Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydrodynamic Approach to Electronic Transport in Graphene: Energy Relaxation

In nearly compensated graphene, disorder-assisted electron-phonon scattering or “supercollisions” are responsible for both quasiparticle recombination and energy relaxation. Within the hydrodynamic approach, these processes contribute weak decay terms to the continuity equations at local equilibrium, i.e., at the level of “ideal” hydrodynamics. Here we report the derivation of the decay term due to weak violation of energy conservation. Such terms have to be considered on equal footing with the well-known recombination terms due to nonconservation of the number of particles in each band. At high enough temperatures in the “hydrodynamic regime” supercollisions dominate both types of the decay terms (as compared to the leading-order electron-phonon interaction). We also discuss the contribution of supercollisions to the heat transfer equation (generalizing the continuity equation for the energy density in viscous hydrodynamics).
Efficient CNN Building Blocks for Encrypted Data

Machine learning on encrypted data can address the concerns related to privacy and legality of sharing sensitive data with untrustworthy service providers. Fully Homomorphic Encryption (FHE) is a promising technique to enable machine learning and inferencing while providing strict guarantees against information leakage. Since deep convolutional neural networks (CNNs) have become the machine learning tool of choice in several applications, several attempts have been made to harness CNNs to extract insights from encrypted data. However, existing works focus only on ensuring data security and ignore security of model parameters. They also report high level implementations without providing rigorous analysis of the accuracy, security, and speed trade-offs involved in the FHE implementation of generic primitive operators of a CNN such as convolution, non-linear activation, and pooling. In this work, we consider a Machine Learning as a Service (MLaaS) scenario where both input data and model parameters are secured using FHE. Using the CKKS scheme available in the open-source HElib library, we show that operational parameters of the chosen FHE scheme such as the degree of the cyclotomic polynomial, depth limitations of the underlying leveled HE scheme, and the computational precision parameters have a major impact on the design of the machine learning model (especially, the choice of the activation function and pooling method). Our empirical study shows that choice of aforementioned design parameters result in significant trade-offs between accuracy, security level, and computational time. Encrypted inference experiments on the MNIST dataset indicate that other design choices such as ciphertext packing strategy and parallelization using multithreading are also critical in determining the throughput and latency of the inference process.
Interpretable Reinforcement Learning Inspired by Piaget's Theory of Cognitive Development

Endeavors for designing robots with human-level cognitive abilities have led to different categories of learning machines. According to Skinner's theory, reinforcement learning (RL) plays a key role in human intuition and cognition. Majority of the state-of-the-art methods including deep RL algorithms are strongly influenced by the connectionist viewpoint. Such algorithms can significantly benefit from theories of mind and learning in other disciplines. This paper entertains the idea that theories such as language of thought hypothesis (LOTH), script theory, and Piaget's cognitive development theory provide complementary approaches, which will enrich the RL field. Following this line of thinking, a general computational building block is proposed for Piaget's schema theory that supports the notions of productivity, systematicity, and inferential coherence as described by Fodor in contrast with the connectionism theory. Abstraction in the proposed method is completely upon the system itself and is not externally constrained by any predefined architecture. The whole process matches the Neisser's perceptual cycle model. Performed experiments on three typical control problems followed by behavioral analysis confirm the interpretability of the proposed method and its competitiveness compared to the state-of-the-art algorithms. Hence, the proposed framework can be viewed as a step towards achieving human-like cognition in artificial intelligent systems.
Magnetic order of tetragonal CuO ultrathin films

We present a detailed low-energy muon spin rotation and x-ray magnetic circular dichroism (XMCD) investigation of the magnetic structure in ultrathin tetragonal (T)-CuO ﬁlms. The measured muon-spin polarization decay indicates an antiferromagnetic (AFM) order with a transition temperature higher than 200 K. The XMCD signal obtained around the Cu L 2 , 3 edges indicates the presence of pinned Cu 2 + moments that are parallel to the sample surface, and additionally, isotropic paramagnetic moments. The pinning of some of the Cu moments is caused by an AFM ordering consisting of moments that lie most likely in plane of the ﬁlm. Moreover, pinned moments show a larger orbital magnetic moment contribution with an approximate ratio of m orb / m spin = 1 . 5, indicating that these spins are located at sites with reduced symmetry. Some fractions of the pinned moments remain pinned from an AFM background even at 360 K, indicating that T N > 360 K. We propose a simple model that explains these experimental ﬁndings, showing that the magnetic order of the T -CuO ultrathin ﬁlms differs from the theoretical predictions.
Hierarchical Ranking for Answer Selection

Answer selection is a task to choose the positive answers from a pool of candidate answers for a given question. In this paper, we propose a novel strategy for answer selection, called hierarchical ranking. We introduce three levels of ranking: point-level ranking, pair-level ranking, and list-level ranking. They formulate their optimization objectives by employing supervisory information from different perspectives to achieve the same goal of ranking candidate answers. Therefore, the three levels of ranking are related and they can promote each other. We take the well-performed compare-aggregate model as the backbone and explore three schemes to implement the idea of applying the hierarchical rankings jointly: the scheme under the Multi-Task Learning (MTL) strategy, the Ranking Integration (RI) scheme, and the Progressive Ranking Integration (PRI) scheme. Experimental results on two public datasets, WikiQA and TREC-QA, demonstrate that the proposed hierarchical ranking is effective. Our method achieves state-of-the-art (non-BERT) performance on both TREC-QA and WikiQA.
End-to-End Language Identification using Multi-Head Self-Attention and 1D Convolutional Neural Networks

In this work, we propose a new approach for language identification using multi-head self-attention combined with raw waveform based 1D convolutional neural networks for Indian languages. Our approach uses an encoder, multi-head selfattention, and a statistics pooling layer. The encoder learns features directly from raw waveforms using 1D convolution kernels and an LSTM layer. The LSTM layer captures temporal information between the features extracted by the 1D convolutional layer. The multi-head self-attention layer takes outputs of the LSTM layer and applies self-attention mechanisms on these features with M different heads. This process helps the model give more weightage to the more useful features and less weightage to the less relevant features. Finally, the frame-level features are combined using a statistics pooling layer to extract the utterance-level feature vector label prediction. We conduct all our experiments on the 373 hrs of audio data for eight different Indian languages. Our experiments show that our approach outperforms the baseline model by an absolute 3.69% improvement in F1-score and achieves the best F1-score of 95.90%. Our approach also shows that using raw waveform models gets a 1.7% improvement in performance compared to the models built using handcrafted features.
Melon Playlist Dataset: A Public Dataset for Audio-Based Playlist Generation and Music Tagging

One of the main limitations in the field of audio signal processing is the lack of large public datasets with audio representations and high-quality annotations due to restrictions of copyrighted commercial music. We present Melon Playlist Dataset, a public dataset of mel-spectrograms for 649,091 tracks and 148,826 associated playlists annotated by 30,652 different tags. All the data is gathered from Melon, a popular Korean streaming service. The dataset is suitable for music information retrieval tasks, in particular, auto-tagging and automatic playlist continuation. Even though the latter can be addressed by collaborative filtering approaches, audio provides opportunities for research on track suggestions and building systems resistant to the cold-start problem, for which we provide a baseline. Moreover, the playlists and the annotations included in the Melon Playlist Dataset make it suitable for metric learning and representation learning.
Long-term, orbital, and rapid variations of the Be star V923 Aql = HD 183656

We present the latest results of a long-term observational project aimed at observing, collecting from the literature, and homogenising the light, colour, and spectral variations of the well-known emission-line Be star V923 Aql. Our analysis of these parameters confirms that all of the observables exhibit cyclic changes with variable cycle length between about 1800 and 3000 days, so far documented for seven consecutive cycles. We show that these variations can be qualitatively understood within the framework of the model of one-armed oscillation of the circumstellar disk, with a wave of increased density and prograde revolution in space. We confirm the binary nature of the object with a 214.716 day period and estimate the probable system properties. We also confirm the presence of rapid light, and likely also spectral changes. However, we cannot provide any firm conclusions regarding their nature. A quantitative modelling study of long-term changes is planned as a follow-up to this work.
Automatically Identifying Parameter Constraints in Complex Web APIs: A Case Study at Adyen

Web APIs may have constraints on parameters, such that not all parameters are either always required or always optional. Moreover, the presence or value of one parameter could cause another parameter to be required, or parameters could have restrictions on what kinds of values are valid. Having a clear overview of the constraints helps API consumers to integrate without the need for additional support and with fewer integration faults. We made use of existing documentation and code analysis approaches for identifying parameter constraints in complex web APIs. In this paper, we report our case study of several APIs at Adyen, a large-scale payment company that offers complex Web APIs to its customers. Our results show that the documentation-and code-based approach can identify 23% and 53% of the constraints respectively and, when combined, 68% of them. We also reflect on the current challenges that these approaches face. In particular, the absence of information that explicitly describes the constraints in the documentation (in the documentation analysis), and the engineering of a sound static code analyser that is sensitive to data-flow, maintains longer parameter references throughout the API's code, and that is able to symbolically execute the several libraries and frameworks used by the API (in the static analysis).
Transfer learning approach for detecting psychological distress in brexit tweets

In 2016, United Kingdom (UK) citizens voted to leave the European Union (EU), which was officially implemented in 2020. During this period, UK residents experienced a great deal of uncertainty around the UK's continued relationship with the EU. Many people have used social media platforms to express their emotions about this critical event. Sentiment analysis has been recently considered as an important tool for detecting mental well-being in Twitter contents. However, detecting the psychological distress status in political related tweets is a challenging task due to the lack of explicit sentences describing the depressive or anxiety status. To address this problem, this paper leverages a transfer learning approach for sentiment analysis to measure the non-clinical psychological distress status in Brexit tweets. The framework transfers the knowledge learnt from self-reported psychological distress tweets (source domain) to detect the distress status in Brexit tweets (target domain). The framework applies a domain adaptation technique to decrease the impact of negative transfer between source and target domains. The paper also introduces a Brexit distress index that can be used to detect levels of psychological distress of individuals in Brexit tweets. We design an experiment that includes data from both domains. The proposed model is able to detect the non-clinical psychological distress status in Brexit tweets with an accuracy of 66% and 62% on the source and target domains, respectively.
Resolution enhancement in the recovery of underdrawings via style transfer by generative adversarial deep neural networks

We apply generative adversarial convolutional neural networks to the problem of style transfer to underdrawings and ghost-images in x-rays of fine art paintings with a special focus on enhancing their spatial resolution. We build upon a neural architecture developed for the related problem of synthesizing high-resolution photo-realistic image from semantic label maps. Our neural architecture achieves high resolution through a hierarchy of generators and discriminator sub-networks, working throughout a range of spatial resolutions. This coarse-to-fine generator architecture can increase the effective resolution by a factor of eight in each spatial direction, or an overall increase in number of pixels by a factor of 64. We also show that even just a few examples of human-generated image segmentations can greatly improve—qualitatively and quantitatively—the generated images. We demonstrate our method on works such as Leonardo’s Madonna of the carnation and the underdrawing in his Virgin of the rocks, which pose several special problems in style transfer, including the paucity of representative works from which to learn and transfer style information.
Cygnus A jointly calibrated and imaged via non-convex optimization from VLA data

Radio interferometric (RI) data are noisy undersampled spatial Fourier components of the unknown radio sky affected by direction-dependent antenna gains. Failure to model these antenna gains accurately results in a radio sky estimate with limited fidelity and resolution. The RI inverse problem has been recently addressed via a joint calibration and imaging approach that consists in solving a non-convex minimization task, involving suitable priors for the direction-dependent effects (DDEs), namely temporal and spatial smoothness, and sparsity for the unknown radio map via an ℓ1-norm prior, in the context of realistic RI simulations. Building on these developments, we propose to promote sparsity of the radio map via a log-sum prior, enforcing sparsity more strongly than the ℓ1 norm. The resulting minimization task is addressed via a sequence of non-convex minimization tasks composed of re-weighted ℓ1 image priors, which are solved approximately. We demonstrate the efficiency of the approach on RI observations of the celebrated radio galaxy Cygnus A obtained with the Karl G. Jansky Very Large Array at the frequency bands X, C, and S . More precisely, we showcase that the approach enhances data fidelity significantly while achieving high-resolution high-dynamic range radio maps, confirming the suitability of the priors considered for the unknown DDEs and radio image. As a clear qualitative indication of the high fidelity achieved by the data and the proposed approach, we report the detection of three background sources in the vicinity of Cyg A, at S band.
Study on Compressed Sensing of Action Potential

Compressive sensing (CS) is a signal processing technique that enables sub-Nyquist sampling and near lossless reconstruction of a sparse signal. The technique is particularly appealing for neural signal processing since it avoids the issues relevant to high sampling rate and large data storage. In this project, different CS reconstruction algorithms were tested on raw action potential signals recorded in our lab. Two numerical criteria were set to evaluate the performance of different CS algorithms: Compression Ratio (CR) and Signal-to-Noise Ratio (SNR). In order to do this, individual CS algorithm testing platforms for the EEG data were constructed within MATLAB scheme. The main considerations for the project were the following. 1) Feasibility of the dictionary 2) Tolerance to non-sparsity 3) Applicability of thresholding or interpolation.
A Unified Light Framework for Real-Time Fault Detection of Freight Train Images

Real-time fault detection for freight trains plays a vital role in guaranteeing the security and optimal operation of railway transportation under stringent resource requirements. Despite the promising results for deep-learning-based approaches, the performance of these fault detectors on freight train images is far from satisfactory in both accuracy and efficiency. This article proposes a unified light framework to improve detection accuracy while supporting a real-time operation with a low-resource requirement. We first design a novel lightweight backbone (real-time fault detection network—RFDNet) to improve the accuracy and reduce computational cost. Then, we propose a multiregion proposal network using multiscale feature maps generated from the RFDNet to improve the detection performance. Finally, we present multilevel position-sensitive score maps and region of interest pooling to further improve accuracy with few redundant computations. Extensive experimental results on public benchmark datasets suggest that our RFDNet can significantly improve the performance of the baseline network with higher accuracy and efficiency. Experiments on six fault datasets show that our method is capable of real-time detection at over 38 frames/s and achieves competitive accuracy and lower computation than the state-of-the-art detectors.
An End-To-End-Trainable Iterative Network Architecture for Accelerated Radial Multi-Coil 2D Cine MR Image Reconstruction

PURPOSE Iterative Convolutional Neural Networks (CNNs) which resemble unrolled learned iterative schemes have shown to consistently deliver state-of-the-art results for image reconstruction problems across different imaging modalities. However, because these methods include the forward model in the architecture, their applicability is of-ten restricted to either relatively small reconstruction problems or to problems with operators which are computationally cheap to compute. As a consequence, they have not been applied to dynamic non-Cartesian multi-coil reconstruction problems so far. METHODS In this work, we propose a CNN-architecture for image reconstruction of accelerated 2D radial cine MRI with multiple receiver coils. The network is based on a computationally light CNN-component and a subsequent conjugate gradient (CG) method which can be jointly trained end-to-end using an efficient training strategy. We investigate the proposed training-strategy and compare our method to other well-known reconstruction techniques with learned and non-learned regularization methods. RESULTS Our proposed method outperforms all other methods based on non-learned regularization. Further, it performs similar or better than a CNN-based method employing a 3D U-Net and a method using adaptive dictionary learning. In addition, we empirically demonstrate that even by training the network with only iteration, it is possible to increase the length of the network at test time and further improve the results. CONCLUSIONS End-to-end training allows to highly reduce the number of trainable parameters of and stabilize the reconstruction network. Further, because it is possible to change the length of the network at test time, the need to find a compromise between the complexity of the CNN-block and the number of iterations in each CG-block becomes irrelevant.
Electrotactile Feedback in Virtual Reality For Precise and Accurate Contact Rendering

This paper presents a wearable electrotactile feedback system to enable precise and accurate contact rendering with virtual objects for mid-air interactions. In particular, we propose the use of electrotactile feedback to render the interpenetration distance between the user's finger and the virtual content is touched. Our approach consists of modulating the perceived intensity (frequency and pulse width modulation) of the electrotactile stimuli according to the registered interpenetration distance. In a user study (N=21), we assessed the performance of four different interpenetration feedback approaches: electrotactile-only, visual-only, electrotactile and visual, and no interpenetration feedback. First, the results showed that contact precision and accuracy were significantly improved when using interpenetration feedback. Second, and more interestingly, there were no significant differences between visual and electrotactile feedback when the calibration was optimized and the user was familiarized with electrotactile feedback. Taken together, these results suggest that electrotactile feedback could be an efficient replacement of visual feedback for accurate and precise contact rendering in virtual reality avoiding the need of active visual focus and the rendering of additional visual artefacts.
If you’ve got it, flaunt it: Making the most of fine-grained sentiment annotations

Fine-grained sentiment analysis attempts to extract sentiment holders, targets and polar expressions and resolve the relationship between them, but progress has been hampered by the difficulty of annotation. Targeted sentiment analysis, on the other hand, is a more narrow task, focusing on extracting sentiment targets and classifying their polarity. In this paper, we explore whether incorporating holder and expression information can improve target extraction and classification and perform experiments on eight English datasets. We conclude that jointly predicting target and polarity BIO labels improves target extraction, and that augmenting the input text with gold expressions generally improves targeted polarity classification. This highlights the potential importance of annotating expressions for fine-grained sentiment datasets. At the same time, our results show that performance of current models for predicting polar expressions is poor, hampering the benefit of this information in practice.
Can You Accept LaTeX Files from Strangers? Ten Years Later

It is well-known that Microsoft Word/Excel compatible documents or PDF files can contain malicious content. LaTeX files are unfortunately no exception either. LaTeX users often include third-party codes through sources or packages (.sty or .cls files). But those packages can execute malicious commands on the users' system, in order to capture sensitive information or to perform denial of service attacks. Checkoway et al. [3] were the first to warn LaTeX users of these threats. Collaborative cloud-based LaTeX editors and services compiling LaTeX sources are particularly concerned. In this paper, we have created a LaTeX package that collects system data and hides them inside the PDF file produced by the target. Then, we have measured what can be recovered by hackers using malicious LaTeX file on online services, and which measures those services have enforced to thwart the threats. Services defend themselves using sandbox or commands restrictions. Commands restrictions are more difficult to setup and we found one service (PMLatex) which is too permissive.