Miracle-dz/UniFAR-FTUs huggingface dataset:
Complex Recurrent Variational Autoencoder for Speech Enhancement

Commonly-used methods in speech enhancement are based on short-time fourier transform (STFT) representation, in particular on the magnitude of the STFT. This is because phase is naturally unstructured and intractable, and magnitude has shown more importance in speech enhancement. Nevertheless, phase has shown its signiﬁcance in some research and cannot be ignored. Complex neural networks, with their inherent advantage, provide a solution for complex spectrogram processing. Complex variational autoencoder (VAE), as an extension of vanilla VAE, has shown positive results in complex spectrogram representation. However, the existing work on complex VAE only uses linear layers and merely applies the model on direct spectra representation. This paper extends the linear complex VAE to a non-linear one. Furthermore, on account of the temporal property of speech signals, a complex recurrent VAE is proposed. The proposed model has been applied on speech enhancement. As far as we know, it is the ﬁrst time that a complex generative model is applied to speech enhancement. Experiments are based on the TIMIT dataset, while speech intelligibility and speech quality have been evaluated. The results show that, for speech enhancement, the proposed method has better performance on speech intelligibility and comparable performance on speech quality.
High-Quality Pluralistic Image Completion via Code Shared VQGAN

the richness of the representation further facilitate the subsequent deploy- ment of a transformer to effectively learn how to composite and complete a masked image at the discrete code domain. Based on the global context well-captured by the transformer and the available visual regions, we are able to sample all tokens simultaneously, which is completely different from the prevailing autoregressive approach of iGPT-based works, and leads to more than 100 × faster inference speed. Experiments show that our framework is able to learn semantically-rich discrete codes efficiently and robustly, resulting in much better image reconstruction quality. Our diverse image completion framework significantly outperforms the state-of-the-art both quantitatively and qualitatively on multiple benchmark datasets. Computing
How Should Voice Assistants Deal With Users' Emotions?

There is a growing body of research in HCI on detecting the users' emotions. Once it is possible to detect users' emotions reliably, the next question is how an emotion-aware interface should react to the detected emotion. In a first step, we tried to find out how humans deal with the negative emotions of an avatar. The hope behind this approach was to identify human strategies, which we can then mimic in an emotion-aware voice assistant. We present a user study in which participants were confronted with an angry, sad, or frightened avatar. Their task was to make the avatar happy by talking to it. We recorded the voice signal and analyzed it. The results show that users predominantly reacted with neutral emotion. However, we also found gender differences, which opens a range of questions.
Methods for combining probability and nonprobability samples under unknown overlaps

Nonprobability (convenience) samples are increasingly sought to reduce the estimation variance for one or more population variables of interest that are estimated using a randomized survey (reference) sample by increasing the effective sample size. Estimation of a population quantity derived from a convenience sample will typically result in bias since the distribution of variables of interest in the convenience sample is different from the population distribution. A recent set of approaches estimates inclusion probabilities for convenience sample units by specifying reference sample-weighted pseudo likelihoods. This paper introduces a novel approach that derives the propensity score for the observed sample as a function of inclusion probabilities for the reference and convenience samples as our main result. Our approach allows specification of a likelihood directly for the observed sample as opposed to the approximate or pseudo likelihood. We construct a Bayesian hierarchical formulation that simultaneously estimates sample propensity scores and the convenience sample inclusion probabilities. We use a Monte Carlo simulation study to compare our likelihood based results with the pseudo likelihood based approaches considered in the literature.
Free Energy Principle for the Noise Smoothness Estimation of Linear Systems with Colored Noise

The free energy principle (FEP) from neuroscience provides a framework called active inference for the joint estimation and control of state space systems, subjected to colored noise. However, the active inference community has been challenged with the critical task of manually tuning the noise smoothness parameter. To solve this problem, we introduce a novel online noise smoothness estimator based on the idea of free energy principle. We mathematically show that our estimator can converge to the free energy optimum during smoothness estimation. Using this formulation, we introduce a joint state and noise smoothness observer design called DEMs. Through rigorous simulations, we show that DEMs outperforms state-of-the-art state observers with least state estimation error. Finally, we provide a proof of concept for DEMs by applying it on a real life robotics problem - state estimation of a quadrotor hovering in wind, demonstrating its practical use.
Performance Portable Solid Mechanics via Matrix-Free p-Multigrid

—Finite element analysis of solid mechanics is a foundational tool of modern engineering, with low-order ﬁnite element methods and assembled sparse matrices representing the industry standard for implicit analysis. We use performance models and numerical experiments to demonstrate that high- order methods greatly reduce the costs to reach engineering tolerances while enabling effective use of GPUs. We demonstrate the reliability, efﬁciency, and scalability of matrix-free p -multigrid methods with algebraic multigrid coarse solvers through large deformation hyperelastic simulations of multiscale structures. We investigate accuracy, cost, and execution time on multi-node CPU and GPU systems for moderate to large models using AMD MI250X (OLCF Crusher), NVIDIA A100 (NERSC Perlmutter), and V100 (LLNL Lassen and OLCF Summit), resulting in order of magnitude efﬁciency improvements over a broad range of model properties and scales. We discuss efﬁcient matrix-free representation of Jacobians and demonstrate how automatic differentiation enables rapid development of nonlinear material models without impacting debuggability and workﬂows targeting GPUs.
Coarse-to-Fine Sparse Sequential Recommendation

Sequential recommendation aims to model dynamic user behavior from historical interactions. Self-attentive methods have proven effective at capturing short-term dynamics and long-term preferences. Despite their success, these approaches still struggle to model sparse data, on which they struggle to learn high-quality item representations. We propose to model user dynamics from shopping intents and interacted items simultaneously. The learned intents are coarse-grained and work as prior knowledge for item recommendation. To this end, we present a coarse-to-fine self-attention framework, namely CaFe, which explicitly learns coarse-grained and fine-grained sequential dynamics. Specifically, CaFe first learns intents from coarse-grained sequences which are dense and hence provide high-quality user intent representations. Then, CaFe fuses intent representations into item encoder outputs to obtain improved item representations. Finally, we infer recommended items based on representations of items and corresponding intents. Experiments on sparse datasets show that CaFe outperforms state-of-the-art self-attentive recommenders by 44.03% NDCG@5 on average.
Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation

The paradigm of worst-group loss minimization has shown its promise in avoiding to learn spurious correlations, but requires costly additional supervision on spurious attributes. To resolve this, recent works focus on developing weaker forms of supervision -- e.g., hyperparameters discovered with a small number of validation samples with spurious attribute annotation -- but none of the methods retain comparable performance to methods using full supervision on the spurious attribute. In this paper, instead of searching for weaker supervisions, we ask: Given access to a fixed number of samples with spurious attribute annotations, what is the best achievable worst-group loss if we"fully exploit"them? To this end, we propose a pseudo-attribute-based algorithm, coined Spread Spurious Attribute (SSA), for improving the worst-group accuracy. In particular, we leverage samples both with and without spurious attribute annotations to train a model to predict the spurious attribute, then use the pseudo-attribute predicted by the trained model as supervision on the spurious attribute to train a new robust model having minimal worst-group loss. Our experiments on various benchmark datasets show that our algorithm consistently outperforms the baseline methods using the same number of validation samples with spurious attribute annotations. We also demonstrate that the proposed SSA can achieve comparable performances to methods using full (100%) spurious attribute supervision, by using a much smaller number of annotated samples -- from 0.6% and up to 1.5%, depending on the dataset.
GWA: A Large High-Quality Acoustic Dataset for Audio Processing

We present the Geometric-Wave Acoustic (GWA) dataset, a large-scale audio dataset of about 2 million synthetic room impulse responses (IRs) and their corresponding detailed geometric and simulation configurations. Our dataset samples acoustic environments from over 6.8K high-quality diverse and professionally designed houses represented as semantically labeled 3D meshes. We also present a novel real-world acoustic materials assignment scheme based on semantic matching that uses a sentence transformer model. We compute high-quality impulse responses corresponding to accurate low-frequency and high-frequency wave effects by automatically calibrating geometric acoustic ray-tracing with a finite-difference time-domain wave solver. We demonstrate the higher accuracy of our IRs by comparing with recorded IRs from complex real-world environments. Moreover, we highlight the benefits of GWA on audio deep learning tasks such as automated speech recognition, speech enhancement, and speech separation. This dataset is the first data with accurate wave acoustic simulations in complex scenes. Codes and data are available at https://gamma.umd. edu/pro/sound/gwa.
Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling

—Evaluating the performance of autonomous vehicles (AV) and their complex subsystems to high precision under naturalistic cir-cumstances remains a challenge, especially when the failure or dangerous cases are rare. Rarity does not only require an enormous sample size for a naive method to achieve high conﬁdence estimation, but it also causes dangerous underestimation of the true failure rate and it is extremely hard to detect. Meanwhile, the state-of-the-art approach that comes with a correctness guarantee can only compute an upper bound for the failure rate under certain conditions, which could limit its practical uses. In this work, we present Deep Importance Sampling (Deep IS) framework that utilizes a deep neural network to obtain an efﬁcient IS that is on-par with the state-of-the-art, capable of reducing the required sample size 43 times smaller than the naive sampling method to achieve 10% relative error and while producing an estimate that is much less conservative. Our high-dimensional experiment estimating the misclassiﬁcation rate of one of the state-of-the-art trafﬁc sign classiﬁers further reveals that this efﬁciency still holds true even when the target is very small, achieving over 600 times efﬁciency boost. This highlights the potential of Deep IS in providing a precise estimate even against high-dimensional uncertainties.
Survey of an office and laboratory university building - An unhealthy building case study

SUMMARY Sick Building Syndrome (SBS) was identified and defined some 30 years ago and has been investigated since then, yet recently designed and constructed buildings seem to disregard both the phenomenon and the significant body of work that documents it. A Post Occupancy Evaluation (POE) study was undertaken in one such building in Israel, combining offices and laboratories. A review of plans and building shows basic flaws in decision-making and design, and lack of POE. The results of measurements, surveys and interviews presented here are the outcome of an educational exercise undertaken with a group of graduate students.
Boiling Quantum Vacuum: Thermal Subsystems from Ground-State Entanglement

In certain special circumstances, such as in the vicinity of a black hole or in a uniformly accelerating frame, vacuum fluctuations appear to give rise to a finite-temperature environment. This effect, currently without experimental confirmation, can be interpreted as a manifestation of quantum entanglement after tracing out vacuum modes in an unobserved region. In this work, we identify a class of experimentally accessible quantum systems where thermal density matrices emerge from vacuum entanglement. We show that reduced density matrices of lower-dimensional subsystems embedded in $D$-dimensional gapped Dirac fermion vacuum, either on a lattice or continuum, have a thermal form with respect to a lower-dimensional Dirac Hamiltonian. Strikingly, we show that vacuum entanglement can even conspire to make a subsystem of a gapped system at zero temperature appear as a hot gapless system. We propose concrete experiments in cold atom quantum simulators to observe the vacuum entanglement induced thermal states.
Projection onto quadratic hypersurfaces

We address the problem of projecting a point onto a quadratic hypersurface, more speciﬁcally a central quadric. We show how this problem reduces to ﬁnding a given root of a scalar-valued nonlinear function. We completely characterize one of the optimal solutions of the projection as either the unique root of this nonlinear function on a given interval, or as a point that belongs to a ﬁnite set of computable solutions. We then leverage this projection and the recent advancements in splitting methods to compute the projection onto the intersection of a box and a quadratic hypersurface with alternating projections and Douglas-Rachford splitting methods. We test these methods on a practical problem from the power systems literature, and show that they outperform IPOPT and Gurobi in terms of objective, execution time and feasibility of the solution.
Bayesian quantile regression for longitudinal count data

ABSTRACT This work introduces Bayesian quantile regression modelling framework for the analysis of longitudinal count data. In this model, the response variable is not continuous and hence an artificial smoothing of counts is incorporated. The Bayesian implementation utilizes the normal–exponential mixture representation of the asymmetric Laplace distribution for the response variable. An efficient Gibbs sampling algorithm is derived for fitting the model to the data. The model is illustrated through simulation studies and implemented in an application drawn from neurology. Model comparison demonstrates the practical utility of the proposed model.
Flux and spectral variability of Mrk 421 during its moderate activity state using NuSTAR: Possible accretion disc contribution?

The X-ray emission in BL Lac objects is believed to be dominated by synchrotron emission from their relativistic jets. However, when the jet emission is not strong, one could expect signatures of X-ray emission from inverse Compton scattering of accretion disc photons by the corona. Moreover, the observed X-ray variability can also originate in the disc, and gets propagated and amplified by the jet. Here, we present results on the BL Lac object Mrk 421 using the NuSTAR data acquired during 2017 when the source was in a moderate X-ray brightness state. For comparison with high jet activity, we also considered one epoch data in April 2013 during a very high X-ray brightness state. Our aim is to explore the possibility of the signature of accretion disc emission in the overall X-ray emission from Mrk 421. The spectral fitting of the data using the two component advective flow model shows gives (a) the size of the dynamic corona at the base of the jet from ~28 to 10 r$_s$, (b) the disc mass accretion rate from 0.021 to 0.051 $\dot M_{\rm Edd}$, (c) the halo mass accretion rate from 0.22 to 0.35 $\dot M_{\rm Edd}$, and (d) the viscosity parameter of the Keplerian accretion disc from 0.18 $-$ 0.25. In the assumed model, the total flux, disc and jet flux correlate with the radio flux observed during these epochs. We conclude that the spectra of all the epochs of Mrk 421 in 2017 are well described by the accretion disc based two component advective flow model. The estimated disc and jet flux relations with radio flux show that accretion disc can contribute to the observed X-ray emission, when X-ray data (that covers a small portion of the broad band spectral energy distribution of Mrk 421) is considered in isolation. However, the present disc based models are disfavoured with respect to the relativistic jet models when considering the X-ray data in conjunction with data at other wavelengths.
Learning to Accelerate by the Methods of Step-size Planning

Gradient descent is slow to converge for ill-conditioned problems and non-convex problems. An important technique for acceleration is step-size adaptation. The first part of this paper contains a detailed review of step-size adaptation methods, including Polyak step-size, L4, LossGrad, Adam, IDBD, and Hypergradient descent, and the relation of step-size adaptation to meta-gradient methods. In the second part of this paper, we propose a new class of methods of accelerating gradient descent that have some distinctiveness from existing techniques. The new methods, which we call {\em step-size planning}, use the {\em update experience} to learn an improved way of updating the parameters. The methods organize the experience into $K$ steps away from each other to facilitate planning. From the past experience, our planning algorithm, Csawg, learns a step-size model which is a form of multi-step machine that predicts future updates. We extends Csawg to applying step-size planning multiple steps, which leads to further speedup. We discuss and highlight the projection power of the diagonal-matrix step-size for future large scale applications. We show for a convex problem, our methods can surpass the convergence rate of Nesterov's accelerated gradient, $1 - \sqrt{\mu/L}$, where $\mu, L$ are the strongly convex factor of the loss function $F$ and the Lipschitz constant of $F'$, which is the theoretical limit for the convergence rate of first-order methods. On the well-known non-convex Rosenbrock function, our planning methods achieve zero error below 500 gradient evaluations, while gradient descent takes about 10000 gradient evaluations to reach a $10^{-3}$ accuracy. We discuss the connection of step-size planing to planning in reinforcement learning, in particular, Dyna architectures. (This is a shorter abstract than in the paper because of length requirement)
Bimodal Distributed Binarized Neural Networks

Binary neural networks (BNNs) are an extremely promising method for reducing deep neural networks’ complexity and power consumption significantly. Binarization techniques, however, suffer from ineligible performance degradation compared to their full-precision counterparts. Prior work mainly focused on strategies for sign function approximation during the forward and backward phases to reduce the quantization error during the binarization process. In this work, we propose a bimodal-distributed binarization method (BD-BNN). The newly proposed technique aims to impose a bimodal distribution of the network weights by kurtosis regularization. The proposed method consists of a teacher–trainer training scheme termed weight distribution mimicking (WDM), which efficiently imitates the full-precision network weight distribution to their binary counterpart. Preserving this distribution during binarization-aware training creates robust and informative binary feature maps and thus it can significantly reduce the generalization error of the BNN. Extensive evaluations on CIFAR-10 and ImageNet demonstrate that our newly proposed BD-BNN outperforms current state-of-the-art schemes.
SALISA: Saliency-based Input Sampling for Efficient Video Object Detection

High-resolution images are widely adopted for high-performance object detection in videos. However, processing high-resolution inputs comes with high computation costs, and naive down-sampling of the input to reduce the computation costs quickly degrades the detection performance. In this paper, we propose SALISA, a novel non-uniform SALiency-based Input SAmpling technique for video object detection that allows for heavy down-sampling of unimportant background regions while preserving the fine-grained details of a high-resolution image. The resulting image is spatially smaller, leading to reduced computational costs while enabling a performance comparable to a high-resolution input. To achieve this, we propose a differentiable resampling module based on a thin plate spline spatial transformer network (TPS-STN). This module is regularized by a novel loss to provide an explicit supervision signal to learn to"magnify"salient regions. We report state-of-the-art results in the low compute regime on the ImageNet-VID and UA-DETRAC video object detection datasets. We demonstrate that on both datasets, the mAP of an EfficientDet-D1 (EfficientDet-D2) gets on par with EfficientDet-D2 (EfficientDet-D3) at a much lower computational cost. We also show that SALISA significantly improves the detection of small objects. In particular, SALISA with an EfficientDet-D1 detector improves the detection of small objects by $77\%$, and remarkably also outperforms EfficientDetD3 baseline.
Theoretical analysis of the extended cyclic reduction algorithm

A BSTRACT . The extended cyclic reduction algorithm developed by Swarztrauber in 1974 was used to solve the block-tridiagonal linear system. The paper ﬁlls in the gap of theoretical results concern-ing the zeros of matrix polynomial B ( r ) i with respect to a tridiagonal matrix which are computed by Newton’s method in the extended cyclic reduction algorithm. Meanwhile, the forward error analysis of the extended cyclic reduction algorithm for solving the block-tridiagonal system is studied. To achieve the two aims, the critical point is to ﬁnd out that the zeros of matrix polynomial B ( r ) i are eigenvalues of a principal submatrix of the coefﬁcient matrix.
Recoil experiments determine the eigenmodes of viscoelastic fluids

We experimentally investigate the transient recoil dynamics of a colloidal probe particle in a viscoelastic fluid after the driving force acting on the probe is suddenly removed. The corresponding recoil displays two distinct timescales which are in excellent agreement with a microscopic model which considers the probe particle to be coupled to two bath particles via harmonic springs. Notably, this model exhibits two sets of eigenmodes which correspond to reciprocal and non-reciprocal force conditions and which can be experimentally confirmed in our experiments. We expect our findings to be relevant under conditions where particles are exposed to non-steady shear forces as this is encountered e.g. in microfluidic sorting devices or the intermittent motion of motile bacteria within their natural viscoelastic surrounding.