Miracle-dz/UniFAR-FTUs huggingface dataset:
Putting a Spin on Language: A Quantum Interpretation of Unary Connectives for Linguistic Applications

Extended versions of the Lambek Calculus currently used in computational linguistics rely on unary modalities to allow for the controlled application of structural rules affecting word order and phrase structure. These controlled structural operations give rise to derivational ambiguities that are missed by the original Lambek Calculus or its pregroup simplification. Proposals for compositional interpretation of extended Lambek Calculus in the compact closed category of FVect and linear maps have been made, but in these proposals the syntax-semantics mapping ignores the control modalities, effectively restricting their role to the syntax. Our aim is to turn the modalities into first-class citizens of the vectorial interpretation. Building on the density matrix semantics of (Correia et al, 2019), we extend the interpretation of the type system with an extra spin density matrix space. The interpretation of proofs then results in ambiguous derivations being tensored with orthogonal spin states. Our method introduces a way of simultaneously representing co-existing interpretations of ambiguous utterances, and provides a uniform framework for the integration of lexical and derivational ambiguity.
Exploiting Redundancy in Pre-trained Language Models for Efficient Transfer Learning

Large pre-trained contextual word representations have transformed the field of natural language processing, obtaining impressive results on a wide range of tasks. However, as models increase in size, computational limitations make them impractical for researchers and practitioners alike. We hypothesize that contextual representations have both intrinsic and task-specific redundancies. We propose a novel feature selection method, which takes advantage of these redundancies to reduce the size of the pre-trained features. In a comprehensive evaluation on two pre-trained models, BERT and XLNet, using a diverse suite of sequence labeling and sequence classification tasks, our method reduces the feature set down to 1--7% of the original size, while maintaining more than 97% of the performance.
Time accelerated image super-resolution using shallow residual feature representative network

The recent advances in deep learning indicate significant progress in the field of single image super-resolution. With the advent of these techniques, high-resolution image with high peak signal to noise ratio (PSNR) and excellent perceptual quality can be reconstructed. The major challenges associated with existing deep convolutional neural networks are their computational complexity and time; the increasing depth of the networks, often result in high space complexity. To alleviate these issues, we developed an innovative shallow residual feature representative network (SRFRN) that uses a bicubic interpolated low-resolution image as input and residual representative units (RFR) which include serially stacked residual non-linear convolutions. Furthermore, the reconstruction of the high-resolution image is done by combining the output of the RFR units and the residual output from the bicubic interpolated LR image. Finally, multiple experiments have been performed on the benchmark datasets and the proposed model illustrates superior performance for higher scales. Besides, this model also exhibits faster execution time compared to all the existing approaches.
Comparison for Improvements of Singing Voice Detection System Based on Vocal Separation

Singing voice detection is the task to identify the frames which contain the singer vocal or not. It has been one of the main components in music information retrieval (MIR), which can be applicable to melody extraction, artist recognition, and music discovery in popular music. Although there are several methods which have been proposed, a more robust and more complete system is desired to improve the detection performance. In this paper, our motivation is to provide an extensive comparison in different stages of singing voice detection. Based on the analysis a novel method was proposed to build a more efficiently singing voice detection system. In the proposed system, there are main three parts. The first is a pre-process of singing voice separation to extract the vocal without the music. The improvements of several singing voice separation methods were compared to decide the best one which is integrated to singing voice detection system. And the second is a deep neural network based classifier to identify the given frames. Different deep models for classification were also compared. The last one is a post-process to filter out the anomaly frame on the prediction result of the classifier. The median filter and Hidden Markov Model (HMM) based filter as the post process were compared. Through the step by step module extension, the different methods were compared and analyzed. Finally, classification performance on two public datasets indicates that the proposed approach which based on the Long-term Recurrent Convolutional Networks (LRCN) model is a promising alternative.
Nematicity and competing orders in superconducting magic-angle graphene

Twisted and nematic Electrons in quantum materials can break rotational symmetry even when the underlying crystal lattice does not. This phenomenon, called nematicity, has been observed in many unconventional superconductors. Cao et al. found that magic-angle twisted bilayer graphene, in which superconductivity was recently discovered, also exhibits nematicity. The breaking of rotational symmetry was observed through transport measurements, which exhibited characteristic anisotropy. Science, this issue p. 264 Transport measurements indicate the breaking of rotational symmetry. Strongly interacting electrons in solid-state systems often display multiple broken symmetries in the ground state. The interplay between different order parameters can give rise to a rich phase diagram. We report on the identification of intertwined phases with broken rotational symmetry in magic-angle twisted bilayer graphene (TBG). Using transverse resistance measurements, we find a strongly anisotropic phase located in a “wedge” above the underdoped region of the superconducting dome. Upon its crossing with the superconducting dome, a reduction of the critical temperature is observed. Furthermore, the superconducting state exhibits an anisotropic response to a direction-dependent in-plane magnetic field, revealing nematic ordering across the entire superconducting dome. These results indicate that nematic fluctuations might play an important role in the low-temperature phases of magic-angle TBG.
Drone-aided Localization in LoRa IoT Networks

Besides being part of the Internet of Things (IoT), drones can play a relevant role in it as enablers. The 3D mobility of UAVs can be exploited to improve node localization in IoT networks for, e.g., search and rescue or goods localization and tracking. One of the widespread IoT communication technologies is Long Range Wide Area Network (LoRaWAN), which allows achieving long communication distances with low power. In this work, we present a drone-aided localization system for LoRa networks in which a UAV is used to improve the estimation of a node's location initially provided by the network. We characterize the relevant parameters of the communication system and use them to develop and test a search algorithm in a realistic simulated scenario. We then move to the full implementation of a real system in which a drone is seamlessly integrated into Swisscom's LoRa network. The drone coordinates with the network with a two-way exchange of information which results in an accurate and fully autonomous localization system. The results obtained in our field tests show a ten-fold improvement in localization precision with respect to the estimation provided by the fixed network. Up to our knowledge, this is the first time a UAV is successfully integrated in a LoRa network to improve its localization accuracy.
Probabilistic Spatial Transformers for Bayesian Data Augmentation

High-capacity models require vast amounts of data, and data augmentation is a common remedy when this resource is limited. Standard augmentation techniques apply small hand-tuned transformations to existing data, which is a brittle process that realistically only allows for simple transformations. We propose a Bayesian interpretation of data augmentation where the transformations are modelled as latent variables to be marginalized, and show how these can be inferred variationally in an end-to-end fashion. This allows for significantly more complex transformations than manual tuning, and the marginalization implies a form of test-time data augmentation. The resulting model can be interpreted as a probabilistic extension of spatial transformer networks. Experimentally, we demonstrate improvements in accuracy and uncertainty quantification in image and time series classification tasks.
Modelling a bright z = 6 galaxy at the faint end of the AGN luminosity function

Recent deep surveys have unravelled a population of faint active galactic nuclei (AGN) in the high redshift Universe, leading to various discussions on their nature and their role during the Epoch of Reionization. We use cosmological radiation-hydrodynamics simulations of a bright galaxy at z = 6 ($M_\star \gtrsim 10^{10} M_\odot$) hosting an actively growing super-massive black hole to study the properties of these objects. In particular, we study how the black hole and the galaxy co-evolve and what is the relative contribution of the AGN and of the stellar populations to the luminosity budget of the system. We find that the feedback from the AGN has no strong effect on the properties of the galaxy, and does not increase the total ionizing luminosity of the host. The average escape fraction of our galaxy is around $f_{\rm esc} \sim 5\%$. While our galaxy would be selected as an AGN in deep X-ray surveys, most of the UV luminosity is originating from stellar populations. This confirms that there is a transition in the galaxy population from star forming galaxies to quasar hosts, with bright Lyman-Break Galaxies (LBGs) with $M_{\rm UV}$ around -22 falling in the overlap region. Our results also suggest that faint AGN do not contribute significantly to reionizing the Universe.
Response of four zucchini (Cucurbita pepo L.) hybrids to different arbuscular mycorrhizal fungi.

Four zucchinihybrids (FocusF1,Comet F1,NataliF1, andEzraF1)inoculated by three different arbuscular mycorrhizal fungi (AMF) ( Glomus intraradices (G i),Glomus etinucatum(Ge),andGigasporamargarita (Gm)) in a growth chamber experimentwere evaluated for seedling traits, nutrientuptake,colonization,and relative mycorrhizal dependency (RMD) . Relative mycorrhizal dependency ranged widely among fourzucchinihybrids.Gm inoculations had higher positive RMDs, whileGiinoculations had lower negative RMDs. Gm -inoculated Focus F 1had the highest RMD (30.22 %). Arbuscular mycorrhizal fungi inoculated seedlings had wider cotyledons and stems. There were al so significant effects of AMF inoculation on the most of nutrients.
Emotional Voice Conversion With Cycle-consistent Adversarial Network

Emotional Voice Conversion, or emotional VC, is a technique of converting speech from one emotion state into another one, keeping the basic linguistic information and speaker identity. Previous approaches for emotional VC need parallel data and use dynamic time warping (DTW) method to temporally align the source-target speech parameters. These approaches often define a minimum generation loss as the objective function, such as L1 or L2 loss, to learn model parameters. Recently, cycle-consistent generative adversarial networks (CycleGAN) have been used successfully for non-parallel VC. This paper investigates the efficacy of using CycleGAN for emotional VC tasks. Rather than attempting to learn a mapping between parallel training data using a frame-to-frame minimum generation loss, the CycleGAN uses two discriminators and one classifier to guide the learning process, where the discriminators aim to differentiate between the natural and converted speech and the classifier aims to classify the underlying emotion from the natural and converted speech. The training process of the CycleGAN models randomly pairs source-target speech parameters, without any temporal alignment operation. The objective and subjective evaluation results confirm the effectiveness of using CycleGAN models for emotional VC. The non-parallel training for a CycleGAN indicates its potential for non-parallel emotional VC.
The Franzese-Stanely Coarse Grained Model for Hydration Water

Water modeling is a challenging problem. Its anomalies are difficult to reproduce, promoting the proliferation of a large number of computational models, among which researchers select the most appropriate for the property they study. In this chapter, we introduce a coarse-grained model introduced by Franzese and Stanley (FS) that accounts for the many-body interactions of water. We review mean-field calculations and Monte Carlo simulations on water monolayers for a wide range of pressures and temperatures, including extreme conditions. The results show the presence of two dynamic crossovers and explain the origin of diffusion anomalies. Moreover, the model shows that all the different scenarios, proposed in the last decades as alternative explanations of the experimental anomalies of water, can be related by the fine-tuning of the many-body (cooperative) interaction. Once this parameter is set from the experiments, the FS model predicts a phase transition between two liquids with different densities and energies in the supercooled water region, ending in a liquid-liquid critical point. From this critical point stems a liquid-liquid Widom line, i.e., the locus of maxima of the water correlation length, that in the FS model can be directly calculated. The results are consistent with the extrapolations from experiments. Furthermore, they agree with those from atomistic models but make predictions over a much wider thermodynamic region, allowing for a better interpretation of the available experimental data. All these findings provide a coherent picture of the properties of water and confirm the validity of the FS model that has proved to be useful for large-scale simulations of biological systems.
Explicit Reordering for Neural Machine Translation

In Transformer-based neural machine translation (NMT), the positional encoding mechanism helps the self-attention networks to learn the source representation with order dependency, which makes the Transformer-based NMT achieve state-of-the-art results for various translation tasks. However, Transformer-based NMT only adds representations of positions sequentially to word vectors in the input sentence and does not explicitly consider reordering information in this sentence. In this paper, we first empirically investigate the relationship between source reordering information and translation performance. The empirical findings show that the source input with the target order learned from the bilingual parallel dataset can substantially improve translation performance. Thus, we propose a novel reordering method to explicitly model this reordering information for the Transformer-based NMT. The empirical results on the WMT14 English-to-German, WAT ASPEC Japanese-to-English, and WMT17 Chinese-to-English translation tasks show the effectiveness of the proposed approach.
Image super-resolution reconstruction based on attention mechanism and feature fusion

Aiming at the problems that the convolutional neural networks neglect to capture the inherent attributes of natural images and extract features only in a single scale in the field of image super-resolution reconstruction, a network structure based on attention mechanism and multi-scale feature fusion is proposed. By using the attention mechanism, the network can effectively integrate the non-local information and second-order features of the image, so as to improve the feature expression ability of the network. At the same time, the convolution kernel of different scales is used to extract the multi-scale information of the image, so as to preserve the complete information characteristics at different scales. Experimental results show that the proposed method can achieve better performance over other representative super-resolution reconstruction algorithms in objective quantitative metrics and visual quality.
A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants

Early quantification of Tuta absoluta pest's effects in tomato plants is a very important factor in controlling and preventing serious damages of the pest. The invasion of Tuta absoluta is considered a major threat to tomato production causing heavy loss ranging from 80 to 100 percent when not properly managed. Therefore, real-time and early quantification of tomato leaf miner Tuta absoluta, can play an important role in addressing the issue of pest management and enhance farmers' decisions. In this study, we propose a Convolutional Neural Network (CNN) approach in determining the effects of Tuta absoluta in tomato plants. Four CNN pre-trained architectures (VGG16, VGG19, ResNet and Inception-V3) were used in training classifiers on a dataset containing health and infested tomato leaves collected from real field experiments. Among the pre-trained architectures, experimental results showed that Inception-V3 yielded the best results with an average accuracy of 87.2 percent in estimating the severity status of Tuta absoluta in tomato plants. The pre-trained models could also easily identify High Tuta severity status compared to other severity status (Low tuta and No tuta)
Which one is the dax? Achieving mutual exclusivity with neural networks

Learning words is a challenge for children and neural networks alike. However, what they struggle with can differ. When prompted by novel words, children have been shown to tend to associate them with unfamiliar referents. This has been taken to reflect a propensity toward mutual exclusivity. In this study, we investigate whether and under which circumstances neural models can exhibit analogous behavior. To this end, we evaluate cross-situational neural models on novel items with distractors, contrasting the interaction between different word learning and referent selection strategies. We find that, as long as they bring about competition between words, constraints in both learning and referent selection can improve success in tasks with novel words and referents. For neural network research, our findings clarify the role of available options for enhanced performance in tasks where mutual exclusivity is advantageous. For cognitive research, they highlight latent interactions between word learning, referent selection mechanisms, and the structure of stimuli.
Introductory Topological Data Analysis

This paper introduces topological data analysis. Starting from notions of a metric space and some elementary graph theory, we take example sets of data and demonstrate some of their topological properties. We discuss simplicial complexes and how they relate to something called the Nerve Theorem. For this we introduce notions from the field of topology such as open covers, homeomorphism and homotopy equivalences. This leads us into discussing filtering data and deriving topologically invariant simplicial complexes from the underlying data set. There is then a small introduction to persistent homology and Betti numbers as these are useful analytical tools for TDA. An accompanying online appendix for the code producing the bulk of the figures in this paper is available at this http URL.
Efficacy of adjunctive treatment with trimetazidine for fragmented QRS among patients with ischemic heart failure: a propensity score match analysis

Objectives: Fragmented QRS (fQRS) is an indicator of nonhomogeneous ventricular activity caused by myocardial ischemia and fibrosis. The anti-ischemic agent trimetazidine (TMZ) added to pharmacological treatment appears to have positive effects on cardiac parameters of patients with ischemic heart failure. We aimed to investigate the relationship of fQRS with adjunctive TMZ therapy in ischemic HF patients. Methods: Four hundred eighty-nine consecutive ambulatory ischemic patients with heart failure eligible for our study were recruited for the study. A 12-lead electrocardiogram with standard chest and limb leads was used to evaluate the presence of fQRS. Further patients were divided into groups according to adjunctive TMZ treatment and fQRS presence. Confounding factors were adjusted by propensity score matching and multivariate logistic regression analysis. Results: One hundred ninety-seven (40.3%) patients had fQRS on their ECGs and 235 (48.1%) patients were on adjunctive treatment with trimetazidine. Compared to patients without fQRS, patients with fQRS had lower left ventricular ejection fraction (LVEF), higher NYHA classes and more frequent mineralocorticoid receptor antagonist and diuretic usage ( p < 0.05). Add-on treatment with TMZ was independently associated with fQRS presence (OR, 0.45; (95% CI, 0.29-0.70); p < 0.0001). Conclusion: According to conventional therapy, adjunctive treatment with TMZ among ischemic heart failure patients can be associated with fQRS in 12-lead ECG independent of LVEF.
Mid-infrared variability of γ-ray emitting blazars

Using data from the Wide-field Infrared Survey Explorer we studied the mid infrared 3.4 $\mu$m (W1-band) and 4.6 $\mu$m (W2-band) flux variability of $\gamma$-ray emitting blazars. Our sample consists of 460 flat spectrum radio quasars (FSRQs) and 575 BL Lacertae (BL Lac) objects. On intra-day timescales, the median amplitude of variability ($\sigma_{m}$) for FSRQs is 0.04$^{+0.03}_{-0.02}$ mag and 0.05$^{+0.03}_{-0.02}$ mag in W1 and W2 bands. For BL Lacs we found median $\sigma_{m}$ in W1(W2) bands of 0.04$^{+0.01}_{-0.02}$ (0.04$^{+0.02}_{-0.02}$) mag. On long timescales, for FSRQs we found a median $\sigma_{m}$ of 0.44$^{+0.28}_{-0.27}$ mag and 0.45$^{+0.27}_{-0.27}$ mag in W1 and W2 bands, while for BL Lacs the median values are 0.21$^{+0.18}_{-0.12}$ mag and 0.22$^{+0.18}_{-0.11}$ mag in W1 and W2 bands. From statistical tests, we found FSRQs to show larger $\sigma_m$ than BL Lacs on both intra-day and long timescales. Among blazars, low synchrotron peaked (LSP) sources showed larger $\sigma_m$ compared to intermediate synchrotron peaked (ISP) and high synchrotron peaked (HSP) sources.The larger $\sigma_{m}$ seen in FSRQs relative to BL Lacs on both intra-day and long timescales could be due to them having the most powerful relativistic jets and/or their mid infrared band coinciding with the peak of the electron energy distribution. BL Lacs have low power jets and the observational window too traces the emission from low energy electrons, thereby leading to low $\sigma_{m}$. In both FSRQs and BL Lacs predominantly a bluer when brighter behaviour was observed. No correlation is found between $\sigma_m$ and black hole mass.