Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysing Deep Learning-Spectral Envelope Prediction Methods for Singing Synthesis

We conduct an investigation on various hyperparameters regarding neural networks used to generate spectral envelopes for singing synthesis. Two perceptive tests, where the first compares two models directly and the other ranks models with a mean opinion score, are performed. With these tests we show that when learning to predict spectral envelopes, 2d-convolutions are superior over previously proposed 1d-convolutions and that predicting multiple frames in an iterated fashion during training is superior over injecting noise to the input data. An experimental investigation whether learning to predict a probability distribution vs. single samples was performed but turned out to be inconclusive. A network architecture is proposed that incorporates the improvements which we found to be useful and we show in our experiments that this network produces better results than other stat-of-the-art methods.
On Nominal Syntax and Permutation Fixed Points

We propose a new axiomatisation of the alpha-equivalence relation for nominal terms, based on a primitive notion of fixed-point constraint. We show that the standard freshness relation between atoms and terms can be derived from the more primitive notion of permutation fixed-point, and use this result to prove the correctness of the new $\alpha$-equivalence axiomatisation. This gives rise to a new notion of nominal unification, where solutions for unification problems are pairs of a fixed-point context and a substitution. Although it may seem less natural than the standard notion of nominal unifier based on freshness constraints, the notion of unifier based on fixed-point constraints behaves better when equational theories are considered: for example, nominal unification remains finitary in the presence of commutativity, whereas it becomes infinitary when unifiers are expressed using freshness contexts. We provide a definition of $\alpha$-equivalence modulo equational theories that take into account A, C and AC theories. Based on this notion of equivalence, we show that C-unification is finitary and we provide a sound and complete C-unification algorithm, as a first step towards the development of nominal unification modulo AC and other equational theories with permutative properties.
A Microservice Architecture for Online Mobile App Optimization

A large number of techniques for analyzing and optimizing mobile apps have emerged in the past decade. However, those techniques' components are notoriously difficult to extract and reuse outside their original tools. This paper introduces MAOMAO, a microservice-based reference architecture for reusing and integrating such components. MAOMAO's twin goals are (1) adoption of available app optimization techniques in practice and (2) improved construction and evaluation of new techniques. The paper uses several existing app optimization techniques to illustrate both the motivation behind MAOMAO and its potential to fundamentally alter the landscape in this area.
PFLD: A Practical Facial Landmark Detector

Being accurate, efficient, and compact is essential to a facial landmark detector for practical use. To simultaneously consider the three concerns, this paper investigates a neat model with promising detection accuracy under wild environments e.g., unconstrained pose, expression, lighting, and occlusion conditions) and super real-time speed on a mobile device. More concretely, we customize an end-to-end single stage network associated with acceleration techniques. During the training phase, for each sample, rotation information is estimated for geometrically regularizing landmark localization, which is then NOT involved in the testing phase. A novel loss is designed to, besides considering the geometrical regularization, mitigate the issue of data imbalance by adjusting weights of samples to different states, such as large pose, extreme lighting, and occlusion, in the training set. Extensive experiments are conducted to demonstrate the efficacy of our design and reveal its superior performance over state-of-the-art alternatives on widely-adopted challenging benchmarks, i.e., 300W (including iBUG, LFPW, AFW, HELEN, and XM2VTS) and AFLW. Our model can be merely 2.1Mb of size and reach over 140 fps per face on a mobile phone (Qualcomm ARM 845 processor) with high precision, making it attractive for large-scale or real-time applications. We have made our practical system based on PFLD 0.25X model publicly available at \url{this http URL} for encouraging comparisons and improvements from the community.
Event Detection in Twitter Stream using Weighted Dynamic Heartbeat Graph Approach

Tweets about everyday events are published on Twitter. Detecting such events is a challenging task due to the diverse and noisy contents of Twitter. In this paper, we propose a novel approach named Weighted Dynamic Heartbeat Graph (WDHG) to detect events from the Twitter stream. Once an event is detected in a Twitter stream, WDHG suppresses it in later stages, in order to detect new emerging events. This unique characteristic makes the proposed approach sensitive to capture emerging events efficiently. Experiments are performed on three real-life benchmark datasets: FA Cup Final 2012, Super Tuesday 2012, and the US Elections 2012. Results show considerable improvement over existing event detection methods in most cases.
Fast Distance Fields for Fluid Dynamics Mesh Generation on Graphics Hardware

We present a CUDA accelerated implementation of the Characteristic/Scan Conversion algorithm to generate narrow band signed distance fields in logically Cartesian grids. We outline an approach of task and data management on GPUs based on an input of a closed triangulated surface with the aim of reducing pre-processing and mesh-generation times. The work demonstrates a fast signed distance field generation of triangulated surfaces with tens of thousands to several million features in high resolution domains. We present improvements to the robustness of the original algorithm and an overview of handling geometric data.
A Framework for Detecting Event related Sentiments of a Community

Social media has revolutionized human communication and styles of interaction. Due to its easiness and effective medium, people share and exchange information, carry out discussion on various events, and express their opinions. For effective policy making and understanding the response of a community on different events, we need to monitor and analyze the social media. In social media, there are some users who are more influential, for example, a famous politician may have more influence than a common person. These influential users belong to specific communities. The main object of this research is to know the sentiments of a specific community on various events. For detecting the event based sentiments of a community we propose a generic framework. Our framework identifies the users of a specific community on twitter. After identifying the users of a community, we fetch their tweets and identify tweets belonging to specific events. The event based tweets are pre-processed. Pre-processed tweets are then analyzed for detecting sentiments of a community for specific events. Qualitative and quantitative evaluation confirms the effectiveness and usefulness of our proposed framework.
Microwave-assisted green synthesis of Gold nanoparticles and Its catalytic activity

The present work demonstrated a green approach of synthesis of gold nanoparticles using Eupatorium odoratum leaf extract as reducing and stabilizing agent assisted with microwave irradiation. Effects of various concentrations of leaf extract on the preparation of gold nanoparticles have been investigated and it was monitored by undertaking UV-vis spectroscopic studies. The experimental results showed that the surface Plasmon resonance (SPR) peak blue shifted with increase in the concentration of leaf extract indicating the decrease of nanoparticles size, which was further confirmed by the Dynamic light scattering (DLS) data. Synthesized particles were further characterized by Fourier Transform Infra-Red spectroscopy (FTIR) and Transmission Electron Microscopy (TEM). All the characterization results confirmed the formation of stable spherical mono-dispersed gold nanoparticles with size ranging from 10-20 nm. The catalytic activity of prepared gold nanoparticles was checked by reducing Nitrophenol to Aminophenol in presence of an excess amount of sodium borohydride. The progress of the reaction was examined by observing the absorbance peak of UV-vis spectroscopy. The study showed positive results and it was found that gold nanoparticles synthesized with 600 µL leaf extract have greater catalytic activity. It was also found that gold nanoparticles remained stable for long duration of time. The present work demonstrated a green approach of synthesis of gold nanoparticles using Eupatorium odoratum leaf extract as reducing and stabilizing agent assisted with microwave irradiation. Effects of various concentrations of leaf extract on the preparation of gold nanoparticles have been investigated and it was monitored by undertaking UV-vis spectroscopic studies. The experimental results showed that the surface Plasmon resonance (SPR) peak blue shifted with increase in the concentration of leaf extract indicating the decrease of nanoparticles size, which was further confirmed by the Dynamic light scattering (DLS) data. Synthesized particles were further characterized by Fourier Transform Infra-Red spectroscopy (FTIR) and Transmission Electron Microscopy (TEM). All the characterization results confirmed the formation of stable spherical mono-dispersed gold nanoparticles with size ranging from 10-20 nm. The catalytic activity of prepared gold nanoparticles was checked by reducing Nitrophenol to Aminophenol in presence of an excess amount of sodium borohydride. The progress of the reaction was examined by observing the absorbance peak of UV-vis spectroscopy. The study showed positive results and it was found that gold nanoparticles synthesized with 600 µL leaf extract have greater catalytic activity. It was also found that gold nanoparticles remained stable for long duration of time.
DeepGini: Prioritizing Massive Tests to Reduce Labeling Cost

Deep neural network (DNN) based systems have been deployed to assist various tasks, including many safety-critical scenarios such as autonomous driving and medical image diagnostics. In company with the DNN-based systems' fantastic accuracy on the well-defined tasks, these systems could also exhibit incorrect behaviors and thus severe accidents and losses. Therefore, beyond the conventional accuracy-based evaluation, the testing method that can assist developers in detecting incorrect behaviors in the earlier stage is critical for quality assurance of these systems. However, given the fact that automated oracle is often not available, testing DNN-based system usually requires prohibitively expensive human efforts to label the testing data. In this paper, to reduce the efforts in labeling the testing data of DNN-based systems, we propose DeepGini, a test prioritization technique for assisting developers in identifying the tests that can reveal the incorrect behavior. DeepGini is designed based on a statistical perspective of DNN, which allows us to transform the problem of measuring the likelihood of misclassification to the problem of measuing the impurity of data set. To validate our technique, we conduct an extensive empirical study on four popular datasets. The experiment results show that DeepGini outperforms the neuron-coverage-based test prioritization in terms of both efficacy and efficiency.
An Abstract View on the De-anonymization Process

Over the recent years, the availability of datasets containing personal, but anonymized information has been continuously increasing. Extensive research has revealed that such datasets are vulnerable to privacy breaches: being able to reveal sensitive information about individuals through deanonymization methods. Here, we provide a taxonomy of the research in de-anonymization.
Augmenting Neural Machine Translation with Knowledge Graphs

While neural networks have been used extensively to make substantial progress in the machine translation task, they are known for being heavily dependent on the availability of large amounts of training data. Recent efforts have tried to alleviate the data sparsity problem by augmenting the training data using different strategies, such as back-translation. Along with the data scarcity, the out-of-vocabulary words, mostly entities and terminological expressions, pose a difficult challenge to Neural Machine Translation systems. In this paper, we hypothesize that knowledge graphs enhance the semantic feature extraction of neural models, thus optimizing the translation of entities and terminological expressions in texts and consequently leading to a better translation quality. We hence investigate two different strategies for incorporating knowledge graphs into neural models without modifying the neural network architectures. We also examine the effectiveness of our augmentation method to recurrent and non-recurrent (self-attentional) neural architectures. Our knowledge graph augmented neural translation model, dubbed KG-NMT, achieves significant and consistent improvements of +3 BLEU, METEOR and chrF3 on average on the newstest datasets between 2014 and 2018 for WMT English-German translation task.
Ultra-Low Program Current and Multilevel Phase Change Memory for High-Density Storage Achieved by a Low-Current SET Pre-Operation

We have fabricated a pillar structure phase change memory (PCM) with the prototypical phase change material Ge2Sb2Te5. Using a Low Current SET (LCS) pre-operation, the devices can operate with an ultralow RESET current density and have multilevel cell (MLC) capability, and these features both contribute to high storage density. Specifically, the RESET current density is only 4.84 MA/cm2, which is lowest among all the CMOS compatible PCM devices, and this ultra-low RESET current density enables the possibility to link with a two-terminal selector for 3D cross-point application. Meanwhile LCS makes MLC operation of our PCM more stable with a low operating current. In summary, this LCS pre-operation can greatly increase the storage density both by enabling integration with selectors for 3D cross-point and by stabilizing MLC operation when maintaining the feature size of PCM devices.
Hybrid Beamforming for Millimeter Wave Systems Using the MMSE Criterion

Hybrid analog and digital beamforming (HBF) has recently emerged as an attractive technique for millimeter-wave (mmWave) communication systems. It well balances the demand for sufficient beamforming gains to overcome the propagation loss and the desire to reduce the hardware cost and power consumption. In this paper, the mean square error (MSE) is chosen as the performance metric to characterize the transmission reliability. Using the minimum sum-MSE criterion, we investigate the HBF design for broadband mmWave transmissions. To overcome the difficulty of solving the multi-variable design problem, the alternating minimization method is adopted to optimize the hybrid transmit and receive beamformers alternatively. Specifically, a manifold optimization-based HBF algorithm is first proposed, which directly handles the constant modulus constraint of the analog component. Its convergence is then proved. To reduce the computational complexity, we then propose a low-complexity general eigenvalue decomposition-based HBF algorithm in the narrowband scenario and three algorithms via the eigenvalue decomposition and orthogonal matching pursuit methods in the broadband scenario. A particular innovation in our proposed alternating minimization algorithms is a carefully designed initialization method, which leads to a faster convergence. Furthermore, we extend the sum-MSE-based design to that with weighted sum-MSE, which is then connected to the spectral efficiency-based design. Simulation results show that the proposed HBF algorithms achieve a significant performance improvement over existing ones and perform close to full-digital beamforming.
Online Object and Task Learning via Human Robot Interaction

This work describes the development of a robotic system that acquires knowledge incrementally through human interaction where new objects and motions are taught on the fly. The robotic system developed was one of the five finalists in the KUKA Innovation Award competition and demonstrated during the Hanover Messe 2018 in Germany. The main contributions of the system are i) a novel incremental object learning module - a deep learning based localization and recognition system - that allows a human to teach new objects to the robot, ii) an intuitive user interface for specifying 3D motion task associated with the new object, and iii) a hybrid force-vision control module for performing compliant motion on an unstructured surface. This paper describes the implementation and integration of the main modules of the system and summarizes the lessons learned from the competition.
Transfer Learning for Performance Modeling of Configurable Systems: A Causal Analysis

Modern systems (e.g., deep neural networks, big data analytics, and compilers) are highly configurable, which means they expose different performance behavior under different configurations. The fundamental challenge is that one cannot simply measure all configurations due to the sheer size of the configuration space. Transfer learning has been used to reduce the measurement efforts by transferring knowledge about performance behavior of systems across environments. Previously, research has shown that statistical models are indeed transferable across environments. In this work, we investigate identifiability and transportability of causal effects and statistical relations in highly-configurable systems. Our causal analysis agrees with previous exploratory analysis \cite{Jamshidi17} and confirms that the causal effects of configuration options can be carried over across environments with high confidence. We expect that the ability to carry over causal relations will enable effective performance analysis of highly-configurable systems.
Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation

Recent advances in conditional image generation tasks, such as image-to-image translation and image inpainting, are largely accounted to the success of conditional GAN models, which are often optimized by the joint use of the GAN loss with the reconstruction loss. However, we reveal that this training recipe shared by almost all existing methods causes one critical side effect: lack of diversity in output samples. In order to accomplish both training stability and multimodal output generation, we propose novel training schemes with a new set of losses named moment reconstruction losses that simply replace the reconstruction loss. We show that our approach is applicable to any conditional generation tasks by performing thorough experiments on image-to-image translation, super-resolution and image inpainting using Cityscapes and CelebA dataset. Quantitative evaluations also confirm that our methods achieve a great diversity in outputs while retaining or even improving the visual fidelity of generated samples.
A Deep, Information-theoretic Framework for Robust Biometric Recognition

Deep neural networks (DNN) have been a de facto standard for nowadays biometric recognition solutions. A serious, but still overlooked problem in these DNN-based recognition systems is their vulnerability against adversarial attacks. Adversarial attacks can easily cause the output of a DNN system to greatly distort with only tiny changes in its input. Such distortions can potentially lead to an unexpected match between a valid biometric and a synthetic one constructed by a strategic attacker, raising security issue. In this work, we show how this issue can be resolved by learning robust biometric features through a deep, information-theoretic framework, which builds upon the recent deep variational information bottleneck method but is carefully adapted to biometric recognition tasks. Empirical evaluation demonstrates that our method not only offers stronger robustness against adversarial attacks but also provides better recognition performance over state-of-the-art approaches.
Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics

Early detection of lung cancer is essential in reducing mortality. Recent studies have demonstrated the clinical utility of low-dose computed tomography (CT) to detect lung cancer among individuals selected based on very limited clinical information. However, this strategy yields high false positive rates, which can lead to unnecessary and potentially harmful procedures. To address such challenges, we established a pipeline that co-learns from detailed clinical demographics and 3D CT images. Toward this end, we leveraged data from the Consortium for Molecular and Cellular Characterization of Screen-Detected Lesions (MCL), which focuses on early detection of lung cancer. A 3D attention-based deep convolutional neural net (DCNN) is proposed to identify lung cancer from the chest CT scan without prior anatomical location of the suspicious nodule. To improve upon the non-invasive discrimination between benign and malignant, we applied a random forest classifier to a dataset integrating clinical information to imaging data. The results show that the AUC obtained from clinical demographics alone was 0.635 while the attention network alone reached an accuracy of 0.687. In contrast when applying our proposed pipeline integrating clinical and imaging variables, we reached an AUC of 0.787 on the testing dataset. The proposed network both efficiently captures anatomical information for classification and also generates attention maps that explain the features that drive performance.
Preconditioning for the Geometric Transportation Problem

In the geometric transportation problem, we are given a collection of points $P$ in $d$-dimensional Euclidean space, and each point is given a supply of $\mu(p)$ units of mass, where $\mu(p)$ could be a positive or a negative integer, and the total sum of the supplies is $0$. The goal is to find a flow (called a transportation map) that transports $\mu(p)$ units from any point $p$ with $\mu(p) > 0$, and transports $-\mu(p)$ units into any point $p$ with $\mu(p) < 0$. Moreover, the flow should minimize the total distance traveled by the transported mass. The optimal value is known as the transportation cost, or the Earth Mover's Distance (from the points with positive supply to those with negative supply). This problem has been widely studied in many fields of computer science: from theoretical work in computational geometry, to applications in computer vision, graphics, and machine learning. In this work we study approximation algorithms for the geometric transportation problem. We give an algorithm which, for any fixed dimension $d$, finds a $(1+\varepsilon)$-approximate transportation map in time nearly-linear in $n$, and polynomial in $\varepsilon^{-1}$ and in the logarithm of the total supply. This is the first approximation scheme for the problem whose running time depends on $n$ as $n\cdot \mathrm{polylog}(n)$. Our techniques combine the generalized preconditioning framework of Sherman, which is grounded in continuous optimization, with simple geometric arguments to first reduce the problem to a minimum cost flow problem on a sparse graph, and then to design a good preconditioner for this latter problem.