Miracle-dz/UniFAR-FTUs huggingface dataset:
Behavior of Liquidity Providers in Decentralized Exchanges

Decentralized exchanges (DEXes) have introduced an innovative trading mechanism, where it is not necessary to match buy-orders and sell-orders to execute a trade. DEXes execute each trade individually, and the exchange rate is automatically determined by the ratio of assets reserved in the market. Therefore, apart from trading, financial players can also liquidity providers, benefiting from transaction fees from trades executed in DEXes. Although liquidity providers are essential for the functionality of DEXes, it is not clear how liquidity providers behave in such markets. In this paper, we aim to understand how liquidity providers react to market information and how they benefit from providing liquidity in DEXes. We measure the operations of liquidity providers on Uniswap and analyze how they determine their investment strategy based on market changes. We also reveal their returns and risks of investments in different trading pair categories, i.e., stable pairs, normal pairs, and exotic pairs. Further, we investigate the movement of liquidity between trading pools. To the best of our knowledge, this is the first work that systematically studies the behavior of liquidity providers in DEXes.
Online Hate: Behavioural Dynamics and Relationship with Misinformation

Online debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In this work, we perform hate speech detection on a corpus of more than one million comments on YouTube videos through a machine learning model fine-tuned on a large set of hand-annotated data. Our analysis shows that there is no evidence of the presence of"serial haters", intended as active users posting exclusively hateful comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed towards one of the two categories of video channels (questionable, reliable) are more prone to use inappropriate, violent, or hateful language within their opponents community. Interestingly, users loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find that the overall toxicity of the discussion increases with its length, measured both in terms of number of comments and time. Our results show that, coherently with Godwin's law, online debates tend to degenerate towards increasingly toxic exchanges of views.
Feature extraction and evaluation for BioMedical Question Answering

In this paper, we present our work on the BioASQ pipeline. The goal is to answer four types of questions: summary, yes/no, factoids, and list. Our goal is to empirically evaluate different modules involved: the feature extractor and the sentence selection block. We used our pipeline to test the effectiveness of each module for all kinds of question types and perform error analysis. We defined metrics that are useful for future research related to the BioASQ pipeline critical to improve the performance of the training pipeline.
TensorFlow RiemOpt: a library for optimization on Riemannian manifolds

The adoption of neural networks and deep learning in non-Euclidean domains has been hindered until recently by the lack of scalable and efficient learning frameworks. Existing toolboxes in this space were mainly motivated by research and education use cases, whereas practical aspects, such as deploying and maintaining machine learning models, were often overlooked. We attempt to bridge this gap by proposing TensorFlow RiemOpt, a Python library for optimization on Riemannian manifolds in TensorFlow. The library is designed with the aim for a seamless integration with the TensorFlow ecosystem, targeting not only research, but also streamlining production machine learning pipelines.
DAYA HAMBAT EKSTRAK DAUN DEWANDARU (Eugenia uniflora L.) TERHADAP AKTIVITAS GST KELAS PI GINJAL TIKUS SECARA IN VITRO

Extracts of dewandaru (Eugenia uniflora L.) have been reported to be inhibitor of general classes (α, μ, and π) of glutathione S-transferase (GST), but it had not been known yet the kind of GST (α, μ, or π) which was inhibited by the extracts. The objective of this research was to know the inhibitory potency of extracts of dewandaru on π (pi) class rat’s renal GST activity. The extracts were obtained by maceration method with chloroform, ethyl acetic, and ethanol as a solvent. To represent the pi class of GST activity, ethacrinic acid was used as a specific substrate on the conjugation reaction with glutation (GSH) was measured spectrometrically. Preparation of sitosol fraction which contains GST by centrifugation. Protein was determined according to the method of Lowry with bovine serum albumin (BSA) as the standard. In the determining IC50 (the concentration of extract which resulting in 50% of inhibitory potency on GST activity), the activity test of GST was added with extract of dewandaru in vary concentrations. IC50 was obtained from linier regression curve which stated the correlation between the log concentration of extract of dewandaru which was added and % inhibition resulted. The result shown that chloroform, ethyl acetic, and ethanolic extract of dewandaru inhibit the pi class of rat’s renal GST activity (EA substrate) with IC50 438,62; 257,35 and 112,86 μg/ml respectively.
Convective, absolute and global azimuthal magnetorotational instabilities

Abstract We study the convective and absolute forms of azimuthal magnetorotational instability (AMRI) in a cylindrical Taylor–Couette (TC) flow with an imposed azimuthal magnetic field. We show that the domain of the convective AMRI is wider than that of the absolute AMRI. Actually, it is the absolute instability which is the most relevant and important for magnetic TC flow experiments. The absolute AMRI, unlike the convective one, stays in the device, displaying a sustained growth that can be experimentally detected. We also study the global AMRI in a TC flow of finite height using direct numerical simulation and find that its emerging butterfly-type structure – a spatio-temporal variation in the form of axially upward and downward travelling waves – is in a very good agreement with the linear analysis, which indicates the presence of two dominant absolute AMRI modes in the flow giving rise to this global butterfly pattern.
Audio-Visual Scene Classification: Analysis of DCASE 2021 Challenge Submissions

This paper presents the details of the Audio-Visual Scene Classification task in the DCASE 2021 Challenge (Task 1 Subtask B). The task is concerned with classification using audio and video modalities, using a dataset of synchronized recordings. This task has attracted 43 submissions from 13 different teams around the world. Among all submissions, more than half of the submitted systems have better performance than the baseline. The common techniques among the top systems are the usage of large pretrained models such as ResNet or EfficientNet which are trained for the task-specific problem. Fine-tuning, transfer learning, and data augmentation techniques are also employed to boost the performance. More importantly, multi-modal methods using both audio and video are employed by all the top 5 teams. The best system among all achieved a logloss of 0.195 and accuracy of 93.8%, compared to the baseline system with logloss of 0.662 and accuracy of 77.1%.
Distribution Matching for Machine Teaching

Machine teaching is an inverse problem of machine learning that aims at steering the student toward its target hypothesis, in which the teacher has already known the student's learning parameters. Previous studies on machine teaching focused on balancing the teaching risk and cost to find the best teaching examples deriving from the student model. This optimization solver is in general ineffective when the student does not disclose any cue of the learning parameters. To supervise such a teaching scenario, this article presents a distribution matching-based machine teaching strategy via iteratively shrinking the teaching cost in a smooth surrogate, which eliminates boundary perturbations from the version space. Technically, our strategy could be redefined as a cost-controlled optimization process that finds the optimal teaching examples without further exploring the parameter distribution of the student. Then, given any limited teaching cost, the training examples would have a closed-form expression. Theoretical analysis and experiment results demonstrate the effectiveness of this strategy.
Accurate Frequency Estimation With Fewer DFT Interpolations Based on Padé Approximation

Frequency estimation is a fundamental problem in many areas. The well-known A&M and its variant estimators have established an estimation framework by iteratively interpolating the discrete Fourier transform (DFT) coefficients. In general, those estimators require two DFT interpolations per iteration, have uneven initial estimation performance against frequencies, and are incompetent for small sample numbers due to low-order approximations involved. Exploiting the iterative estimation framework of A&M, we unprecedentedly introduce the Padé approximation to frequency estimation, unveil some features about the updating function used for refining the estimation in each iteration, and develop a simple closed-form solution to solving the residual estimation error. Extensive simulation results are provided, validating the superiority of the new estimator over the state-the-art estimators in wide ranges of key parameters.
Incremental Learning of Probabilistic Movement Primitives (ProMPs) for Human-Robot Cooperation

For a successful deployment of physical Human-Robot Cooperation (pHRC), humans need to be able to teach robots new motor skills quickly. Probabilistic movement primitives (ProMPs) are a promising method to encode a robot's motor skills learned from human demonstrations in pHRC settings. However, most algorithms to learn ProMPs from human demonstrations operate in batch mode, which is not ideal in pHRC. In this paper we propose a new learning algorithm to learn ProMPs incrementally in pHRC settings. Our algorithm incorporates new demonstrations sequentially as they arrive, allowing humans to observe the robot's learning progress and incrementally shape the robot's motor skill. A built in forgetting factor allows for corrective demonstrations resulting from the human's learning curve or changes in task constraints. We compare the performance of our algorithm to existing batch ProMP algorithms on reference data generated from a pick-and-place task at our lab. Furthermore, we show in a proof of concept study on a Franka Emika Panda how the forgetting factor allows us to adopt changes in the task. The incremental learning algorithm presented in this paper has the potential to lead to a more intuitive learning progress and to establish a successful cooperation between human and robot faster than training in batch mode.
Inaccuracy Matters: Accounting for Solution Accuracy in Event-Triggered Nonlinear Model Predictive Control

We consider the effect of using approximate system predictions in event-triggered control schemes. These approximations often result from using numerical transcription methods for solving continuous-time optimal control problems. Mesh refinement can guarantee upper bounds on the error in the differential equations that model the system dynamics. We employ the accuracy guarantees of a mesh refinement scheme to show that the proposed event-triggering scheme, which compares the measured system with approximate state predictions, can be used with a guaranteed strictly positive interupdate time. Furthermore, if knowledge of the employed transcription scheme or the approximation errors are available, then better online estimates of interupdate times can be obtained. We also detail a method of tightening constraints on the approximate system trajectory to guarantee constraint satisfaction of the continuous-time system. This is the first work to incorporate prediction accuracy in triggering metrics to guarantee reliable lower bounds for interupdate times and perform solution-dependent constraint tightening.
Non-negative matrix factorization algorithms greatly improve topic model fits

We report on the potential for using algorithms for non-negative matrix factorization (NMF) to improve parameter estimation in topic models. While several papers have studied connections between NMF and topic models, none have suggested leveraging these connections to develop new algorithms for fitting topic models. NMF avoids the"sum-to-one"constraints on the topic model parameters, resulting in an optimization problem with simpler structure and more efficient computations. Building on recent advances in optimization algorithms for NMF, we show that first solving the NMF problem then recovering the topic model fit can produce remarkably better fits, and in less time, than standard algorithms for topic models. While we focus primarily on maximum likelihood estimation, we show that this approach also has the potential to improve variational inference for topic models. Our methods are implemented in the R package fastTopics.
LEVEL-SET PERCOLATION OF THE GAUSSIAN FREE FIELD ON REGULAR GRAPHS III: GIANT COMPONENT ON EXPANDERS

We consider the zero-average Gaussian free field on a certain class of finite d-regular graphs for fixed d ≥ 3. This class includes d-regular expanders of large girth and typical realisations of random d-regular graphs. We show that the level set of the zero-average Gaussian free field above level h has a giant component in the whole supercritical phase, that is for all h < h⋆, with probability tending to one as the size of the graphs tends to infinity. In addition, we show that this component is unique. This significantly improves the result of [AČ20b], where it was shown that a linear fraction of vertices is in mesoscopic components if h < h⋆.
Data Augmentation for Text Generation Without Any Augmented Data

Data augmentation is an effective way to improve the performance of many neural text generation models. However, current data augmentation methods need to define or choose proper data mapping functions that map the original samples into the augmented samples. In this work, we derive an objective to formulate the problem of data augmentation on text generation tasks without any use of augmented data constructed by specific mapping functions. Our proposed objective can be efficiently optimized and applied to popular loss functions on text generation tasks with a convergence rate guarantee. Experiments on five datasets of two text generation tasks show that our approach can approximate or even surpass popular data augmentation methods.
A systematic review of transfer learning based approaches for diabetic retinopathy detection

Cases of diabetes and related diabetic retinopathy (DR) have been increasing at an alarming rate in modern times. Early detection of DR is an important problem since it may cause permanent blindness in the late stages. In the last two decades, many different approaches have been applied in DR detection. Reviewing academic literature shows that deep neural networks (DNNs) have become the most preferred approach for DR detection. Among these DNN approaches, Convolutional Neural Network (CNN) models are the most used ones in the field of medical image classification. Designing a new CNN architecture is a tedious and time-consuming approach. Additionally, training an enormous number of parameters is also a difficult task. Due to this reason, instead of training CNNs from scratch, using pre-trained models has been suggested in recent years as transfer learning approach. Accordingly, the present study as a review focuses on DNN and Transfer Learning based applications of DR detection considering 43 publications between 2015 and 2021. The published papers are summarized using 3 figures and 10 tables, giving information about 29 pre-trained CNN models, 13 DR data sets and standard performance metrics.
ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models

Most widely used pre-trained language models operate on sequences of tokens corresponding to word or subword units. By comparison, token-free models that operate directly on raw text (bytes or characters) have many benefits: They can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Because byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments.1
Potential of GaN Gunn Devices for High Power Generation Above 200 GHz

This paper investigates the potential of Gallium Nitride (GaN) Gunn diodes for generating high power radiation at millimeter and sub-millimeter wave frequencies. Simulations were carried out based on an ensemble Monte Carlo computer model. This model accounts for thermal effects, series resistance, and device circuit interaction through the harmonic balance technique. The accuracy of the model has been validated in the case of InP Gunn devices at frequencies above 100 GHz[1]. Initially uniform and linearly graded doping profiles in the active region were considered. It is found that, similar to devices based on InP, the graded profile resulted in a much improved performance in terms of power, efficiency, and operating temperature. In particular, a GaN Gunn structure consisting of a 1 μm thick active region with a graded doping profile increasing from 6 × 10 15 cm −3 at the cathode terminal to 4.6×10 16 cm −3 at the anode terminal yielded promising results. The DC bias voltage was estimated from the calculated velocity-electric field data to be about 25 V. With this bias, it was found that oscillations in the fundamental mode could be obtained over the frequency range from 220 GHz to 330 GHz subject to a load resistance of 1 Ohm and a maximum operating temperature of 800 K. The maximum output power was 30 mW at 290 GHz with a corresponding conversion efficiency of 0.25 %. Considerable improvement was obtained from a device with a heterojunction at the cathode. For a 1 μm device, an optimum power level close to 150 mW was predicted at 215 GHz with an efficiency of 2 %. This estimated power level is about an order of magnitude higher than what can be achieved from InP Gunn oscillators at the same frequency.
DRL: Deep Reinforcement Learning for Intelligent Robot Control - Concept, Literature, and Future

Combination of machine learning (for generating machine intelligence), computer vision (for better environment perception), and robotic systems (for controlled environment interaction) motivates this work toward proposing a vision-based learning framework for intelligent robot control as the ultimate goal (vision-based learning robot). This work specifically introduces deep reinforcement learning as the the learning framework, a General-purpose framework for AI (AGI) meaning application-independent and platform-independent. In terms of robot control, this framework is proposing specifically a high-level control architecture independent of the low-level control, meaning these two required level of control can be developed separately from each other. In this aspect, the high-level control creates the required intelligence for the control of the platform using the recorded low-level controlling data from that same platform generated by a trainer. The recorded low-level controlling data is simply indicating the successful and failed experiences or sequences of experiments conducted by a trainer using the same robotic platform. The sequences of the recorded data are composed of observation data (input sensor), generated reward (feedback value) and action data (output controller). For experimental platform and experiments, vision sensors are used for perception of the environment, different kinematic controllers create the required motion commands based on the platform application, deep learning approaches generate the required intelligence, and finally reinforcement learning techniques incrementally improve the generated intelligence until the mission is accomplished by the robot.
Noised Consistency Training for Text Summarization

Neural abstractive summarization methods often require large quantities of labeled training data. However, labeling large amounts of summarization data is often prohibitive due to time, financial, and expertise constraints, which has limited the usefulness of summarization systems to practical applications. In this paper, we argue that this limitation can be overcome by a semi-supervised approach: consistency training which is to leverage large amounts of unlabeled data to improve the performance of supervised learning over a small corpus. The consistency regularization semi-supervised learning can regularize model predictions to be invariant to small noise applied to input articles. By adding noised unlabeled corpus to help regularize consistency training, this framework obtains comparative performance without using the full dataset. In particular, we have verified that leveraging large amounts of unlabeled data decently improves the performance of supervised learning over an insufficient labeled dataset.
Learning Structures for Deep Neural Networks

In this paper, we focus on the unsupervised setting for structure learning of deep neural networks and propose to adopt the efficient coding principle, rooted in information theory and developed in computational neuroscience, to guide the procedure of structure learning without label information. This principle suggests that a good network structure should maximize the mutual information between inputs and outputs, or equivalently maximize the entropy of outputs under mild assumptions. We further establish connections between this principle and the theory of Bayesian optimal classification, and empirically verify that larger entropy of the outputs of a deep neural network indeed corresponds to a better classification accuracy. Then as an implementation of the principle, we show that sparse coding can effectively maximize the entropy of the output signals, and accordingly design an algorithm based on global group sparse coding to automatically learn the inter-layer connection and determine the depth of a neural network. Our experiments on a public image classification dataset demonstrate that using the structure learned from scratch by our proposed algorithm, one can achieve a classification accuracy comparable to the best expert-designed structure (i.e., convolutional neural networks (CNN)). In addition, our proposed algorithm successfully discovers the local connectivity (corresponding to local receptive fields in CNN) and invariance structure (corresponding to pulling in CNN), as well as achieves a good tradeoff between marginal performance gain and network depth.