Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuroprotective Effect of Intrastriatal Caffeic Acid Phenethyl Ester Treatment in 6-OH Dopamine Model of Parkinson's Disease in Rats

Parkinson's disease (PD) is the second most common neurodegenerative disorder, and the main cause of PD is still not known. Until now, no cure for Parkinson's disease is yet in sight. Caffeic acid phenethyl ester (CAPE) is a polyphenolic component of the propolis, which can be derived from honeybee hive propolis. We aimed to determine the effect of intrastriatal CAPE administration as a neuroprotective agent on 6-hydroxydopamine (6-OHDA)-induced PD model. Adult male Wistar rats weighing 280–320 g were used. The PD model was induced with unilateral intrastriatal 6-OHDA injection. Treatment groups received 20 μmol/5 μL/4 day and 80 μmol/5 μL/4 day CAPE 24 h after 6-OHDA injection. Eight days after 6-OHDA application, behavioral studies (adhesive tape removal test, open-field test, cylinder test, and apomorphine-induced asymmetric rotational behavior) were performed once more to compare the effects of CAPE on behavior tests. Striatal histological verifications, immunohistochemistry, and stereological quantitation were performed. Our results for the first time showed that, besides improving the motor performance, CAPE treatment also prevents 6-OHDA-induced loss of TH-positive neurons. From our results, CAPE may be a promising clinical agent in the treatment of PD.
Angiotensin-converting enzyme 2 alleviates pulmonary artery hypertension through inhibition of focal adhesion kinase expression

Focal adhesion kinase (FAK) is an important therapeutic target in pulmonary artery hypertension (PAH); however, the mechanism of its activation remains unknown. The present study aimed to investigate whether angiotensin-converting enzyme 2 (ACE2) could regulate FAK and alleviate PAH in a rat model of PAH established with a single administration of monocrotaline followed by continuous hypoxia treatment. In the current study, right ventricular pressure, body weight and the right ventricular hypertrophy index were measured, and hematoxylin-eosin staining was performed on lung tissues to determine whether the modeling was successful. Changes in the serum levels of FAK were measured using an ELISA kit to evaluate the association between ACE2 and FAK. The mRNA expression levels of ACE2, FAK, caspase-3 and survivin were determined using reverse transcription-quantitative PCR (RT-qPCR). The protein expression levels of ACE2, phosphorylated FAK/FAK, cleaved caspase-3/pro-caspase-3 and survivin were determined via western blotting. Immunohistochemistry was applied to detect the expression of FAK around the pulmonary arterioles. Apoptosis of smooth muscle cells around pulmonary arterioles was observed by TUNEL staining. After treatment with the ACE2 activator DIZE or inhibitor DX-600, the results demonstrated that ACE2 reduced PAH-induced changes in arteriole morphology compared with the control. It also inhibited FAK expression in serum. WB and RT-qPCR results suggested that ACE2 inhibited the expression of FAK and pathway-related proteins, and promoted caspase-3 expression. Additionally, ACE2 reduced FAK expression around the pulmonary arterioles and promoted smooth muscle cell apoptosis. The results indicated that ACE2 activation inhibited FAK expression, leading to alleviation of the symptoms of PAH.
Word-Level Coreference Resolution

Recent coreference resolution models rely heavily on span representations to find coreference links between word spans. As the number of spans is O(n^2) in the length of text and the number of potential links is O(n^4), various pruning techniques are necessary to make this approach computationally feasible. We propose instead to consider coreference links between individual words rather than word spans and then reconstruct the word spans. This reduces the complexity of the coreference model to O(n^2) and allows it to consider all potential mentions without pruning any of them out. We also demonstrate that, with these changes, SpanBERT for coreference resolution will be significantly outperformed by RoBERTa. While being highly efficient, our model performs competitively with recent coreference resolution systems on the OntoNotes benchmark.
Accounting for Variations in Speech Emotion Recognition with Nonparametric Hierarchical Neural Network

In recent years, deep-learning-based speech emotion recognition models have outperformed classical machine learning models. Previously, neural network designs, such as Multitask Learning, have accounted for variations in emotional expressions due to demographic and contextual factors. However, existing models face a few constraints: 1) they rely on a clear definition of domains (e.g. gender, noise condition, etc.) and the availability of domain labels; 2) they often attempt to learn domain-invariant features while emotion expressions can be domain-specific. In the present study, we propose the Nonparametric Hierarchical Neural Network (NHNN), a lightweight hierarchical neural network model based on Bayesian nonparametric clustering. In comparison to Multitask Learning approaches, the proposed model does not require domain/task labels. In our experiments, the NHNN models generally outperform the models with similar levels of complexity and state-of-the-art models in within-corpus and cross-corpus tests. Through clustering analysis, we show that the NHNN models are able to learn group-specific features and bridge the performance gap between groups.
The MOSDEF survey: the dependence of H α-to-UV SFR ratios on SFR and size at z ∼ 2

We perform an aperture-matched analysis of dust-corrected H α and UV star formation rates (SFRs) using 303 star-forming galaxies with spectroscopic redshifts 1.36 < zspec < 2.66 from the MOSFIRE Deep Evolution Field survey. By combining H α and H β emission line measurements with multiwaveband resolved Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey/3D-HST imaging, we directly compare dust-corrected H α and UV SFRs, inferred assuming a fixed attenuation curve shape and constant SFHs, within the spectroscopic aperture. Previous studies have found that H α and UV SFRs inferred with these assumptions generally agree for typical star-forming galaxies, but become increasingly discrepant for galaxies with higher SFRs (≳100 M⊙ yr−1), with H α-to-UV SFR ratios being larger for these galaxies. Our analysis shows that this trend persists even after carefully accounting for the apertures over which H α and UV-based SFRs (and the nebular and stellar continuum reddening) are derived. Furthermore, our results imply that H α SFRs may be higher in the centres of large galaxies (i.e. where there is coverage by the spectroscopic aperture) compared to their outskirts, which could be indicative of inside-out galaxy growth. Overall, we suggest that the persistent difference between nebular and stellar continuum reddening and high H α-to-UV SFR ratios at the centres of large galaxies may be indicative of a patchier distribution of dust in galaxies with high SFRs.
Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph

In cross-lingual text classification, it is required that task-specific training data in high-resource source languages are available, where the task is identical to that of a low-resource target language. However, collecting such training data can be infeasible because of the labeling cost, task characteristics, and privacy concerns. This paper proposes an alternative solution that uses only task-independent word embeddings of high-resource languages and bilingual dictionaries. First, we construct a dictionary-based heterogeneous graph (DHG) from bilingual dictionaries. This opens the possibility to use graph neural networks for cross-lingual transfer. The remaining challenge is the heterogeneity of DHG because multiple languages are considered. To address this challenge, we propose dictionary-based heterogeneous graph neural network (DHGNet) that effectively handles the heterogeneity of DHG by two-step aggregations, which are word-level and language-level aggregations. Experimental results demonstrate that our method outperforms pretrained models even though it does not access to large corpora. Furthermore, it can perform well even though dictionaries contain many incorrect translations. Its robustness allows the usage of a wider range of dictionaries such as an automatically constructed dictionary and crowdsourced dictionary, which are convenient for real-world applications.
Juvenile state hypothesis: What we can learn from lottery ticket hypothesis researches?

The proposition of lottery ticket hypothesis revealed the relationship between network structure and initialization parameters and the learning potential of neural networks. The original lottery ticket hypothesis performs pruning and weight resetting after training convergence, exposing it to the problem of forgotten learning knowledge and potential high cost of training. Therefore, we propose a strategy that combines the idea of neural network structure search with a pruning algorithm to alleviate this problem. This algorithm searches and extends the network structure on existing winning ticket sub-network to producing new winning ticket recursively. This allows the training and pruning process to continue without compromising performance. A new winning ticket sub-network with deeper network structure, better generalization ability and better test performance can be obtained in this recursive manner. This method can solve: the difficulty of training or performance degradation of the sub-networks after pruning, the forgetting of the weights of the original lottery ticket hypothesis and the difficulty of generating winning ticket sub-network when the final network structure is not given. We validate this strategy on the MNIST and CIFAR-10 datasets. And after relating it to similar biological phenomena and relevant lottery ticket hypothesis studies in recent years, we will further propose a new hypothesis to discuss which factors that can keep a network juvenile, i.e., those possible factors that influence the learning potential or generalization performance of a neural network during training.
Stereo camera calibration analysis for seafloor mapping

In this paper a stereo camera calibration analysis was developed for the MBARI’s Low Altitude Survey System. An image type contribution approach was taken, splitting the input images for the calibration in different groups. The reprojection error and row alignment error were used as measures for the different grouped combinations from calibrations. This procedure allowed the researchers to improve object 3D reconstructions and 2D mosaics generation.
1-Bit MIMO for Terahertz Channels

This paper tackles the problem of single-user multiple-input multiple-output communication with 1-bit digital-to-analog and analog-to-digital converters. With the information-theoretic capacity as benchmark, the complementary strategies of beamforming and equiprobable signaling are contrasted in the regimes of operational interest, and the ensuing spectral efficiencies are characterized. Various canonical channel types are considered, with emphasis on line-of-sight settings under both spherical and planar wavefronts, respectively representative of short and long transmission ranges at mmWave and terahertz frequencies. In all cases, a judicious combination of beamforming and equiprobable signaling is shown to operate within a modest gap from capacity.
A Euclidean perspective on completeness and weak gravity

In this paper we use Euclidean gravity methods to show that charged black holes which are sufficiently close to extremality must be able to decay. The argument proceeds by showing that Euclidean gravity would otherwise imply a violation of charge quantization. As this is the assumption which leads to the weak gravity conjecture, our argument gives a derivation of that conjecture. We use a small negative cosmological constant as an infrared regulator, but our argument applies to near-extremal black holes which are arbitrarily small compared to the AdS curvature scale. We also give a universal formula for the density of black hole microstates which transform in each irreducible representation of any finite gauge group. Since each representation appears with nonzero fraction, this gives a new proof of the completeness hypothesis for finite gauge fields. Based on these observations we make two conjectures about many-body quantum physics: we propose a lower bound on the critical temperature for the instability of a semi-local quantum liquid, and we propose that our formula for the density of black hole microstates in each representation of a finite gauge group also applies at high energy to any quantum field theory with a finite group global symmetry. ar X iv :2 10 9. 03 83 8v 1 [ he pth ] 8 S ep 2 02 1
Versions of Gradient Temporal Difference Learning

—Sutton, Szepesv´ari and Maei introduced the ﬁrst gradient temporal-difference (GTD) learning algorithms compatible with both linear function approximation and off-policy training. The goal of this paper is (a) to propose some variants of GTDs with extensive comparative analysis and (b) to establish new theoretical analysis frameworks for the GTDs. These variants are based on convex-concave saddle-point interpretations of GTDs, which effectively unify all the GTDs into a single framework, and provide simple stability analysis based on recent results on primal-dual gradient dynamics. Finally, numerical comparative analysis is given to evaluate these approaches.
Leveraging Code Clones and Natural Language Processing for Log Statement Prediction

Software developers embed logging statements inside the source code as an imperative duty in modern software development as log files are necessary for tracking down runtime system issues and troubleshooting system management tasks. Prior research has emphasized the importance of logging statements in the operation and debugging of software systems. However, the current logging process is mostly manual and ad hoc, and thus, proper placement and content of logging statements remain as challenges. To overcome these challenges, methods that aim to automate log placement and log content, i.e., ‘where, what, and how to log’, are of high interest. Thus, we propose to accomplish the goal of this research, that is “to predict the log statements by utilizing source code clones and natural language processing (NLP)”, as these approaches provide additional context and advantage for log prediction. We pursue the following four research objectives: (RO1) investigate whether source code clones can be leveraged for log statement location prediction, (RO2) propose a clone-based approach for log statement prediction, (RO3) predict log statement’s description with code-clone and NLP models, and (RO4) examine approaches to automatically predict additional details of the log statement, such as its verbosity level and variables. For this purpose, we perform an experimental analysis on seven open-source java projects, extract their method-level code clones, investigate their attributes, and utilize them for log location and description prediction. Our work demonstrates the effectiveness of log-aware clone detection for automated log location and description prediction and outperforms the prior work.
Learning Opinion Summarizers by Selecting Informative Reviews

Opinion summarization has been traditionally approached with unsupervised, weakly-supervised and few-shot learning techniques. In this work, we collect a large dataset of summaries paired with user reviews for over 31,000 products, enabling supervised training. However, the number of reviews per product is large (320 on average), making summarization – and especially training a summarizer – impractical. Moreover, the content of many reviews is not reflected in the human-written summaries, and, thus, the summarizer trained on random review subsets hallucinates. In order to deal with both of these challenges, we formulate the task as jointly learning to select informative subsets of reviews and summarizing the opinions expressed in these subsets. The choice of the review subset is treated as a latent variable, predicted by a small and simple selector. The subset is then fed into a more powerful summarizer. For joint training, we use amortized variational inference and policy gradient methods. Our experiments demonstrate the importance of selecting informative reviews resulting in improved quality of summaries and reduced hallucinations.
Reachable sets for two-level open quantum systems driven by coherent and incoherent controls

In this work, we study controllability in the set of all density matrices for a two-level open quantum system driven by coherent and incoherent controls. In Pechen (2011 Phys. Rev. A 84 042106) an approximate controllability, i.e. controllability with some precision, was shown for generic N-level open quantum systems driven by coherent and incoherent controls. However, the explicit formulation of this property, including the behavior of this precision as a function of transition frequencies and decoherence rates of the system, was not known. The present work provides a rigorous analytical study of reachable sets for two-level open quantum systems. First, it is shown that for N = 2 the presence of incoherent control does not affect the reachable set (while incoherent control may affect the time necessary to reach particular state). Second, the reachable set in the Bloch ball is described and it is shown that already just for one coherent control any point in the Bloch ball can be achieved with precision δ ∼ γ/ω, where γ is the decoherence rate and ω is the transition frequency. Typical values are δ ≲ 10−3 that implies high accuracy of achieving any density matrix. Moreover, we show that most points in the Bloch ball can be exactly reached, except of two lacunae of size ∼δ. For two coherent controls, the system is shown to be completely controllable in the set of all density matrices. Third, the reachable set as a function of the final time is found and shown to exhibit a non-trivial structure.
VISUAL AND AUDITORY CUES OF ASSERTIONS AND QUESTIONS IN BRAZILIAN PORTUGUESE AND MEXICAN SPANISH: A COMPARATIVE STUDY

The aim of this paper is to compare the multimodal production of assertions and questions in two different languages: Brazilian Portuguese and Mexican Spanish. Descriptions of the auditory and visual cues of these speech acts are presented based on Brazilian and Mexican corpora. The sentence “Como você sabe” was produced as an assertion and an echo question by ten speakers (five male) from Rio de Janeiro and the sentence “Apaga la tele” was produced as an assertion and a yes-no question by five speakers (three male) from Mexico City. The speech acts intonational patterns were described in terms of F0 movements and annotated in the nuclear region of the contours with ToBI system. Momentary facial muscular changes (namely Action Units) located in the upper and lower part of the face as well as head movements were used to analyze the facial expressions. The acoustic description showed that Brazilian Portuguese assertions are produced with a falling F0 nuclear configuration (H+L*L%) and echo questions with a rising F0 nuclear configuration (L+<H*L%). Mexican Spanish assertions present two types of F0 nuclear configurations, either a low flat nuclear F0 (L*L%) or a falling-rising (L+H*L%) nuclear F0, whereas Mexican Spanish yes-no questions are produced with a low nuclear F0 followed by a rising boundary tone (L*LH%). The outcome of the visual analysis indicates that, whereas Brazilian Portuguese assertions are visually produced with blink and right head tilt and Mexican Spanish assertions with lip stretcher, lowering the eyebrows, tightening the eyelid and wrinkling the nose can be considered question markers in both language varieties.
Charge fluctuation entropy of Hawking radiation: A replica-free way to find large entropy

We study the fluctuation entropy for two-dimensional matter systems with an internal symmetry coupled to Jackiw-Teitelboim (JT) gravity joined to a Minkowski region. The fluctuation entropy is the Shannon entropy associated to probabilities of finding particular charge for a region. We first consider a case where the matter has a global symmetry. We find that the fluctuation entropy of Hawking radiation shows an unbounded growth and exceeds the entanglement entropy in presence of islands. This indicates that the global symmetry is violated. We then discuss the fluctuation entropy for matter coupled to a two-dimensional gauge field. We find a lower bound on the gauge coupling g_0g0 in order to avoid a similar issue. Also, we point out a few puzzles related to the island prescription in presence of a gauge symmetry.
Gold nanoparticles in virus detection: Recent advances and potential considerations for SARS‐CoV‐2 testing development

Abstract Viruses are infectious agents that pose significant threats to plants, animals, and humans. The current coronavirus disease 2019 pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), has spread globally and resulted in over 2 million deaths and immeasurable financial losses. Rapid and sensitive virus diagnostics become crucially important in controlling the spread of a pandemic before effective treatment and vaccines are available. Gold nanoparticle (AuNP)‐based testing holds great potential for this urgent unmet biomedical need. In this review, we describe the most recent advances in AuNP‐based viral detection applications. In addition, we discuss considerations for the design of AuNP‐based SARS‐CoV‐2 testings. Finally, we highlight and propose important parameters to consider for the future development of effective AuNP‐based testings that would be critical for not only this COVID‐19 pandemic, but also potential future outbreaks. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > In Vitro Nanoparticle‐Based Sensing
NU:BRIEF – A Privacy-aware Newsletter Personalization Engine for Publishers

Newsletters have (re-) emerged as a powerful tool for publishers to engage with their readers directly and more effectively. Despite the diversity in their audiences, publishers’ newsletters remain largely a one-size-fits-all offering, which is suboptimal. In this paper, we present NU:BRIEF, a web application for publishers that enables them to personalize their newsletters without harvesting personal data. Personalized newsletters build a habit and become a great conversion tool for publishers, providing an alternative readers-generated revenue model to a declining ad/clickbait-centered business model. Demo: https://demo.nubrief. com/md03PaAJSwXMegL5BbKpQlArK3elb3hDUglcHodx4gE=/ Explainer video: https://www.youtube. com/watch? v=AUZGuyPJYH4
IFBiD: Inference-Free Bias Detection

This paper is the first to explore an automatic way to detect bias in deep convolutional neural networks by simply looking at their weights. Furthermore, it is also a step towards understanding neural networks and how they work. We show that it is indeed possible to know if a model is biased or not simply by looking at its weights, without the model inference for an specific input. We analyze how bias is encoded in the weights of deep networks through a toy example using the Colored MNIST database and we also provide a realistic case study in gender detection from face images using state-of-the-art methods and experimental resources. To do so, we generated two databases with 36K and 48K biased models each. In the MNIST models we were able to detect whether they presented a strong or low bias with more than 99% accuracy, and we were also able to classify between four levels of bias with more than 70% accuracy. For the face models, we achieved 90% accuracy in distinguishing between models biased towards Asian, Black, or Caucasian ethnicity.