Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Hierarchy Preserving Graph Embedding via Subspace Constraints

Hierarchy preserving network embedding is a method that project nodes into feature space by preserving the hierarchy property of networks. Recently, researches on network representation have considerably profited from taking hierarchy into consideration. Among these works, SpaceNE1 [1] stands out by preserving hierarchy with the help of subspace constraints on the hierarchy subspace system. However, like all other hierarchy preserving network embedding methods, SpaceNE is time-consuming and cannot generalize to new nodes. In this paper, we propose an inductive method, FastHGE, to learn node representations more efficiently and generalize to new nodes more easily. Empirically, the experiment of node classification demonstrates that the convergence speed of FastHGE is increased by 30 times in the case of the same accuracy with SpaceNE.
GDTW: A Novel Differentiable DTW Loss for Time Series Tasks

Dynamic time warping (DTW) is one of the most successful methods that addresses the challenge of measuring the discrepancy between two series, which is robust to shift and distortion along the time axis of the sequence. Based on DTW, we propose a novel loss function for time series data called Gumbel-Softmin based fast DTW (GDTW). To the best of our knowledge, this is the first differentiable DTW loss for series data that scales linearly with the sequence length. The proposed Gumbel-Softmin replaces the simple minimization operator in DTW so as to better integrate the acceleration technology. We also design a deep learning model combining GDTW as a feature extractor. Thorough experiments over a broad range of time series analysis tasks were performed, showing the efficiency and effectiveness of our method.
Compressed Representation of Cepstral Coefficients via Recurrent Neural Networks for Informed Speech Enhancement

Speech enhancement is one of the biggest challenges in hearing prosthetics. In face-to-face communication devices have to estimate the signal of interest, but playback of speech signals from an electronic device opens up new opportunities. Audio signals can be enriched with hidden data, which can subsequently be decoded by the receiver. We investigate a hybrid strategy made of signal processing and RNN (Recurrent Neural Networks) to calculate and compress cepstral coefficients: these are descriptors of the speech signal, which can be embedded in the signal itself and used at the receiver’s end to perform an Informed Speech Enhancement. Objective evaluations showed an increase in speech quality for noisy signals enhanced with our method.
Synthetic Aperture Acoustic Imaging with Deep Generative Model Based Source Distribution Prior

Acoustic imaging has a wide range of real-world applications such as machine health monitoring. Conventionally, large microphone arrays are utilized to achieve useful spatial resolution in the imaging process. The advent of location-aware autonomous mobile robotic platforms opens up unique opportunity to apply synthetic aperture techniques to the acoustic imaging problem. By leveraging motion and location cues as well as some available prior information on the source distribution, a small moving microphone array has the potential to achieve imaging resolution far beyond the physical aperture limits. In this work, we propose to image large acoustic sources with a combination of synthetic aperture and their geometric structures modeled by a conditional generative adversarial network (cGAN). The acoustic imaging problem is formulated as a linear inverse problem and solved with the gradient-based method. Numerical simulations show that our synthetic aperture imaging framework can reconstruct the acoustic source distribution from microphone recordings and outperform static microphone arrays.
Demystifying Model Averaging for Communication-Efficient Federated Matrix Factorization

Federated learning (FL) is encountered with the challenge of training a model in massive and heterogeneous networks. Model averaging (MA) has become a popular FL paradigm where parallel (stochastic) gradient descent (GD) is run on a small sampled subset of clients multiple times before uploading the local models to a server for averaging, which has been proven effective in reducing the communication cost for achieving a good model. However, MA has not been considered for the important matrix factorization (MF) model, which has vast signal processing and machine learning applications. In this paper, we investigate the federated MF problem and propose a new MA based algorithm, named FedMAvg, by judiciously combining the alternating minimization technique and MA. Through analysis, we show that gradually decreasing the number of local GD and only allowing partial clients to communicate with the server can greatly reduce the communication cost, especially in heterogeneous networks with non-i.i. d. data. Experimental results by applying FedMAvg to data clustering and item recommendation tasks demonstrate its efficacy in terms of both task performance and communication efficiency.
A Large-Dimensional Analysis of Symmetric SNE

Stochastic Neighbour Embedding methods (SNE, t-SNE) aim at finding a faithful low-dimensional representation of a high-dimensional dataset. Despite their popularity, being solution to a non-convex optimization, the behavior of these tools is not well understood. This work provides first answers by leveraging a large dimensional statistics approach, where the number n and dimension p of the large-dimensional data are of the same magnitude. We derive and study the canonical equation verified by the critical points of this non-convex optimization problem. The study notably reveals that, in a simple setup, the achievable SNE solutions correspond to a subset of those critical points. In particular, when the clusters composing the dataset are balanced in size, these solutions are symmetrical and assume closed-form expressions.As a major conclusion, the analysis rigorously proves a long-standing heuristic statement on the "proper normalization" of the symmetric SNE: out of two natural normalization choices, only the claimed proper one leads to non-trivial solutions.
Measurement Coding Framework with Adjacent Pixels Based Measurement Matrix for Compressively Sensed Images

To further compress measurements, the output of block-based compressed sensing, this work presents a measurement coding framework using measurement-domain intra prediction. In the framework, a deterministic measurement matrix based on the correlation of adjacent pixels (APMM) is proposed to embed the pixel-domain boundary information of each block to the measurement domain. By adopting APMM, the pixel-domain information can be efficiently used for measurement-domain intra prediction. To avoid the interference of pixels that are far apart and achieve a high prediction accuracy, we employ boundary measurements of neighboring blocks as reference for prediction. Finally, the residuals between measurements and predictions are processed by quantization and Huffman coding to generate a coded bit sequence for transmitting. Compared to the state-of-the-art, this work achieves a 24% decrease in bitrate and a 1.68dB increase in PSNR on average.
Arrays of First-Order Steerable Differential Microphones

The literature is rich with techniques for the design of small-size Differential Microphone Arrays (DMAs), known for their almost frequency-invariant beampatterns and low computational cost. Few works, instead, discuss the properties of beamformers based on multiple DMA units. In this paper, we consider arbitrarily shaped planar arrays of DMA units. In turn, each DMA unit is a first-order continuously-steerable differential microphone characterized by an arbitrary configuration of omnidirectional sensors and a symmetric beampattern. We present a beamforming technique that, assumed all the DMA units to steer identical beams in the same direction, allows us to approach the behavior of a Delay-And-Sum beamformer or Super-Directive beamformer by solely varying a single scalar parameter. Efficient implementations of the proposed beamformers can be developed by taking into account that, for a wide range of frequencies, the values of such a parameter are practically invariant with respect to the geometry of the array.
A Fast Randomized Adaptive CP Decomposition For Streaming Tensors

In this paper, we introduce a fast adaptive algorithm for CAN- DECOMP/PARAFAC decomposition of streaming three-way tensors using randomized sketching techniques. By leveraging randomized least-squares regression and approximating matrix multiplication, we propose an efficient first-order estimator to minimize an exponentially weighted recursive least- squares cost function. Our algorithm is fast, requiring a low computational complexity and memory storage. Experiments indicate that the proposed algorithm is capable of adaptive tensor decomposition with a competitive performance evaluation on both synthetic and real data.
The power of negative reasoning

Semialgebraic proof systems have been studied extensively in proof complexity since the late 1990s to understand the power of Gröbner basis computations, linear and semidefinite programming hierarchies, and other methods. Such proof systems are defined alternately with only the original variables of the problem and with special formal variables for positive and negative literals, but there seems to have been no study how these different definitions affect the power of the proof systems. We show for Nullstellensatz, polynomial calculus, Sherali-Adams, and sums-of-squares that adding formal variables for negative literals makes the proof systems exponentially stronger, with respect to the number of terms in the proofs. These separations are witnessed by CNF formulas that are easy for resolution, which establishes that polynomial calculus, Sherali-Adams, and sums-of-squares cannot efficiently simulate resolution without having access to variables for negative literals.
End-to-End Learning of Variational Models and Solvers for the Resolution of Interpolation Problems

Variational models are among the state-of-the-art formulations for the resolution of ill-posed inverse problems. Following recent advances in learning-based variational settings, we investigate the end-to-end learning of variational models, more precisely of the regularization term given some observation model, jointly to the associated solver, so that we can optimize the reconstruction performance. In the proposed end-to-end setting, both the variational cost and the gradient-based solver are stated as neural networks using automatic differentiation for the latter. We consider an application to inverse problems with incomplete datasets (image inpainting and multivariate time series interpolation). We experimentally illustrate that this framework can lead to a significant gain in terms of reconstruction performance, including w.r. t. the direct minimization of the variational formulation derived from the known generative model.
Online Hyper-Parameter Tuning for the Contextual Bandit

We study here the problem of learning the exploration exploitation trad-off in the contextual bandit problem with linear reward function setting. In the traditional algorithms that solve the contextual bandit problem, the exploration is a parameter that is tuned by the user. However, our proposed algorithm learn to choose the right exploration parameters in an online manner based on the observed context, and the immediate reward received for the chosen action. We have presented here two algorithms that uses a bandit to find the optimal exploration of the contextual bandit algorithm, which we hope is the first step toward the automation of the multi-armed bandit algorithm.
Cross-Corpus Speech Emotion Recognition Using Joint Distribution Adaptive Regression

In this paper, we focus on the research of cross-corpus speech emotion recognition (SER), in which the training and testing speech signals in cross-corpus SER belong to dierent speech corpus. Due to this fact, mismatched feature distributions may exist between the training and testing speech feature sets degrading the performance of most originally well-performing SER methods. To deal with cross-corpus SER, we propose a novel domain adaptation (DA) method called joint distribution adaptive regression (JDAR). The basic idea of JDAR is to learn a regression matrix by jointly considering the marginal and conditional probability distribution between the training and testing speech signals and hence their feature distribution dierence can be alleviated in the subspace spanned by the learned regression matrix. To evaluate the proposed JDAR, we conduct extensive cross-corpus SER experiments on EmoDB, eNTERFACE, and CASIA speech databases. Experimental results show that the proposed JDAR achieves satisfactory performance and outperforms most of state-of-the-art subspace learning based DA methods.
Scalable Privacy-Preserving Distributed Extremely Randomized Trees for Structured Data With Multiple Colluding Parties

Today, in many real-world applications of machine learning algorithms, the data is stored on multiple sources instead of at one central repository. In many such scenarios, due to privacy concerns and legal obligations, e.g., for medical data, and communication/computation overhead, for instance for large scale data, the raw data cannot be transferred to a center for analysis. Therefore, new machine learning approaches are proposed for learning from the distributed data in such settings. In this paper, we extend the distributed Extremely Randomized Trees (ERT) approach w.r. t. privacy and scalability. First, we extend distributed ERT to be resilient w.r. t. the number of colluding parties in a scalable fashion. Then, we extend the distributed ERT to improve its scalability without any major loss in classification performance. We refer to our proposed approach as k-PPD-ERT or Privacy-Preserving Distributed Extremely Randomized Trees with k colluding parties.
Extending Music Based On Emotion And Tonality Via Generative Adversarial Network

We propose a generative model for music extension in this paper. The model is composed of two classifiers, one for music emotion and one for music tonality, and a generative adversarial network (GAN). Therefore, it can generate symbolic music not only based on low level spectral and temporal characteristics, but also on high level emotion and tonality attributes of previously observed music pieces. The generative model works in a universal latent space constructed by the variational autoencoder (VAE) for representing music pieces. We conduct subjective listening tests and derive objective measures for performance evaluation. Experimental results show that the proposed model produces much smoother and more authentic music pieces than the baseline model in terms of all subjective and objective measures.
Wasserstein Barycenter Transport for Acoustic Adaptation

The recognition of music genre and the discrimination between music and speech are important components of modern digital music systems. Depending on the acquisition conditions, such as background environment, these signals may come from different probability distributions, making the learning problem complicated. In this context, domain adaptation is a key theory to improve performance. Considering data coming from various background conditions, the adaptation scenario is called multi-source. This paper proposes a multi-source domain adaptation algorithm called Wasserstein Barycenter Transport, which transports the source domains to a target domain by creating an intermediate domain using the Wasserstein barycenter. Our method outperforms other state-of-the-art algorithms, and performs better than classifiers trained with target-only data.
Branching programs with bounded repetitions and flow formulas

Restricted branching programs capture various complexity measures like space in Turing machines or length of proofs in proof systems. In this paper, we focus on the application in the proof complexity that was discovered by Lovasz et al. [14] who showed the equivalence between regular Resolution and read-once branching programs for "unsatisfied clause search problem" (Searchφ). This connection is widely used, in particular, in the recent breakthrough result about the Clique problem in regular Resolution by Atserias et al. [5]. We study the branching programs with bounded repetitions, so-called (1, +k)-BPs (Sieling [21]) in application to the Searchφ problem. On the one hand, it is a natural generalization of read-once branching programs. On the other hand, this model gives a powerful proof system that can efficiently certify the unsatisfiability of a wide class of formulas that is hard for Resolution (Knop [13]). We deal with Searchφ that is "relatively easy" compared to all known hard examples for the (1, +k)-BPs. We introduce the first technique for proving exponential lower bounds for the (1, +k)-BPs on Searchφ. To do it we combine a well-known technique for proving lower bounds on the size of branching programs [12,21,22] with the modification of the "closure" technique [1,3]. In contrast with most Resolution lower bounds, our technique uses not only "local" properties of the formula, but also a "global" structure. Our hard examples are based on the Flow formulas introduced in [3].
Applied Methods for Sparse Sampling of Head-Related Transfer Functions

Production of high fidelity spatial audio applications requires individual head-related transfer functions (HRTFs). As the acquisition of HRTF is an elaborate process, interest lies in interpolating full length HRTF from sparse samples. Ear-alignment is a recently developed pre-processing technique, shown to reduce an HRTF’s spherical harmonics order, thus permitting sparse sampling over fewer directions. This paper describes the application of two methods for ear-aligned HRTF interpolation by sparse sampling: Orthogonal Matching Pursuit and Principal Component Analysis. These methods consist of generating unique vector sets for HRTF representation. The methods were tested over an HRTF dataset, indicating that interpolation errors using small sampling schemes may be further reduced by up to 5 dB in comparison with spherical harmonics interpolation.
Experimental and Theoretical Characterization of Metal Complexation with Humic Acid

ABSTRACT This study investigated the bonding of calcium and magnesium to specific sites in humic acid as a function of pH, temperature, and trace metal concentrations. Other metals were investigated but not in detail. Using a surface complexation model with well-defined phenolic and carboxylic sites in humic acid, the fractions of calcium/magnesium and trace metals that bond with the various sites were quantified. These sites were also classified as monodentate or bidentate. The goal of the study was to understand the conditions that favor calcium/magnesium bonding to bidentate humic acid. Therefore, adsorption measurements were performed followed by surface complexation simulations. The total metal and labile metal ion formed in the ultrafiltrate were determined by inductively coupled plasma mass spectrometry and square-wave anodic stripping voltammetry, respectively. The results showed that the adsorption capacity of each metal to humic acid was influenced by pH and metal loading. For example, zinc(II) and copper(II) were statistically different in their adsorption capacities. The Morel and Dzombak surface complexation models showed stronger lead and copper interactions with monodentate sites compared to bidentate sites. The phenolic sites were weakly involved in bonding, and the bonding capacities of calcium and magnesium ions to phenolic or carboxylic were pH dependent. For example, lead(II) used only 5% of bidentate carboxylic–phenolic sites at 45°C showing 95% favorable calcium/magnesium bonding to these sites in humic acid. These results may allow substantial scale reduction.
Effectiveness of Oil-Based Denture Dentifrices-Organoleptic Characteristics, Physicochemical Properties and Antimicrobial Action

Denture dentifrices must be effective and not deleterious to prosthetic devices. This study formulated and evaluated dentifrices based on oils of Copaifera officinalis, Eucalyptus citriodora, Melaleuca alternifolia, Pinus strobus, and Ricinus communis. Organoleptic characteristics (appearance, color, odor, taste), physicochemical properties (pH, density, consistency, rheological, abrasiveness, weight loss, and surface roughness) and antimicrobial (Hole-Plate Diffusion–HPD)/anti-biofilm (Colony Forming Units–CFU) action against Staphylococcus aureus, Streptococcus mutans, and Candida albicans were evaluated. Formulations were compared with water (negative control) and a commercial dentifrice (positive control). The data were analyzed by Kruskal-Wallis and Dunn tests (α = 0.05). The organoleptic and physicochemical properties were adequate. All dentifrices promoted weight losses, with high values for C. officinalis and R. communis, and an increase in surface roughness, without differing from each other. For antimicrobial action, C. officinalis and E. citriodora dentifrices were similar to positive control showing effectiveness against S. mutans and C. albicans and no dentifrice was effective against S. aureus; regarding the anti-biofilm action, the dentifrices were not effective, showing higher CFU counts than positive control for all microorganisms. The dentifrices presented satisfactory properties; and, although they showed antimicrobial action when evaluated by HPD, they showed no effective anti-biofilm action on multispecies biofilm.
Investigation of Active Learning Techniques For dynamic Time-of-Use (dToU) Tariff Policy Design for Residential Users

The active learning approach is a special case of semi-supervised machine learning which is able to interactively query the user to reduce the uncertainty of the machine learning model. The approach is useful to minimize the data labelling cost. The project aims to study and use this method to characterize residential electricity users’ demand response to improve the prediction accuracy of energy demand. During the trials, the policies which penalise or incentivize the users to change their behaviour involve a cost associated with the grid management. Therefore, the experiments which include the above-mentioned policies are considered as cost bearing experiments. The goal of this project is to study the effect of selective sampling and random sampling of such cost bearing experiments on energy consumption prediction accuracy in simulated residential energy consumption environment. Firstly, we show the simulator design for simulating demand response of users under dynamic tariff policy. Then, we investigate two selective sampling methodsvariance reduction and novelty detection. The performance of these methods under various criteria is analysed.