Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting the effects of kinetic impactors on asteroid deflection using end-to-end deep learning

One possible approach to deflect the trajectory of an asteroid on a collision course with the Earth, and prevent a potentially devastating impact, is the use of a kinetic impactor. The upcoming NASA DART and ESA Hera space missions will be the first to study and demonstrate this technique, by driving a spacecraft into the moon of a binary asteroid system with the aim of altering its momentum, and knocking it off course. In this work, we seek to predict critical parameters associated with such an impact, namely the momentum transfer efficiency and axial ratio of the target body, based on light curve data observed from ground before and after the impact in order to give insights into the real effect of the deflection effort. We present here our approach to this problem, which we address from a purely data-driven perspective based on simulated data provided as a part of the Andrea Milani Planetary Defence Challenge, organised by the EU H2020 Stardust-R research network in conjunction with ESA. Formulating the problem as a time series regression task, we develop an end-to-end deep learning pipeline in which we apply the latest advances in deep learning for time series, such as the use of the Transformer architecture as well as ensembling and self-supervised learning techniques. Exploiting these techniques for the challenge, we achieved second place out of the student teams, and fifth place overall without relying on any a priori knowledge of the physics of the asteroid system.
Short-term Power Load Forecasting based on Feature Fusion of Parallel LSTM-CNN

Accurate power load forecasting is beneficial to grid scheduling and operation. In order to fully exploit the potential relationship between load and feature. This research suggests a short-term power load forecasting strategy based on feature fusion to increase prediction accuracy, which combines parallel LSTM and CNN. The model first determines the optimal features of the model input through the random forest method and mutual information method, then classifies the features, and inputs the classified categories into the parallel LSTM modules respectively, and then uses the CNN network to output the neural network of different LSTM modules at the same time. The elements are convolved to extract the features of each moment, finally get the prediction result through the fully connected layer. The results of two short-term power load forecasting cases show that the proposed method's prediction effect outclasses existing methods. with model evaluation parameter MAPE of 1.367% and 0.974% respectively.
Combining Drone-based Monitoring and Machine Learning for Online Reliability Evaluation of Wind Turbines

The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to aid in the maintenance process and thereby to improve the availability of the farms. However, much of the reliability analysis is performed offline based on statistical data. In this article, we propose a drone-assisted monitoring based method for online reliability evaluation of wind turbines. A blade system of a wind turbine is used as an illustrative example to demonstrate the proposed approach.
A GENTLER Approach to Urban Traffic Modelling and Prediction

Intelligent Transportation aims to usher in a new and improved version of motorised traffic, one that is stream-lined, safe and at the heart of the net-zero agenda. Designing and building the urban infrastructure necessary to turn that vision into a reality relies on complex decision making, which often hinges on estimating the dynamics of future traffic through areas of the road network that are yet to be built. Traffic models capable of yielding such estimations, robustly and reliably, are valuable technological tools that urban planners can utilise to inform their decisions. To that end, we propose a novel algorithm that employs Genetic Programming and Transfer Learning to produce traffic models which accurately predict vehicle flow through a given junction based on readings collected from sur-rounding areas. We enhance the algorithm with a randomisation mechanism and run a comprehensive experimental study on a segment of the city of Darmstadt's road network, in order to investigate the effects of the exploration-exploitation interplay on the generated models' prediction accuracy.
Asymptotic analysis of the linearized Navier-Stokes equations in a general 2D domain

We continue our study of the asymptotic behavior of the Navier-Stokes equations linearized around the rest state as viscostiy " approaches zero. We study the convergence as "! 0 to the inviscid type equations. Suitable correctors are obtained which resolve the boundary layer and we obtain convergence results valid up to the boundary. Explicit asymptotic expansion formulas are given which display the boundary layer phenomena. We improve our previous by treating here the general smooth bounded domain in R 2 instead of two-dimensional channels. Curvilinear coordinates are used to resolve the complex geometry.
Nantong Blue Calico Image Dataset and Its Recognition

Nantong blue calico is a kind of important intangible cultural heritages in China. To better safeguard and inherit it in a digital way, it is necessary to construct a large-scale dataset for Nantong blue calico. As so far, however, we could not find a public dataset for blue calico. The goal of this paper is to give a public image dataset which named $N$ tBC consisting of Nantong blue calico patterns and provide a baseline result for the recognition of Nantong blue calico patterns. In this paper, we perform several baseline experiments on the NtBC dataset, including handcrafted and deep feature based classification methods. we compare some handcrafted methods and four kinds of popular convolutional neural networks (CNNs), including ResNet-50, AlexNet, GoogLeNet-V1 and VGGNet-16. Experimental results show that ResNet-50 yields an accuracy of 93.8% in the recognition performance, which shows that it is efficient to classify blue calico patterns through deep learning methods. As a consequence, this result provides the current best baseline result for Nantong blue calico image recognition. We believe our $N$ tBC will facilitate future research on Chinese traditional patterns development, fine grained visual classification, and imbalanced learning fields. We make the dataset and pre-trained models publicly available at https://github.com/facebook/react.
A Fast and Compact Hybrid CNN for Hyperspectral Imaging-based Bloodstain Classification

In forensic sciences, blood is a shred of essential evidence for reconstructing crime scenes. Blood identification and classification may help to confirm a suspect, although several chemical processes are used to recreate the crime scene. However, these approaches can have an impact on DNA analysis. A potential application of bloodstain identification and classification using Hyperspectral Imaging (HSI) can be used as substance clas-sification in forensic science for crime scene analysis. Therefore, this work proposes the use of a fast and compact Hybrid CNN to process HSI data for bloodstain identification and classification. For experimental and validation purposes, we perform exper-iments on a publicly available Hyperspectral-based Bloodstain dataset. This dataset has different types of substances i.e., blood and blood-like compounds, for instance, ketchup, artificial blood, beetroot juice, poster paint, tomato concentrate, acrylic paint, uncertain blood. We compare the results with state-of-the-art 3D CNN model and examine the results in detail and present a discussion of each tested architecture with limited availability of the training samples (e.g., only 5 % (792 samples) of the data samples are used to train the model, and validated on 5 % (792 samples) data samples and finally blindly tested on 90 % (14260 samples) of the data samples). The source code can be access on https://github.com/MHassaanButt/FCHCNN-for-HSIC
Hybrid Tourism Recommendation System: A Multi-Objective Perspective

A smart recommendation method can greatly improve tourists' travel experience, and it is an important task for tourism recommendation systems to intelligently suggest scenic spots for tourists according to their historical visit records. Currently, the collaborative filtering and deep neural network-based methods occupy the mainstream of tourism recommendation systems. Although each type of recommendation methods is superior over the others in terms of different aspects, the performance of a single recommendation method is limited. In order to inherit the advantages of different types of recommendation methods, this work suggests a hybrid method for assembling multiple methods for tourism recommendation. Based on the scenic spots obtained by multiple recommendation methods, the proposed hybrid method uses two novel objectives to evaluate each scenic spot, and identifies the best $K$ scenic spots via the techniques used in evolutionary multi-objective optimization. In comparison to existing recommendation methods and hybrid methods, the proposed hybrid method exhibits better performance on two public tourism datasets and a new dataset created based on the tourism information of Huangshan City.
Optimizing Mixed-Asset Portfolio With Real Estate: Why Price Predictions?

—The main purpose of portfolio optimization is to reduce the risk, and/or maximize the return of a group of investments. Most of the works that have been done on port- folio optimization are based on the Modern Portfolio Theory introduced by Markowitz in 1959. Some of them have employed price predictions to compute optimal asset weights. It has been demonstrated that using price predictions, instead of historical data, might improve portfolio performance under a risk-adjusted perspective. However, contributions in the field mainly focused on stocks, while little attention has been given on multi-asset portfolios including real estate. In this paper, we fill this gap by running a genetic algorithm on 456 portfolios to demonstrate the added value of including price predictions in our asset allocation problem. To investigate this, we compare the theoretical case of having a perfect foresight, where the predicted price p t is exactly the same as the expected price p t ; under this case, the portfolio optimization task takes place in the test set (since we have assumed a perfect price prediction). We compare the results under perfect foresight with results derived from portfolio optimization that only took place in the training set, and the weights were then directly applied to the test set. Our goal is to demonstrate the theoretical advantages of using price predictions on mixed-asset portfolios that include real estate. Our results show that there can be significant improvements (up to 45%) in sharpe ratio, rate of return, and risk, when using price predictions instead of a historical prices based portfolio.
Efficient quantum algorithm for factorization

This article presents an algorithm for fast factorization on quantum computer. Quantum mechanic introduces to information processing new effects that can be used to solve efficiently problems classical known as hard. Simon algorithm was one of the first proofs that quantum computer has some advantages over classical model of computation. Shor generalized ideas introduce by Simon and show that real world problems are in range of quantum computation. Article presents also general description of quantum Fourier transform (QFT), which is without doubt the most powerful tool used in algorithms described in this paper. Generalization of algorithms for Simon problem and fast factorization gives new class of problems for which fast quantum algorithms can be construct.
Automatic verification of finite state machines using temporal logic

Finite state machines are common components of VLSI circuits. Because they occur so frequently, much effort has been spent building tools to assist in their design and verification. Since the proper sequencing of events is of paramount importance to the correct operation of a finite state machine, it is not surprising that temporal logics have been applied to the verification problem. These logics were originally developed by philosophers specifically for reasoning about the ordering of events in time. One of the first applications was the CTL model checking procedure developed by Clarke and Emerson, in which the truth of a formula of a branching-time temporal logic in a labelled state transition graph could be determined in time that was linear in both the size of the structure and the length of the formula. Although this algorithm has been shown to be very useful in the verification of several hardware controllers, it suffers from a number of serious drawbacks. These deficiencies include a notion of a finite state machine that does not permit external events to effect transitions, a lack of compositionality, and an inability to generalize between similar systems of different sizes. In this thesis, a number of alternative techniques are proposed to solve these problems. In particular, a new model checking algorithm that can deal with conditional transitions is introduced and analyzed. A theory is presented concerning the equivalence of finite state machines with respect to the preservation of temporal logic properties. Finally, this theory is used to develop a method to generalize between systems composed of differing numbers of similar finite state machines.
Thermal-Aware Design for Approximate DNN Accelerators

Recent breakthroughs in Neural Networks (NNs) have made DNN accelerators ubiquitous and led to an ever-increasing quest on adopting them from Cloud to edge computing. However, state-of-the-art DNN accelerators pack immense computational power in a relatively confined area, inducing significant on-chip power densities that lead to intolerable thermal bottlenecks. Existing state of the art focuses on using approximate multipliers only to trade-off efficiency with inference accuracy. In this work, we present a thermal-aware approximate DNN accelerator design in which we additionally trade-off approximation with temperature effects towards designing DNN accelerators that satisfy tight temperature constraints. Using commercial multi-physics tool flows for heat simulations, we demonstrate how our thermal-aware approximate design reduces the temperature from 139<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="zervakis-ieq1-3141054.gif"/></alternatives></inline-formula> C, in an accurate circuit, down to 79<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="zervakis-ieq2-3141054.gif"/></alternatives></inline-formula> C. This enables DNN accelerators to fulfill tight thermal constraints, while still maximizing the performance and reducing the energy by around 75% with a negligible accuracy loss of merely 0.44% on average for a wide range of NN models. Furthermore, using physics-based transistor aging models, we demonstrate how reductions in voltage and temperature obtained by our approximate design considerably improve the circuit’s reliability. Our approximate design exhibits around 40% less aging-induced degradation compared to the baseline design.
OAMP for Low Complexity Detection in Large-Scale LDS Systems

This paper focuses on the low-complexity multiuser detection of coded low-density signature (LDS) systems where the numbers of resources and users are both large. Typically, the detection complexity using the conventional message passing algorithm grows exponentially with the number of users occupied at each resource, making it unaffordable for large-scale LDS systems. To address this problem, we propose to apply orthogonal approximate message passing (OAMP) to detect LDS symbols with polynomial complexity. The numerical results demonstrate the superiority of the proposed method in terms of the error performance over the traditional turbo receiver.
Research on Algorithm of Broken Wire Detection of Elevator Rope based on CCD Vision

It will cause serious casualties for passengers, if the traction wire rope is broken in frequent accidents about elevator, therefore, it is very important to monitor whether the traction wire rope is broken in real time. This paper proposes a method to Monitor whether the elevator’s traction wire rope is broken by the detection algorithm of CCD vision. Collect the image information of the traction wire rope in real time through the CCD camera, and it is for the collected image through performing median filtering, the segmentation of binarization threshold, and the edge extraction of Canny operator and we can obtain the edge information of the traction wire rope through the axis’s projection algorithm or the area Difference algorithm, and it will compare the calculated result with the standard error. The traction wire rope is working normally when the comparison result does not exceed the value of allowable error, otherwise it is judged that the traction wire rope is broken. Experimental results show that the algorithm can detect Whether the elevator’s traction wire rope is broken in real time.
Consume Machine Learning Integration Algorithms to Predict COVID-19 Infection

In the context of the emerging coronavirus pneumonia epidemic becoming a global epidemic, nucleic acid testing as a as a precise prevention and control method has been universally recognized, but because the scope of the test is too big and the production process is complicated, the kits produced by biological companies are difficult to use widely, for this reason I develop some machine learning integrated algorithms which can forecast whether a man is infected with COVID-19 based on three highly accessible features. This method can predict whether a person has been infected with COVID-19 based only on three indicators: heart rate, blood oxygen level, and body surface temperature, and we use several tree integration. We used several tree integration algorithms such as Random Forest, XGBoost, and GBM, and its accuracy, recall, and F1 score obtained 100% accuracy on the test set, which has been better than the current nucleic acid detection methods, proving that this method can be theoretically used as an accurate, convenient, and efficient self-detection method.
Evolving Classification Rules for Predicting Hypoglycemia Events

People with diabetes have to properly manage their blood glucose levels in order to avoid acute complications. This is a difficult task and an accurate and timely prediction may be of vital importance, specially of extreme values. Perhaps one of the main concerns of people with diabetes is to suffer an hypoglycemia (low value) event and moreover, that the event will be prolonged in time. It is crucial to predict events of hyperglycemia (high value) and hypoglycemia that may cause health damages in the short term and potential permanent damages in the long term. The aim of this paper is to describe our research on predicting hypoglycemia events using Dynamic structured Grammatical Evolution. Our proposal gives white box models induced by a grammar based on if-then-else conditions. We trained and tested our system with real data collected from 5 different diabetic patients, producing 30 minutes predictions with encouraging results.
Supervised Multi-Objective Optimization Algorithm Using Estimation

This work proposes a supervised multi-objective optimization algorithm that assumes the existence of non-dominated solutions that serve as supervised data. In an expensive multi-objective optimization problem, it is required to obtain a solution set that approximates the Pareto front with an extremely small number of function evaluations. We often know some good solutions in advance when dealing with optimization problems. In this case, instead of generating solutions from scratch, generating solutions from known good solutions can be a shortcut for optimization. The proposed method estimates the Pareto front and Pareto set using the response surface methodology with existing non-dominated solutions as the supervised data. The proposed method selects a subset of objective vectors on the estimated Pareto front and obtains the subset as the solution set. Experimental results using DTLZ and WFG test suites show that the proposed method works well even with only ten non-dominated solutions and 150 function evaluations.
Scaling Invariance and Contingent Claim Pricing

[MAS R-9914] Prices of tradables can only be expressed relative to eachother at any instant of time. This fundamental fact should thereforealso hold for contingent claims, i.e. tradable instruments, whoseprices depend on the prices of other tradables. We show that thisproperty induces local scale-invariance in the problem of pricingcontingent claims. Due to this symmetry we do {it not/} require anymartingale techniques to arrive at the price of a claim. If thetradables are driven by Brownian motion, we find, in a natural way,that this price satisfies a PDE. Both possess a manifestgauge-invariance. A unique solution can only be given when we imposerestrictions on the drifts and volatilities of the tradables, i.e.the underlying market structure. We give some examples of theapplication of this PDE to the pricing of claims. In the Black-Scholesworld we show the equivalence of our formulation with the standardapproach. It is stressed that the formulation in terms of tradablesleads to a significant conceptual simplification of thepricing-problem. #[MAS R-9919] This article is the second one in a series on the use of scaling invariance in finance. In the first paper, we introduced a new formalism for the pricing of derivative securities, which focusses on tradable objects only, and which completely avoids the use of martingale techniques. In this article we show the use of the formalism in the context of path-dependent options. We derive compact and intuitive formulae for the prices of a whole range of well known options such as arithmetic and geometric average options, barriers, rebates and lookback options. Some of these have not appeared in the literature before. For example, we find rather elegant formulae for double barrier options with moving barriers, continuous dividends and all possible configurations of the barriers. The strength of the formalism reveals itself in the ease with which these prices can be derived. This allowed us to pinpoint some mistakes regarding geometric mean options, which frequently appear in the literature. Furthermore, symmetries such as put-call transformations appear in a natural way within the framework.