Miracle-dz/UniFAR-FTUs huggingface dataset:
Restore from Restored: Single Image Denoising with Pseudo Clean Image

In this study, we propose a simple and effective fine-tuning algorithm called "restore-from-restored", which can greatly enhance the performance of fully pre-trained image denoising networks. Many supervised denoising approaches can produce satisfactory results using large external training datasets. However, these methods have limitations in using internal information available in a given test image. By contrast, recent self-supervised approaches can remove noise in the input image by utilizing information from the specific test input. However, such methods show relatively lower performance on known noise types such as Gaussian noise compared to supervised methods. Thus, to combine external and internal information, we fine-tune the fully pre-trained denoiser using pseudo training set at test time. By exploiting internal self-similar patches (i.e., patch-recurrence), the baseline network can be adapted to the given specific input image. We demonstrate that our method can be easily employed on top of the state-of-the-art denoising networks and further improve the performance on numerous denoising benchmark datasets including real noisy images.
Architectural Software Patterns for the Development of IoT Smart Applications

Software developers usually start coding an application with no formal architecture in mind and relying on intuition and experience instead of on well-known design patters. A different approach is recommended for the development of IoT smart applications due to its high complexity that combines sensors, actuators, communication technologies, and big data analytics, as well as its distributed nature that spans for different layers of field, fog, and cloud infrastructure. Literature reports many experiences of software development for IoT smart applications. However, architectural solutions are presented with no rationale for the choice of software components and the way they relate to each other. This paper proposes a classification for software components and their relationships in order to model a software architecture for a particular IoT smart application. Three smart applications for cities, buildings, and agriculture were selected as examples of using some components, connectors, and well-known design patterns. Finally, the problems and challenges involved in the choice of software architectures for IoT are discussed.
Nonparametric constraints on neutron star matter with existing and upcoming gravitational wave and pulsar observations

Observations of neutron stars, whether in binaries or in isolation, provide information about the internal structure of the most extreme material objects in the Universe. In this work, we combine information from recent observations to place joint constraints on the properties of neutron star matter. We use (i) lower limits on the maximum mass of neutron stars obtained through radio observations of heavy pulsars, (ii) constraints on tidal properties inferred through the gravitational waves neutron star binaries emit as they coalesce, and (iii) information about neutron stars’ masses and radii obtained through X-ray emission from surface hot spots. In order to combine information from such distinct messengers while avoiding the kind of modeling systematics intrinsic to parametric inference schemes, we employ a nonparametric representation of the neutron-star equation of state based on Gaussian processes conditioned on nuclear theory models. We find that existing astronomical observations imply R_(1.4) = 12.32^(+1.09)_(−1.47) km for the radius of a 1.4 M⊙ neutron star and p(2ρ_(nuc)) = 3.8^(+2.7)_(−2.9) × 10³⁴ dyn/cm² for the pressure at twice nuclear saturation density at the 90% credible level. The upper bounds are driven by the gravitational wave observations, while X-ray and heavy pulsar observations drive the lower bounds. Additionally, we compute expected constraints from potential future astronomical observations and find that they can jointly determine R_(1.4) to O(1) km and p(2ρ_(nuc)) to 80% relative uncertainty in the next five years.
A Framework for Evaluation of Machine Reading Comprehension Gold Standards

Machine Reading Comprehension (MRC) is the task of answering a question over a paragraph of text. While neural MRC systems gain popularity and achieve noticeable performance, issues are being raised with the methodology used to establish their performance, particularly concerning the data design of gold standards that are used to evaluate them. There is but a limited understanding of the challenges present in this data, which makes it hard to draw comparisons and formulate reliable hypotheses. As a first step towards alleviating the problem, this paper proposes a unifying framework to systematically investigate the present linguistic features, required reasoning and background knowledge and factual correctness on one hand, and the presence of lexical cues as a lower bound for the requirement of understanding on the other hand. We propose a qualitative annotation schema for the first and a set of approximative metrics for the latter. In a first application of the framework, we analyse modern MRC gold standards and present our findings: the absence of features that contribute towards lexical ambiguity, the varying factual correctness of the expected answers and the presence of lexical cues, all of which potentially lower the reading comprehension complexity and quality of the evaluation data.
Explore and Exploit with Heterotic Line Bundle Models

We use deep reinforcement learning to explore a class of heterotic SU(5) GUT models constructed from line bundle sums over Complete Intersection Calabi Yau (CICY) manifolds. We perform several experiments where A3C agents are trained to search for such models. These agents significantly outperform random exploration, in the most favourable settings by a factor of 1700 when it comes to finding unique models. Furthermore, we find evidence that the trained agents also outperform random walkers on new manifolds. We conclude that the agents detect hidden structures in the compactification data, which is partly of general nature. The experiments scale well with h(1, 1), and may thus provide the key to model building on CICYs with large h(1, 1).
Video Caption Dataset for Describing Human Actions in Japanese

In recent years, automatic video caption generation has attracted considerable attention. This paper focuses on the generation of Japanese captions for describing human actions. While most currently available video caption datasets have been constructed for English, there is no equivalent Japanese dataset. To address this, we constructed a large-scale Japanese video caption dataset consisting of 79,822 videos and 399,233 captions. Each caption in our dataset describes a video in the form of “who does what and where. ” To describe human actions, it is important to identify the details of a person, place, and action. Indeed, when we describe human actions, we usually mention the scene, person, and action. In our experiments, we evaluated two caption generation methods to obtain benchmark results. Further, we investigated whether those generation methods could specify “who does what and where. ”
Sampling strategy in efficient potential energy surface mapping for predicting atomic diffusivity in crystals by machine learning

We propose a machine-learning-based (ML-based) method for efficiently predicting atomic diffusivity in crystals, in which the potential energy surface (PES) of a diffusion carrier is partially evaluated by first-principles calculations. To preferentially evaluate the region of interest governing the atomic diffusivity, a statistical PES model based on a Gaussian process (GP-PES) is constructed and updated iteratively from known information on already-computed potential energies (PEs). In the proposed method, all local energy minima (stable & metastable sites) and elementary processes of atomic diffusion (atomic jumps) are explored on the predictive mean of the GP-PES. The uncertainty of jump frequency in each elementary process is then estimated on the basis of the variance of the GP-PES. The acquisition function determining the next grid point to be computed is designed to reflect the impacts of the uncertainties of jump frequencies on the uncertainty of the macroscopic atomic diffusivity. The numerical solution of the master equation is here employed to readily estimate the atomic diffusivity, which enables us to design the acquisition function reflecting the centrality of each elementary process.
Automatic Curriculum Learning For Deep RL: A Short Survey

Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in Deep Reinforcement Learning (DRL). These methods shape the learning trajectories of agents by challenging them with tasks adapted to their capacities. In recent years, they have been used to improve sample efficiency and asymptotic performance, to organize exploration, to encourage generalization or to solve sparse reward problems, among others. To do so, ACL mechanisms can act on many aspects of learning problems. They can optimize domain randomization for Sim2Real transfer, organize task presentations in multi-task robotic settings, order sequences of opponents in multi-agent scenarios, etc. The ambition of this work is dual: 1) to present a compact and accessible introduction to the Automatic Curriculum Learning literature and 2) to draw a bigger picture of the current state of the art in ACL to encourage the cross-breeding of existing concepts and the emergence of new ideas.
QFAST: Quantum Synthesis Using a Hierarchical Continuous Circuit Space

We present QFAST, a quantum synthesis tool designed to produce short circuits and to scale well in practice. Our contributions are: 1) a novel representation of circuits able to encode placement and topology; 2) a hierarchical approach with an iterative refinement formulation that combines "coarse-grained" fast optimization during circuit structure search with a good, but slower, optimization stage only in the final circuit instantiation stage. When compared against state-of-the-art techniques, although not optimal, QFAST can generate much shorter circuits for "time dependent evolution" algorithms used by domain scientists. We also show the composability and tunability of our formulation in terms of circuit depth and running time. For example, we show how to generate shorter circuits by plugging in the best available third party synthesis algorithm at a given hierarchy level. Composability enables portability across chip architectures, which is missing from the available approaches.
Development of Automatic Speech Recognition for Kazakh Language using Transfer Learning

Development of Automatic Speech Recognition system for Kazakh language is very challenging due to a lack of data.Existing data of kazakh speech with its corresponding transcriptions are heavily accessed and not enough to gain a worth mentioning results.For this reason, speech recognition of Kazakh language has not been explored well.There are only few works that investigate this area with traditional methods Hidden Markov Model, Gaussian Mixture Model, but they are suffering from poor outcome and lack of enough this http URL our work we suggest a new method that takes pre-trained model of Russian language and applies its knowledge as a starting point to our neural network structure, which means that we are transferring the weights of pre-trained model to our neural network.The main reason we chose Russian model is that pronunciation of kazakh and russian languages are quite similar because they share 78 percent letters and there are quite large corpus of russian speech dataset. We have collected a dataset of Kazakh speech with transcriptions in the base of Suleyman Demirel University with 50 native speakers each having around 400 sentences.Data have been chosen from famous Kazakh books. We have considered 4 different scenarios in our experiment. First, we trained our neural network without using a pre-trained Russian model with 2 LSTM layers and 2 BiLSTM . Second, we have trained the same 2 LSTM layered and 2 BiLSTM layered using a pre-trained model. As a result, we have improved our models training cost and Label Error Rate by using external Russian speech recognition model up to 24 percent and 32 percent respectively.Pre-trained Russian language model has trained on 100 hours of data with the same neural network architecture.
Opinion Spam Detection in Online Reviews Using Neural Networks

People of the Internet era usually rely on online reviews to make decisions about an online purchase, a hotel booking or a car rental and much more, since people believe that making decisions based on other’s opinions lead to making the right choice. As writing fake reviews come with monetary gain, the opinion spam activities have increased dramatically on online review websites. Thus opinion spam in reviews has become a big challenge to people to make purchase decisions as well as damages the reputation of review websites. Hence the deceptive opinion spam detection is an essential task in the field of natural language processing. Most of the existing research on opinion spam detection uses the traditional bag-of-words model to represent the review text features and apply standard machine learning models such as Support-Vector Machines or Naïve Bayes as classifiers. There is only a few state of the art methods, which have utilized neural network based methods for spam detection. With the recent advancements in deep learning, there are successful applications of Convolutional Neural Networks (CNN) for natural language processing problems which have achieved improved performance. In this study, a CNN model is developed to detect opinion spam using the features extracted from the pre-trained GloVe: Global Vectors for Word Representation model. Moreover, some word and character level features used in existing research work, are extracted from the text and concatenated with a feature set extracted by the convolutional layers of the model to improve the performance. The proposed model found to outperform the state-of-the-art method and the inclusion of additional features improved the performance further.
Benchmarking TinyML Systems: Challenges and Direction

Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an entirely new class of smart applications. However, continued progress is limited by the lack of a widely accepted benchmark for these systems. Benchmarking allows us to measure and thereby systematically compare, evaluate, and improve the performance of systems and is therefore fundamental to a field reaching maturity. In this position paper, we present the current landscape of TinyML and discuss the challenges and direction towards developing a fair and useful hardware benchmark for TinyML workloads. Furthermore, we present our three preliminary benchmarks and discuss our selection methodology. Our viewpoints reflect the collective thoughts of the TinyMLPerf working group that is comprised of 30 organizations.
Optimization-Based Hierarchical Motion Planning for Autonomous Racing

In this paper we propose a hierarchical controller for autonomous racing where the same vehicle model is used in a two level optimization framework for motion planning. The high-level controller computes a trajectory that minimizes the lap time, and the low-level nonlinear model predictive path following controller tracks the computed trajectory online. Following a computed optimal trajectory avoids online planning and enables fast computational times. The efficiency is further enhanced by the coupling of the two levels through a terminal constraint, computed in the high-level controller. Including this constraint in the real-time optimization level ensures that the prediction horizon can be shortened, while safety is guaranteed. This proves crucial for the experimental validation of the approach on a full size driverless race car. The vehicle in question won two international student racing competitions using the proposed framework; moreover, our hierarchical controller achieved an improvement of 20% in the lap time compared to the state of the art result achieved using a very similar car and track.
Classification of a Pincer Nail Using a Recurrent-based Neural Network

A pincer nail is a disease where a nail becomes deformed. The origin is unclear. Patients with a serious pincer nail cannot walk normally. Serious pincer nails are often treated surgically but tend to recur. Early diagnosis is essential because it allows for complete recovery using a special wire treatment. However, most patients do not seek diagnosis until the pincer nail is advanced. This paper proposes a system to assist doctors in early diagnosis and verifies that the system can distinguish between patients with a pincer nail and healthy persons. The system measures the gravity center fluctuation and uses a neural network to classify the data and defines the feature of a pincer nail patient. The results indicate that that the neural network can classify patients and healthy persons using the gait while walking.
On the early evolution of massive star clusters: the case of cloud D1 and its embedded cluster in NGC 5253

We discuss a theoretical model for the early evolution of massive star clusters and confront it with the ALMA, radio and infrared observations of the young stellar cluster highly obscured by the molecular cloud D1 in the nearby dwarf spheroidal galaxy NGC 5253. We show that a large turbulent pressure in the central zones of D1 cluster may cause individual wind-blown bubbles to reach pressure confinement before encountering their neighbors. In this case stellar winds are added to the hot shocked wind pockets of gas around individual massive stars that leads them to meet and produce a cluster wind in time-scales less than $10^5$ yrs. In order to inhibit the possibility of cloud dispersal, or the early negative star formation feedback, one should account for mass loading that may come, for example, from pre-main sequence (PMS) low-mass stars through photo-evaporation of their proto-stellar disks. Mass loading at a rate in excess of 8$\times 10^{-9}$ M$_{\odot}$ yr$^{-1}$ per each PMS star is required to extend the hidden star cluster phase in this particular cluster. In this regime, the parental cloud remains relatively unperturbed, while pockets of molecular, photoionized and hot gas coexist within the star forming region. Nevertheless, the most likely scenario for cloud D1 and its embedded cluster is that the hot shocked winds around individual massive stars should merge at an age of a few millions of years when the PMS star proto-stellar disks vanish and mass loading ceases that allows a cluster to form a global wind.
Research on a Mine Gas Concentration Forecasting Model Based on a GRU Network

To improve the level of safety in coal mine production, it is important to enhance the accuracy of coal mine gas concentration prediction. In the context of deep learning, we proposed a mine gas concentration prediction model based on gated recurrent units (GRUs). The GRU model is not only simple in structure but also offers high prediction accuracy, and it can make full use of the time-series characteristic of mine gas concentration data. First, we apply the Pauta criterion and Lagrange interpolation to preprocess mine gas concentration monitoring data. Then, a spatial reconstruction method is used to construct the training set for the prediction model. Finally, the mean square error (MSE) is used as the loss function and adaptive moment estimation (Adam) is used as the optimization algorithm to determine the learning parameters of the GRU model for predicting gas concentration values. Experimental results show that compared with models based on support vector regression (SVR), a backpropagation neural network (BPNN), a recurrent neural network (RNN) and a long short-term memory (LSTM) network, the proposed GRU-based model for gas concentration prediction achieves reduced error on the test set, and moreover, the GRU model is more efficient than the LSTM model in terms of run time. Thus, the accuracy and efficiency of gas concentration prediction are both improved, showing that the proposed model is of high practical value.
Developing a Semantic-Driven Hybrid Segmentation Method for Point Clouds of 3D Shapes

With the rapid development of point cloud processing technologies and the availability of a wide range of 3D capturing devices, a geometric object from the real world can be directly represented digitally as a dense and fine point cloud. Decomposing a 3D shape represented in point cloud into meaningful parts has very important practical implications in the fields of computer graphics, virtual reality and mixed reality. In this paper, a semantic-driven automated hybrid segmentation method is proposed for 3D point cloud shapes. Our method consists of three stages: semantic clustering, variational merging, and region remerging. In the first stage, a new feature of point cloud, called Local Concave-Convex Histogram, is introduced to first extract saddle regions complying with the semantic boundary feature. All other types of regions are then aggregated according to this extracted feature. This stage often leads to multiple over-segmentation convex regions, which are then remerged by a variational method established based on the narrow-band theory. Finally, in order to recombine the regions with the approximate shapes, order relation is introduced to improve the weighting forms in calculating the conventional Shape Diameter Function. We have conducted extensive experiments with the Princeton Dataset. The results show that the proposed algorithm outperforms the state-of-the-art algorithms in this area. We have also applied the proposed algorithm to process the point cloud data acquired directly from the real 3D objects. It achieves excellent results too. These results demonstrate that the method proposed in this paper is effective and universal.
Estimation of Air Temperature from FY-4A AGRI Data: A Comparison of Different Machine Learning Algorithm

Air Temperature(Tair),a basic meteorological observation element, is an essential meteorological parameter in physiology, hydrology, meteorology, environment, etc. The Tair data ,which is characterized by high precision, is of great significance for the greenhouse effect, land surface processes and so on. With the advent of high performing imaging instrument onboard geostationary satellites such as Advanced Geostationary Radiation Imager(AGRI) onboard FY-4A of China, it provides high spatial and temporal resolution data. To estimate Tair from such high-resolution data, this paper presents an effective method for estimation Tair based on AGRI data. Different machine learning algorithms–-random forest (RF), k-nearest neighbors(KNN) and extreme gradient boosting(XGB)–-are evaluated for estimation of Tair under clear sky conditions in the Southwest of China. For the training dataset, the two infrared brightness temperatures of AGRI (BT12 and BT13), digital elevation model(DEM), latitude and longitude, surface pressure, time and relative humidity(RH) are selected. The Tair data obtained by National Centers for Environmental Information(NCEI), evaluates different machine learning algorithm performance in the Southwest of China. The results show that the performance of the XGB model is better than RF and KNN with a correlation coefficient (R) of 0.977, a mean bias of -0.036□,and the root mean square error (RMSE) of 1.266□.