can be further improved. Especially in the case where only small amounts of data are available, these improvements can significantly enhance the predictive capability. We apply the proposed regularization term to a Hamiltonian Neural Network (HNN) and Constrained Hamiltonian Neural Network (CHHN) for a single and double pendulum, generate predictions under unseen initial conditions and report significant gains in predictive accuracy.
The Hurst roughness exponent and its model-free estimation

We say that a continuous real-valued function x admits the Hurst roughness exponent H if the pth variation of x converges to zero if p > 1/H and to infinity if p < 1/H. For the sample paths of many stochastic processes, such as fractional Brownian motion, the Hurst roughness exponent exists and equals the standard Hurst parameter. In our main result, we provide a mild condition on the Faber–Schauder coefficients of x under which the Hurst roughness exponent exists and is given as the limit of the classical Gladyshev estimates Ĥn(x). This result can be viewed as a strong consistency result for the Gladyshev estimators in an entirely model-free setting, because no assumption whatsoever is made on the possible dynamics of the function x. Nonetheless, our proof is probabilistic and relies on a martingale that is hidden in the Faber–Schauder expansion of x. Since the Gladyshev estimators are not scale-invariant, we construct several scale-invariant estimators that are derived from the sequence (Ĥn)n∈N. We also discuss how a dynamic change in the Hurst roughness parameter of a time series can be detected. Finally, we extend our results to the case in which the pth variation of x is defined over a sequence of unequally spaced partitions. Our results are illustrated by means of high-frequency financial time series.
Probabilistic Regression with Huber Distributions

In this paper we describe a probabilistic method for estimating the position of an object along with its covariance matrix using neural networks. Our method is designed to be robust to outliers, have bounded gradients with respect to the network outputs, among other desirable properties. To achieve this we introduce a novel probability distribution inspired by the Huber loss. We also introduce a new way to parameterize positive definite matrices to ensure invariance to the choice of orientation for the coordinate system we regress over. We evaluate our method on popular body pose and facial landmark datasets and get performance on par or exceeding the performance of non-heatmap methods. Our code is available at github.com/Davmo049/Public_prob_regression_with_huber_distributions