Miracle-dz/UniFAR-FTUs huggingface dataset:
The Geology and Petroleum Prospects of the Tuz Gölü Basin

Tuz golu havzasi, caprazvari yapisal bir depresyon icinde yer alan NW-SE yonlu karalararasi bir havzadir. Kuzeyinde Ankara yukselimi, dogusunda Kirsehir masifi, guneyinde Toros daglari ve batisinda Sivrihisar-Bozdag masifi ile cevrelenmistir (Levha II). Petrolun jeolojik temeli, genellikle ofiyolitlerden, bunlarin asindigi yerlerde Mesozoyik kirectaslarindan veya Paleozoyik metamorfiklerinden olusmustur. Tuz golu havzasi Ust Senoniyen-Oligosen suresince gelismistir. Tam bir Sedimenter sikli temsil eden 10 000 metreden fazla kalinliktaki sedimentler havzanin en derin yerinde birikmistir. Subsidans Ust Senoniyen-Alt Orta Eosen suresince meydana gelmis ve bunu Ust-Orta Eosende baslayan ve Oligosen sonlarina kadar devam eden regresyon takip etmistir. Ust Senoniyen-Alt Orta Eosen suresince Tuz golu havzasi Haymana mintikasiyle kuzeybatiya dogru tek ve devamli bir depresyon olusturmustur. Orta Eosen Nummulitik kirectaslarinin depolanmasindan sonra Tuz golu havzasi, yukselen Haymana havzasindan Karacadag yukseliminin dogu kenari boyunca uzanan bir fay zonu ile ayrilmistir. Kuzey-kuzeydoguya dogru Cankiri havzasiyle baglantisi Paleosen devrinde meydana gelmis ve Tuz golu havzasinin NW-SE yonlu fay zonlariyle sinirlanmis bir graben halini aldigi Orta Eosen-Oligosen boyunca devam etmistir. Oligosen sonlarinda veya Miyosen doneminde meydana gelen esas deformasyondan sonra, Neojen esnasinda yer yer depolanma havzalari olusmus ve bu havzalarda degisik kalinlikta volkaniklerle gol kirectaslan dahil karasal sedimentler birikmistir. Tuz golu havzasi Pliyosendeki son Alpin kompresyonel hareketlerden hafif etkilenmistir. Neojende meydana gelen ve Pleyistosene kadar devam eden tansiyonel hareketler tarihi devirlere kadar uzanan volkanik faaliyetlere sebep olmuslardir.
Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram Signal Classification

An essential part for the accurate classification of electrocardiogram (ECG) signals is the extraction of informative yet general features, which are able to discriminate diseases. Cardiovascular abnormalities manifest themselves in features on different time scales: small scale morphological features, such as missing P-waves, as well as rhythmical features apparent on heart rate scales. For this reason we incorporate a variant of the complex wavelet transform, called a scatter transform, in a deep residual neural network (ResNet). The former has the advantage of being derived from theory, making it well behaved under certain transformations of the input. The latter has proven useful in ECG classification, allowing feature extraction and classification to be learned in an end-to-end manner. Through the incorporation of trainable layers in between scatter transforms, the model gains the ability to combine information from different channels, yielding more informative features for the classification task and adapting them to the specific domain. For evaluation, we submitted our model in the official phase in the PhysioNet/Computing in Cardiology Challenge 2020. Our (Team Triage) approach achieved a challenge validation score of 0.640, and full test score of 0.485, placing us 4th out of 41 in the official ranking.
Circular Chromatic Number of Signed Graphs

A signed graph is a pair $(G, \sigma)$, where $G$ is a graph (loops and multi edges allowed) and $\sigma: E(G) \to \{+, -\}$ is a signature which assigns to each edge of $G$ a sign. Various notions of coloring of signed graphs have been studied. In this paper, we extend circular coloring of graphs to signed graphs. Given a signed graph $(G, \sigma)$ with no positive loop, a circular $r$-coloring of $(G, \sigma)$ is an assignment $\psi$ of points of a circle of circumference $r$ to the vertices of $G$ such that for every edge $e=uv$ of $G$, if $\sigma(e)=+$, then $\psi(u)$ and $\psi(v)$ have distance at least $1$, and if $\sigma(e)=-$, then $\psi(v)$ and the antipodal of $\psi(u)$ have distance at least $1$. The circular chromatic number $\chi_c(G, \sigma)$ of a signed graph $(G, \sigma)$ is the infimum of those $r$ for which $(G, \sigma)$ admits a circular $r$-coloring. For a graph $G$, we define the signed circular chromatic number of $G$ to be $\max\{\chi_c(G, \sigma): \sigma \text{ is a signature of $G$}\}$. We study basic properties of circular coloring of signed graphs and develop tools for calculating $\chi_c(G, \sigma)$. We explore the relation between the circular chromatic number and the signed circular chromatic number of graphs, and present bounds for the signed circular chromatic number of some families of graphs. In particular, we determine the supremum of the signed circular chromatic number of $k$-chromatic graphs of large girth, of simple bipartite planar graphs, $d$-degenerate graphs, simple outerplanar graphs and series-parallel graphs. We construct a signed planar simple graph whose circular chromatic number is $4+\frac{2}{3}$. This is based and improves on a signed graph built by Kardos and Narboni as a counterexample to a conjecture of Máčajová, Raspaud, and Škoviera.
A Patch-based Image Denoising Method Using Eigenvectors of the Geodesics' Gramian Matrix

With the sophisticated modern technology in the camera industry, the demand for accurate and visually pleasing images is increasing. However, the quality of images captured by cameras are inevitably degraded by noise. Thus, some processing on images is required to filter out the noise without losing vital image features such as edges, corners, etc. Even though the current literature offers a variety of denoising methods, fidelity and efficiency of their denoising are sometimes uncertain. Thus, here we propose a novel and computationally efficient image denoising method that is capable of producing an accurate output. This method inputs patches partitioned from the image rather than pixels that are well known for preserving image smoothness. Then, it performs denoising on the manifold underlying the patch-space rather than that in the image domain to better preserve the features across the whole image. We validate the performance of this method against benchmark image processing methods.
Link between radio-loud AGNs and host-galaxy shape

The morphology of quiescent galaxies has been found to be correlated with the activity of their central super massive black hole. In this work, we use data from the first data release of the LOFAR Two-Metre Sky Survey (LoTSS DR1) and the Sloan Digital Sky Survey Data Release 7 (SDSS DR7) to select more than 15 000 quiescent galaxies at z < 0.3 to investigate the connection between radio-loud active galactic nuclei (RLAGNs) and the morphology of their host galaxy. Taking advantage of the depth of LoTSS, we find that the fraction of RLAGNs with L150 MHz > 1021 W Hz−1 at fixed stellar mass, velocity dispersion, or surface mass density does not depend on the galaxy projected axis ratio (q). However, the high-power (L150 MHz > 1023 W Hz−1) RLAGNs are more likely to be found in massive, round galaxies, while the low- and intermediate-power (L150 MHz ≤ 1023 W Hz−1) RLAGNs have similar distributions of q to non-RLAGN galaxies. We argue that our results support the picture that high-power RLAGNs are more easily triggered in galaxies with a merger-rich history, while low-power RLAGNs can be triggered in galaxies growing mainly via secular processes. Our work also supports the idea that the low-luminosity RLAGN may be sufficient for maintenance-mode feedback in low-mass quiescent galaxies with disc-like morphology, which is based on a simple extrapolation from the observed energy balance between cooling and RLAGN-induced cavities in massive clusters. We find no significant difference between the q distributions of RLAGNs likely to be found in clusters and those likely not found in clusters after controlling the radio luminosity and stellar mass of the two samples, indicating that the environment does not significantly influence the morphology–RLAGN correlation.
The NeteaseGames System for Voice Conversion Challenge 2020 with Vector-quantization Variational Autoencoder and WaveNet

This paper presents the description of our submitted system for Voice Conversion Challenge (VCC) 2020 with vector-quantization variational autoencoder (VQ-VAE) with WaveNet as the decoder, i.e., VQ-VAE-WaveNet. VQ-VAE-WaveNet is a nonparallel VAE-based voice conversion that reconstructs the acoustic features along with separating the linguistic information with speaker identity. The model is further improved with the WaveNet cycle as the decoder to generate the high-quality speech waveform, since WaveNet, as an autoregressive neural vocoder, has achieved the SoTA result of waveform generation. In practice, our system can be developed with VCC 2020 dataset for both Task 1 (intra-lingual) and Task 2 (cross-lingual). However, we only submit our system for the intra-lingual voice conversion task. The results of VCC 2020 demonstrate that our system VQ-VAE-WaveNet achieves: 3.04 mean opinion score (MOS) in naturalness and a 3.28 average score in similarity ( the speaker similarity percentage (Sim) of 75.99%) for Task 1. The subjective evaluations also reveal that our system gives top performance when no supervised learning is involved. What's more, our system performs well in some objective evaluations. Specifically, our system achieves an average score of 3.95 in naturalness in automatic naturalness prediction and ranked the 6th and 8th, respectively in ASV-based speaker similarity and spoofing countermeasures.
Reducing (to) the Ranks. Eﬃcient Rank-based B¨uchi Automata Complementation (Technical Report)

This paper provides several optimizations of the rank-based approach for complementing B¨uchi automata. We start with Schewe’s theoretically optimal construction and develop a set of techniques for pruning its state space that are key to obtaining small complement automata in practice. In particular, the reductions (except one) have the property that they preserve (at least some) so-called super-tight runs , which are runs whose ranking is as tight as possible . Our evaluation on a large benchmark shows that the optimizations indeed signiﬁcantly help the rank-based approach and that, in a large number of cases, the obtained complement is the smallest from those produced by a large number of state-of-the-art tools for B¨uchi complementation.
Phenazine Production in The Presence of Heavy Metals in Recombinant Erwinia herbicola Bearing the Hemoglobin Gene

Phenazine production of the highest, and 50 ppm Cd (Fig. 2 a) in the presence of recombinant bacteria observed that 72 hours (9.18 μg/ml). In the presence of wild bacteria 100 ppm Cd (Fig. 2 b) observed that 72 hours (8.16 μg/ml). In the presence of 50 ppm Cd and 150 ppm Cd 72 hours recombinant bacteria and
Preliminary Phytochemical Screening of Cajanus cajan Linn.

The seeds and leaves of Cajanus cajan Linn. which is a shrub cultivated in Central India are used in medicine belong to the family Fabeaceae. Its application in anti-ulcer, wound healing, hepatoprotective, anti-asthamatic ailments is practiced by the villagers. From extensive literature survey it was revealed that no reports were available on macroscopic as well as pytochemical standardization parameters of Cajanus cajan Linn. in order to check the identity and purity of the drug. The present study aimed to establish methods for quality control of drugs, botanical evaluation which comprises of macroscopy, physicochemical parameters like loss on drying, extractive values, Ash values and to investigate the phytochemicals present in the extracts in the preliminary level with respect tochemical tests and thin layer chromatography. Thus it was thought worthwhile to explore this videly cultivated plant on the basis of it's standardization parameters. The study will provide referential information for the better understanding the plant to be used as medicine for the treatment of the various disease.
Photovoltaic module segmentation and thermal analysis tool from thermal images

The growing interest in the use of clean energy has led to the construction of increasingly large photovoltaic systems. Consequently, monitoring the proper functioning of these systems has become a highly relevant issue. In this paper, automatic detection, and analysis of photovoltaic modules are proposed. To perform the analysis, a module identification step, based on a digital image processing algorithm, is first carried out. This algorithm consists of image enhancement (contrast enhancement, noise reduction, etc.), followed by segmentation of the photovoltaic module. Subsequently, a statistical analysis based on the temperature values of the segmented module is performed. Besides, a graphical user interface has been designed as a potential tool that provides relevant information of the photovoltaic modules.
DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM

The assumption of scene rigidity is common in visual SLAM algorithms. However, it limits their applicability in populated real-world environments. Furthermore, most scenarios including autonomous driving, multi-robot collaboration and augmented/virtual reality, require explicit motion information of the surroundings to help with decision making and scene understanding. We present in this paper DynaSLAM II, a visual SLAM system for stereo and RGB-D camera configurations that tightly integrates the multi-object tracking capability. DynaSLAM II makes use of instance semantic segmentation and ORB features to track dynamic objects. The structures of the static scene and the dynamic objects are optimized jointly with the trajectories of both the camera and the moving agents within a novel bundle adjustment proposal. The 3D bounding boxes of the objects are also estimated and loosely optimized within a fixed temporal window. We demonstrate that tracking dynamic objects does not only provide rich clues for scene understanding but can be also beneficial for camera tracking.
Transcriptional regulation of the I factor, a Drosophila melanogaster transposable element

The I factor is a transposable element of the LINE family found in Drosophila melanogaster. High frequency I factor transposition is associated with I-R hybrid dysgenesis, a syndrome of female sterility occurring in the progeny of males from an inducer strain, which carry active I factors and females from a reactive strain which do not. Expression of the I factor is restricted to the female germ line and is repressed in inducer strains. Transcription is regulated by sequences located internally in the 5' untranslated region of the element. A sequence-specific DNA binding protein present in ovaries recognises a 19bp site within this region, known as site 1, which a previous study suggested is important for germ line-specific transcription. The protein that binds to site 1 was identified as Adult Enhancer Factor 1, a transcriptional repressor that regulates the alcohol dehydrogenase and yolk protein genes. A series of mutations were made in site 1 and the effects on AEF1 binding in vitro, and the expression of a reporter gene controlled by I factor regulatory sequences in vivo, were investigated. It was discovered that deletion of site 1 and other mutations that reduce AEF1 binding did not reduce expression, contrary to previous findings. The transgenes used in the previous study were characterized and found not to be as they were described. Constructs designed to express sense and antisense AEF-1 RNA in the female germ line were introduced into flies and the effect on ovarian expression from a reporter gene under the control of I factor regulatory sequences was investigated. In each case expression was slightly lower than in a control experiment. The implications of these findings for models of I factor regulation are discussed.
Pronoun-Targeted Fine-tuning for NMT with Hybrid Losses

Popular Neural Machine Translation model training uses strategies like backtranslation to improve BLEU scores, requiring large amounts of additional data and training. We introduce a class of conditional generative-discriminative hybrid losses that we use to fine-tune a trained machine translation model. Through a combination of targeted fine-tuning objectives and intuitive re-use of the training data the model has failed to adequately learn from, we improve the model performance of both a sentence-level and a contextual model without using any additional data. We target the improvement of pronoun translations through our fine-tuning and evaluate our models on a pronoun benchmark testset. Our sentence-level model shows a 0.5 BLEU improvement on both the WMT14 and the IWSLT13 De-En testsets, while our contextual model achieves the best results, improving from 31.81 to 32 BLEU on WMT14 De-En testset, and from 32.10 to 33.13 on the IWSLT13 De-En testset, with corresponding improvements in pronoun translation. We further show the generalizability of our method by reproducing the improvements on two additional language pairs, Fr-En and Cs-En. Code available at .
Boosting One-Point Derivative-Free Online Optimization via Residual Feedback

Zeroth-order optimization (ZO) typically relies on two-point feedback to estimate the unknown gradient of the objective function. Nevertheless, two-point feedback can not be used for online optimization of time-varying objective functions, where only a single query of the function value is possible at each time step. In this work, we propose a new one-point feedback method for online optimization that estimates the objective function gradient using the residual between two feedback points at consecutive time instants. Moreover, we develop regret bounds for ZO with residual feedback for both convex and nonconvex online optimization problems. Specifically, for both deterministic and stochastic problems and for both Lipschitz and smooth objective functions, we show that using residual feedback can produce gradient estimates with much smaller variance compared to conventional one-point feedback methods. As a result, our regret bounds are much tighter compared to existing regret bounds for ZO with conventional one-point feedback, which suggests that ZO with residual feedback can better track the optimizer of online optimization problems. Additionally, our regret bounds rely on weaker assumptions than those used in conventional one-point feedback methods. Numerical experiments show that ZO with residual feedback significantly outperforms existing one-point feedback methods also in practice.
Al-based BMI Inference from Facial Images: An Application to Weight Monitoring

Self-diagnostic image-based methods for healthy weight monitoring is gaining increased interest following the alarming trend of obesity. Only a handful of academic studies exist that investigate AI-based methods for Body Mass Index (BMI) inference from facial images as a solution to healthy weight monitoring and management. To promote further research and development in this area, we evaluate and compare the performance of five different deep-learning based Convolutional Neural Network (CNN) architectures i.e., VGG19, ResNet50, DenseNet, MobileNet, and lightCNN for BMI inference from facial images. Experimental results on the three publicly available BMI annotated facial image datasets assembled from social media, namely, VisualBMI, VIP-Attributes, and Bollywood datasets, suggest the efficacy of the deep learning methods in BMI inference from face images with minimum Mean Absolute Error (MAE) of 1.04 obtained using ResNet50.
STRUCTURE-ACTIVITY RELATIONSHIPS IN 5-SUBSTITUTED 3-AMINO-1, 2, 4-TRIAZOLES-INDUCED GOITERS IN RATS

The goitrogenic actions of 5-substituted 3-amino-l, 2, 4-triazoles, 3-amino-5-mercapto-l, 2, 4-triazole (AMTZ), 3-amino-1, 2, 4-triazole-5-carboxylic acid (ATZC), 3, 5-diamino-l, 2, 4-triazole (DTZ), 3-amino-5-methylthio-l, 2, 4-triazole (AMTTZ), and 3-amino-1, 2, 4- triazole (ATZ) were compared in rats. ATZ, AMTZ, ATZC, and DTZ inhibited the thyroid peroxidase (TPO)-catalyzed oxidation of guaiacol in vitro, exhibiting the order of antithyroid activity ATZ>AMTZ>ATZC>DTZ. In in vivo experiments, ATZ, ATZC, or AMTZ inhibited TPO activity, and induced goiter accompanied by decrease in serum thyroid hormone and increase in serum thyroid-stimulating hormone in rats. These antithyroid activities were shown in order of ATZ>ATZC>AMTZ. The TPO inhibitory action of ATZC in vivo was weaker than that of AMTZ, but it was more persistent than AMTZ. The anti-TPO action of DTZ disappeared in vivo, and AMTTZ showed no effects in vitro and in vivo. These results show that ATZC, AMTZ, and DTZ potentially have the anti-TPO activity like as ATZ and the 5-position site of ATZ would have a very important meaning to determine the antithyroid action of ATZ. In addition, the results strongly suggest that the anti-thyroid action depends on the substitute but not on the parental compound.
Multi-Task Deep Reinforcement Learning with Knowledge Transfer for Continuous Control

While Deep Reinforcement Learning (DRL) has emerged as a promising approach to many complex tasks, it remains challenging to train a single DRL agent that is capable of undertaking multiple different continuous control tasks. In this paper, we present a Knowledge Transfer based Multi-task Deep Reinforcement Learning framework (KTM-DRL) for continuous control, which enables a single DRL agent to achieve expert-level performance in multiple different tasks by learning from task-specific teachers. In KTM-DRL, the multi-task agent first leverages an offline knowledge transfer algorithm designed particularly for the actor-critic architecture to quickly learn a control policy from the experience of task-specific teachers, and then it employs an online learning algorithm to further improve itself by learning from new online transition samples under the guidance of those teachers. We perform a comprehensive empirical study with two commonly-used benchmarks in the MuJoCo continuous control task suite. The experimental results well justify the effectiveness of KTM-DRL and its knowledge transfer and online learning algorithms, as well as its superiority over the state-of-the-art by a large margin.
Explainability for fair machine learning

As the decisions made or influenced by machine learning models increasingly impact our lives, it is crucial to detect, understand, and mitigate unfairness. But even simply determining what ``unfairness'' should mean in a given context is non-trivial: there are many competing definitions, and choosing between them often requires a deep understanding of the underlying task. It is thus tempting to use model explainability to gain insights into model fairness, however existing explainability tools do not reliably indicate whether a model is indeed fair. In this work we present a new approach to explaining fairness in machine learning, based on the Shapley value paradigm. Our fairness explanations attribute a model's overall unfairness to individual input features, even in cases where the model does not operate on sensitive attributes directly. Moreover, motivated by the linearity of Shapley explainability, we propose a meta algorithm for applying existing training-time fairness interventions, wherein one trains a perturbation to the original model, rather than a new model entirely. By explaining the original model, the perturbation, and the fair-corrected model, we gain insight into the accuracy-fairness trade-off that is being made by the intervention. We further show that this meta algorithm enjoys both flexibility and stability benefits with no loss in performance.