Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments

In this paper, a neural network-augmented algorithm for noise-robust online dereverberation with a Kalman ﬁltering variant of the weighted prediction error (WPE) method is proposed. The ﬁlter stochastic variations are predicted by a deep neural network (DNN) trained end-to-end using the ﬁlter residual error and signal characteristics. The presented framework allows for robust dereverberation on a single-channel noisy reverberant dataset similar to WHAMR! The Kalman ﬁltering WPE introduces distortions in the en-hanced signal when predicting the ﬁlter variations from the residual error only, if the target speech power spectral density is not perfectly known and the observation is noisy. The proposed approach avoids these distortions by correcting the ﬁlter variations estimation in a data-driven way, increasing the robustness of the method to noisy scenarios. Furthermore, it yields a strong dereverberation and denoising performance compared to a DNN-supported recursive least squares variant of WPE, especially for highly noisy inputs.
Dimensionality Expansion and Transfer Learning for Next Generation Energy Management Systems

—Electrical management systems (EMS) are playing a central role in enabling energy savings. They can be deployed within an everyday household where they monitor and manage appliances and help residents be more energy efﬁcient and subsequently also more economical. One of they key functionalities of EMS is to automatically detect and identify appliances within a household through the process of load monitoring. In this paper, we propose a new transfer learning approach for building EMS (BEMS) and study the trade-offs in terms of numbers of samples and target classes in adapting a backbone model during the transfer process. We also perform a ﬁrst time analysis of feature expansion through video-like transformation of time series data for device classiﬁcation in non intrusive load monitoring (NILM) and propose a deep learning architecture enabling accurate appliance identiﬁcation. We examine the relative performance of our method on 5 different representative low-frequency datasets and show that our method performs with an average F1 score of 0.88 on these datasets.
The effects of customer relationship management dimensions on organizational performance (Case study: shipping organization of Iran)

N owadays organizations, for the lack of comprehensive knowledge about the effects of c ustomer's relationship management on the performance, have been failed to expand consistent strategies and the raise of their assets through these relationships. It is obvious about trade expansion in the country and entering into the world market, the relationship management is among the vital needs. C onsidering the importance of the issue, the present study investigated the effects of customer relationship management on the organizational performance of shipping organization. This was an applicable research in terms of its purpose, and a correlational-survey researc h in terms of its methodology. The required data were gathered through a questionnaire designed by the re searcher. It contained 40 questions, the v alidity of whic h was confirmed with the consideration of experts and superv isor of the study. The reliability, based on C ronbach index, was equal to 0.81 . The questionnaire c ontained questions of personal profile, major and technic al questions for study ing the stated hy pothesis. The collected data were analyzed by SPSS software and at two levels of qualitative (frequency, average, standard dev iation) and at inferential lev el (regre ssion and Freeman nonparametric test). A cc ording to the obtained results from regression test, all of the hypotheses of the study were confirmed exc ept the fifteenth hypothesis (the c ustomer relationship management base technology has significant effects on the internal processes performanc e). The results of the studyshowed that except the mentioned hy pothesis, the other dimension of c ustomer relationship management (the organization of customer relationship management, knowledge management, customer orientation, c ustomer relationship management) had significant effec ts on the organizational performances (financial, c ustomer, internal proc esses, training and growth). The re sults of Freeman test showed that the studied v ariables had significant lev els of under 0.05. So the ranking of their averages were not equal and the variable of training and expansion had the highest av erage level. The v ariable of customer relationship management base on technology had the lowest average in comparison with other v ariables.
An Algebraic Approach to Learning and Grounding

We consider the problem of learning the semantics of composite algebraic expressions from examples. The outcome is a versatile framework for studying learning tasks that can be put into the following abstract form: The input is a partial algebra $\alg$ and a finite set of examples $(\varphi_1, O_1), (\varphi_2, O_2), \ldots$, each consisting of an algebraic term $\varphi_i$ and a set of objects~$O_i$. The objective is to simultaneously fill in the missing algebraic operations in $\alg$ and ground the variables of every $\varphi_i$ in $O_i$, so that the combined value of the terms is optimised. We demonstrate the applicability of this framework through case studies in grammatical inference, picture-language learning, and the grounding of logic scene descriptions.
Consensual Aggregation on Random Projected High-dimensional Features for Regression

In this paper, we present a study of a kernel-based consensual aggregation on randomly projected high-dimensional features of predictions for regression. The aggregation scheme is composed of two steps: the high-dimensional features of predictions, given by a large number of regression estimators, are randomly projected into a smaller subspace using Johnson-Lindenstrauss Lemma in the first step, and a kernel-based consensual aggregation is implemented on the projected features in the second step. We theoretically show that the performance of the aggregation scheme is close to the performance of the aggregation implemented on the original high-dimensional features, with high probability. Moreover, we numerically illustrate that the aggregation scheme upholds its performance on very large and highly correlated features of predictions given by different types of machines. The aggregation scheme allows us to flexibly merge a large number of redundant machines, plainly constructed without model selection or cross-validation. The efficiency of the proposed method is illustrated through several experiments evaluated on different types of synthetic and real datasets.
User-Level Differential Privacy against Attribute Inference Attack of Speech Emotion Recognition in Federated Learning

Many existing privacy-enhanced speech emotion recognition (SER) frameworks focus on perturbing the original speech data through adversarial training within a centralized machine learning setup. However, this privacy protection scheme can fail since the adversary can still access the perturbed data. In recent years, distributed learning algorithms, especially federated learning (FL), have gained popularity to protect privacy in machine learning applications. While FL provides good intuition to safeguard privacy by keeping the data on local devices, prior work has shown that privacy attacks, such as attribute inference attacks, are achievable for SER systems trained using FL. In this work, we propose to evaluate the user-level differential privacy (UDP) in mitigating the privacy leaks of the SER system in FL. UDP provides theoretical privacy guarantees with privacy parameters $\epsilon$ and $\delta$. Our results show that the UDP can effectively decrease attribute information leakage while keeping the utility of the SER system with the adversary accessing one model update. However, the efficacy of the UDP suffers when the FL system leaks more model updates to the adversary. We make the code publicly available to reproduce the results in https://github.com/usc-sail/fed-ser-leakage.
3D face reconstruction with dense landmarks

Landmarks often play a key role in face analysis, but many aspects of identity or expression cannot be represented by sparse landmarks alone. Thus, in order to reconstruct faces more accurately, landmarks are often combined with additional signals like depth images or techniques like differentiable rendering. Can we keep things simple by just using more landmarks? In answer, we present the first method that accurately predicts 10x as many landmarks as usual, covering the whole head, including the eyes and teeth. This is accomplished using synthetic training data, which guarantees perfect landmark annotations. By fitting a morphable model to these dense landmarks, we achieve state-of-the-art results for monocular 3D face reconstruction in the wild. We show that dense landmarks are an ideal signal for integrating face shape information across frames by demonstrating accurate and expressive facial performance capture in both monocular and multi-view scenarios. This approach is also highly efficient: we can predict dense landmarks and fit our 3D face model at over 150FPS on a single CPU thread. Please see our website: https://microsoft.github. io/DenseLandmarks/.
The Virasoro structure and the scattering matrix for Liouville conformal field theory

In this work, we construct a representation of the Virasoro algebra in the canonical Hilbert space associated to Liouville conformal ﬁeld theory. The study of the Virasoro operators is performed through the introduction of a new family of Markovian dynamics associated to holomorphic vector ﬁelds deﬁned in the disk. As an output, we show that the Hamiltonian of Liouville conformal ﬁeld theory can be diagonalized through the action of the Virasoro algebra. This enables to show that the scattering matrix of the theory is diagonal and that the family of the so-called primary ﬁelds (which are eigenvectors of the Hamiltonian) admits an analytic extension to the whole complex plane, as conjectured in the physics literature.
A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition

Sign language recognition from sequences of monocular images or 2D poses is a challenging ﬁeld, not only due to the difﬁculty to infer 3D information from 2D data, but also due to the temporal relationship between the sequences of information. Additionally, the wide variety of signs and the constant need to add new ones on production environments makes it infeasible to use traditional classiﬁcation techniques. We propose a novel Contrastive Transformer-based model, which demonstrate to learn rich representations from body key points sequences, allowing better comparison between vector embedding. This allows us to apply these techniques to perform one-shot or few-shot tasks, such as classiﬁcation and translation. The experiments showed that the model could generalize well and achieved competitive results for sign classes never seen in the training process.
Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension

Procedural Multimodal Documents (PMDs) organize textual instructions and corresponding images step by step. Comprehending PMDs and inducing their representations for the downstream reasoning tasks is designated as Procedural MultiModal Machine Comprehension (M3C). In this study, we approach Procedural M3C at a fine-grained level (compared with existing explorations at a document or sentence level), that is, entity. With delicate consideration, we model entity both in its temporal and cross-modal relation and propose a novel Temporal-Modal Entity Graph (TMEG). Specifically, graph structure is formulated to capture textual and visual entities and trace their temporal-modal evolution. In addition, a graph aggregation module is introduced to conduct graph encoding and reasoning. Comprehensive experiments across three Procedural M3C tasks are conducted on a traditional dataset RecipeQA and our new dataset CraftQA, which can better evaluate the generalization of TMEG.
Spectral Denoising for Microphone Classification

In this paper, we propose the use of denoising for microphone classification, to enable its usage for several key application domains that involve noisy conditions. We describe the proposed analysis pipeline and the baseline algorithm for microphone classification, and discuss various denoising approaches which can be applied to it within the time or spectral domain; finally, we determine the best-performing denoising procedure, and evaluate the performance of the overall, integrated approach with several SNR levels of additive input noise. As a result, the proposed method achieves an average accuracy increase of about 25% on denoised content over the reference baseline.
Extraction of deterministic components for high frequency stochastic process - an application from CSI 300 index

: This paper models stochastic process of price time series of CSI 300 index in Chinese ﬁnancial market, analyzes volatility characteristics of intraday high-frequency price data. In the new generalized Barndorﬀ-Nielsen and Shephard model, the lag caused by asynchrony of market information is considered, and the problem of lack of long-term dependence is solved. To speed up the valuation process, several machine learning and deep learning algorithms are used to estimate parameter and evaluate forecast results. Tracking historical jumps of diﬀerent magnitudes oﬀers promising avenues for simulating dynamic price processes and predicting future jumps. Numerical results show that the deterministic component of stochastic volatility processes would always be captured over short and longer-term windows. Research ﬁnding could be suitable for inﬂuence investors and regulators interested in predicting market dynamics based on realized volatility.
The Extended and Asymmetric Extended Krylov Subspace in Moment-Matching-Based Order Reduction of Large Circuit Models

The rapid growth of circuit complexity has rendered Model Order Reduction (MOR) a key enabler for the efficient simulation of large circuit models. MOR techniques based on moment-matching are well established due to their simplicity and computational performance in the reduction process. However, moment-matching methods based on the ordinary Krylov subspace are usually inadequate to accurately approximate the original circuit behavior, and at the same time do not produce reduced-order models as compact as needed. In this paper, we present a moment-matching method which utilizes the extended and the asymmetric extended Krylov subspace (EKS and AEKS), while it allows the parallel computation of the transfer function in order to deal with circuits that have many terminals. The proposed method can handle large-scale regular and singular circuits and generate accurate and efficient reduced-order models for circuit simulation. Experimental results on industrial IBM power grids demonstrate that the EKS method can achieve an error reduction up to 85.28% over a standard Krylov subspace method, while the AEKS method greatly reduces the runtime of EKS, introducing a negligible overhead in the reduction error.
Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise

We introduce a general framework for nonlinear stochastic gradient descent (SGD) for the scenarios when gradient noise exhibits heavy tails. The proposed framework subsumes several popular nonlinearity choices, like clipped, normalized, signed or quantized gradient, but we also consider novel nonlinearity choices. We establish for the considered class of methods strong convergence guarantees assuming a strongly convex cost function with Lipschitz continuous gradients under very general assumptions on the gradient noise. Most notably, we show that, for a nonlinearity with bounded outputs and for the gradient noise that may not have finite moments of order greater than one, the nonlinear SGD's mean squared error (MSE), or equivalently, the expected cost function's optimality gap, converges to zero at rate~$O(1/t^\zeta)$, $\zeta \in (0,1)$. In contrast, for the same noise setting, the linear SGD generates a sequence with unbounded variances. Furthermore, for the nonlinearities that can be decoupled component wise, like, e.g., sign gradient or component-wise clipping, we show that the nonlinear SGD asymptotically (locally) achieves a $O(1/t)$ rate in the weak convergence sense and explicitly quantify the corresponding asymptotic variance. Experiments show that, while our framework is more general than existing studies of SGD under heavy-tail noise, several easy-to-implement nonlinearities from our framework are competitive with state of the art alternatives on real data sets with heavy tail noises.
Accelerating Backward Aggregation in GCN Training With Execution Path Preparing on GPUs

The emerging Graph Convolutional Network (GCN) has been widely used in many domains, where it is important to improve the efficiencies of applications by accelerating GCN trainings. Due to the sparsity nature and exploding scales of input real-world graphs, state-of-the-art GCN training systems (e.g., GNNAdvisor) employ graph processing techniques to accelerate the message exchanging (i.e., aggregations) among the graph vertices. Nevertheless, these systems treat both the aggregation stages of forward and backward propagation phases as all-active graph processing procedures that indiscriminately conduct computations on all vertices of an input graph. In this article, we first point out that in a GCN training problem with a given training set on an input graph, its aggregation stages of backward propagation phases (called as backward aggregations in this article) can be equivalently converted to partially-active graph processing procedures, which conduct computations on only partial vertices of the input graph. By leveraging such a finding, we propose an execution path preparing method that collects and coalesces the graph data used during different training layers of backward aggregations, and constructs their corresponding sub-graphs (called as execution paths in this article) as inputs to conduct the backward training on GPUs. Further, we propose a structural-aware strategy for the execution paths to compute their optimal group sizes, so as to gain as high as possible performances on GPUs during the backward aggregations. The experiment results by conducting GCN training in typical real-world graphs show that compared with GNNAdvisor, our approach improves the performance of backward aggregations by up to 5.68x on NVIDIA P100 GPU, and up to 6.57x on NVIDIA V100S GPU
A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning

The diffusion of rumors on social media generally follows a propagation tree structure, which provides valuable clues on how an original message is transmitted and responded by users over time. Recent studies reveal that rumor verification and stance detection are two relevant tasks that can jointly enhance each other despite their differences. For example, rumors can be debunked by cross-checking the stances conveyed by their relevant posts, and stances are also conditioned on the nature of the rumor. However, stance detection typically requires a large training set of labeled stances at post level, which are rare and costly to annotate. Enlightened by Multiple Instance Learning (MIL) scheme, we propose a novel weakly supervised joint learning framework for rumor verification and stance detection which only requires bag-level class labels concerning the rumor's veracity. Specifically, based on the propagation trees of source posts, we convert the two multi-class problems into multiple MIL-based binary classification problems where each binary model is focused on differentiating a target class (of rumor or stance) from the remaining classes. Then, we propose a hierarchical attention mechanism to aggregate the binary predictions, including (1) a bottom-up/top-down tree attention layer to aggregate binary stances into binary veracity; and (2) a discriminative attention layer to aggregate the binary class into finer-grained classes. Extensive experiments conducted on three Twitter-based datasets demonstrate promising performance of our model on both claim-level rumor detection and post-level stance classification compared with state-of-the-art methods.
Customizable End-To-End Optimization Of Online Neural Network-Supported Dereverberation For Hearing Devices

This work focuses on online dereverberation for hearing devices using the weighted prediction error (WPE) algorithm. WPE filtering requires an estimate of the target speech power spectral density (PSD). Recently deep neural networks (DNNs) have been used for this task. However, these approaches optimize the PSD estimate which only indirectly affects the WPE output, thus potentially resulting in limited dereverberation. In this paper, we propose an end-to-end approach specialized for online processing, that directly optimizes the dereverberated output signal. In addition, we propose to adapt it to the needs of different types of hearing-device users by modifying the optimization target as well as the WPE algorithm characteristics used in training. We show that the proposed end-to-end approach outperforms the traditional and conventional DNN-supported WPEs on a noise-free version of the WHAMR! dataset.
Finite temperature description of an interacting Bose gas

We derive the equation of state of a Bose gas with contact interactions using relativistic quantum field theory. The calculation accounts for both thermal and quantum corrections up to 1-loop order. We work in the Hartree-Fock-Bogoliubov approximation and follow Yukalov's prescription of introducing two chemical potentials, one for the condensed phase and another one for the excited phase, to circumvent the well-known Hohenberg-Martin dilemma. As a check on the formalism, we take the non-relativistic limit and reproduce known non-relativistic results. Finally, we translate our results to the hydrodynamical, two-fluid model for finite-temperature superfluids. Our results are relevant for the phenomenology of Bose-Einstein Condensate and superfluid dark matter candidates, as well as the color-flavor locking phase of quark matter in neutron stars.
A comparison of voltage mode soft switching methods for PWM converters

A comparison study was conducted to characterize the loss mechanisms, component stresses, and overall efficiencies of a group of voltage mode soft switching PWM methods including two improvement circuits developed at UCI. All soft switching methods in the selected group allow zero voltage turn-on and turn-off of the main switch and utilize a single auxiliary switch with some resonant components. Advantages and disadvantages were identified for each method. Experimental verification for each soft switching method were provided.
On decay of the solutions for the dispersion generalized Benjamin--Ono and Benjamin--Ono equations

We show that uniqueness results of the kind those obtained for KdV and Schr¨odinger equations ([7], [28]), are not valid for the dispersion generalized-Benjamin-Ono equation in the weighted Sobolev spaces for appropriated s and r . In particular, we obtain that the uniqueness result proved for the dispersion generalized- Benjamin-Ono equation ([13]), is not true for all pairs of solutions u 1 6 = 0 and u 2 6 = 0. To achieve these results we employ the techniques present in our recent work [6]. We also improve some Theorems established for the dispersion generalized-Benjamin-Ono equation and for the Benjamin-Ono equation ([13], [12]).