Miracle-dz/UniFAR-FTUs huggingface dataset:
A Benchmark Study by using various Machine Learning Models for Predicting Covid-19 trends

Machine learning and deep learning play vital roles in predicting diseases in the medical field. Machine learning algorithms are widely classified as supervised, unsupervised, and reinforcement learning. This paper contains a detailed description of our experimental research work in that we used a supervised machine-learning algorithm to build our model for outbreaks of the novel Coronavirus that has spread over the whole world and caused many deaths, which is one of the most disastrous Pandemics in the history of the world. The people suffered physically and economically to survive in this lockdown. This work aims to understand better how machine learning, ensemble, and deep learning models work and are implemented in the real dataset. In our work, we are going to analyze the current trend or pattern of the coronavirus and then predict the further future of the covid-19 confirmed cases or new cases by training the past Covid-19 dataset by using the machine learning algorithm such as Linear Regression, Polynomial Regression, K-nearest neighbor, Decision Tree, Support Vector Machine and Random forest algorithm are used to train the model. The decision tree and the Random Forest algorithm perform better than SVR in this work. The performance of SVR and lasso regression are low in all prediction areas Because the SVR is challenging to separate the data using the hyperplane for this type of problem. So SVR mostly gives a lower performance in this problem. Ensemble (Voting, Bagging, and Stacking) and deep learning models(ANN) also predict well. After the prediction, we evaluated the model using MAE, MSE, RMSE, and MAPE. This work aims to find the trend/pattern of the covid-19.
Location-based Activity Behavior Deviation Detection for Nursing Home using IoT Devices

With the advancement of the Internet of Things(IoT) and pervasive computing applications, it provides a better opportunity to understand the behavior of the aging population. However, in a nursing home scenario, common sensors and techniques used to track an elderly living alone are not suitable. In this paper, we design a location-based tracking system for a four-story nursing home - The Salvation Army, Peacehaven Nursing Home in Singapore. The main challenge here is to identify the group activity among the nursing home's residents and to detect if they have any deviated activity behavior. We propose a location-based deviated activity behavior detection system to detect deviated activity behavior by leveraging data fusion technique. In order to compute the features for data fusion, an adaptive method is applied for extracting the group and individual activity time and generate daily hybrid norm for each of the residents. Next, deviated activity behavior detection is executed by considering the difference between daily norm patterns and daily input data for each resident. Lastly, the deviated activity behavior among the residents are classified using a rule-based classification approach. Through the implementation, there are 44.4% of the residents do not have deviated activity behavior , while 37% residents involved in one deviated activity behavior and 18.6% residents have two or more deviated activity behaviors.
Oscillating nuclear charge radii as sensors for ultralight dark matter

We show that coupling of ultralight dark matter (UDM) to quarks and gluons would lead to an oscillation of the nuclear charge radius for both the quantum chromodynamics (QCD) axion and scalar dark matter. Consequently, the resulting oscillation of electronic energy levels could be resolved with optical atomic clocks, and their comparisons can be used to investigate UDM-nuclear couplings, which were previously only accessible with other platforms. We demonstrate this idea using the ${}^2S_{1/2} (F=0)\leftrightarrow {}^2F_{7/2} (F=3)$ electric octupole and ${}^2S_{1/2} (F=0)\leftrightarrow \,{}^2D_{3/2} (F=2)$ electric quadrupole transitions in ${}^{171}Yb^+$. Based on the derived sensitivity coefficients for these two transitions and a long-term comparison of their frequencies using a single trapped ${}^{171}Yb^+$ ion, we find bounds on the scalar UDM-nuclear couplings and the QCD axion decay constant. These results are at a similar level compared to the tightest spectroscopic limits, and future investigations, also with other optical clocks, promise significant improvements.
Magnetorestictive amorphous sensor for biomedical monitoring

A novel sensor, very simple and inexpensive to manufacture, and with adjustable sensitivity, was developed for the monitoring of articular movements and lung ventilation. This sensor makes use of the change in permeability with respect to mechanical stress or strain. If an amorphous ribbon is glued on a plastic ribbon which shows considerably higher thickness, bending this compound sensor will cause plain tensile stress or compression in the amorphous magnetostrictive material. In the same way, using a prebent plastic ribbon yields an effective sensor for elongation in the connecting line of the sandwich ends. >
Efficient low-energy single-electron detection using a large-area superconducting microstrip

Superconducting strip single-photon detectors (SSPDs) are excellent tools not only for single-photon detection but also for single-particle detection owing to their high detection eﬃciency, low dark counts, and low time jitter. Although the detection of various particles, including electrons with keV-scale energy, has been reported so far, there have been no studies for detecting low-energy electrons. It has yet to be clariﬁed how low-energy electrons interact with electrons and/or phonons in a superconductor during electron detection. Here we report the detection property of a superconducting micro-strip single-electron detector (SSED) for electrons with energy below 200 eV. The detection eﬃciency is estimated as at least 37 % when electrons impinging on the stripline possess an energy of 200 eV. We also show that the minimum detectable energy of electrons is about 10 eV with our SSED, much lower than those of ions, which implies that the electron-electron interaction plays a signiﬁcant role. SSEDs might open a wide range of applications, from condensed matter physics to quantum information science, because of their compatibility with the cryogenic environment.
Sequential State and Variance Estimation within the Ensemble Kalman Filter

Abstract Kalman filter methods for real-time assimilation of observations and dynamical systems typically assume knowledge of the system parameters. However, relatively little work has been done on extending state estimation procedures to include parameter estimation. Here, in the context of the ensemble Kalman filter, a Monte Carlo–based algorithm is proposed for sequential estimation of the states and an unknown scalar observation variance. A Bayesian approach is adopted that yields analytical updating of the parameter distribution and provides samples from the posterior distribution of the states and parameters. The proposed assimilation algorithm extends standard ensemble methods, including perturbed observations, and serial and square root assimilation schemes. The method is illustrated on the Lorenz 40-variable system and is shown to be robust with system nonlinearities, sparse observation networks, and the choice of the initial parameter distribution.
A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation

—Inertial sensor-based human activity recognition (HAR) is the base of many human-centered mobile applications. Deep learning-based ﬁne-grained HAR models enable accurate classiﬁcation in various complex application scenarios. Nevertheless, the large storage and computational overhead of the existing ﬁne-grained deep HAR models hinder their widespread deployment on resource-limited platforms. Inspired by the knowl- edge distillation’s reasonable model compression and potential performance improvement capability, we design a multi-level HAR modeling pipeline called Stage-Logits-Memory Distillation (SMLDist) based on the widely-used MobileNet. By paying more attention to the frequency-related features during the distillation process, the SMLDist improves the HAR classiﬁcation robustness of the students. We also propose an auto-search mechanism in the heterogeneous classiﬁers to improve classiﬁcation performance. Extensive simulation results demonstrate that SMLDist outperforms various state-of-the-art HAR frameworks in accuracy and F1 macro score. The practical evaluation of the Jetson Xavier AGX platform shows that the SMLDist model is both energy- efﬁcient and computation-efﬁcient. These experiments validate the reasonable balance between the robustness and efﬁciency of the proposed model. The comparative experiments of knowledge distillation on six public datasets also demonstrate that the SML- Dist outperforms other advanced knowledge distillation methods of students’ performance, which veriﬁes the good generalization of the SMLDist on other classiﬁcation tasks, including but not limited to HAR.
Pre‐processing procedures for raw point clouds from terrestrial laser scanners

The use of geometric primitives such as geometric curvature, variances of curvature and surface normal vectors as pre‐processing methods for edge and boundary detection in three‐dimensional (3D) unorganised point clouds is proposed. These processes are important for high‐level procedures such as registration, segmentation, classification and detection of specific shapes or objects in point clouds. To demonstrate the effectiveness of these methods, we present examples of tree detection and segmentation of terrestrial laser scanner point clouds.
Rapidity distribution within the defocusing non-linear Schrödinger equation model

We consider the classical field integrable system whose evolution equation is the nonlinear Schrödinger equation with defocusing non-linearities, which is the classical limit of the quantum Lieb-Liniger model. We propose a simple derivation of the relation between two sets of conserved quantities: on the one hand the trace of the monodromy matrix, parameterized by the spectral parameter and introduced in the inverse-scattering framework, and on the other hand the rapidity distribution, a concept imported from the Lieb-Liniger model. To do so we use the definition of the rapidity distribution as the asymptotic momentum distribution after a very large expansion. We propose two different ways to derive the result, each one using a thought experiment that implements an expansion.
Joint action loss for proximal policy optimization

PPO (Proximal Policy Optimization) is a state-of-the-art policy gradient algorithm that has been successfully applied to complex computer games such as Dota 2 and Honor of Kings. In these environments, an agent makes compound actions consisting of multiple sub-actions. PPO uses clipping to restrict policy updates. Although clipping is simple and effective, it is not efficient in its sample use. For compound actions, most PPO implementations consider the joint probability (density) of sub-actions, which means that if the ratio of a sample (state compound-action pair) exceeds the range, the gradient the sample produces is zero. Instead, for each sub-action we calculate the loss separately, which is less prone to clipping during updates thereby making better use of samples. Further, we propose a multi-action mixed loss that combines joint and separate probabilities. We perform experiments in Gym-$\mu$RTS and MuJoCo. Our hybrid model improves performance by more than 50\% in different MuJoCo environments compared to OpenAI's PPO benchmark results. And in Gym-$\mu$RTS, we find the sub-action loss outperforms the standard PPO approach, especially when the clip range is large. Our findings suggest this method can better balance the use-efficiency and quality of samples.
Adults in the room? The auditor and dividends in small firms: Evidence from a natural experiment

We examine the effect of auditing on dividends in small private firms. We hypothesize that auditing can constrain dividends by way of promoting accounting conservatism. We use register data on private Norwegian firms and random variation induced by the introduction of a policy allowing small private firms to forgo the use of an auditor to estimate the effect of auditing on dividend payout. Identification is obtained by a regression discontinuity around the arbitrary thresholds for the policy. Propensity score matching is used to create a balanced synthetic control. We consistently find that forgoing auditing led to a significant increase in dividends in small private firms.
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning

Off-policy learning from multistep returns is crucial for sample-efficient reinforcement learning, but counteracting off-policy bias without exacerbating variance is challenging. Classically, off-policy bias is corrected in a per-decision manner: past temporal-difference errors are re-weighted by the instantaneous Importance Sampling (IS) ratio after each action via eligibility traces. Many off-policy algorithms rely on this mechanism, along with differing protocols for cutting the IS ratios to combat the variance of the IS estimator. Unfortunately, once a trace has been fully cut, the effect cannot be reversed. This has led to the development of credit-assignment strategies that account for multiple past experiences at a time. These trajectory-aware methods have not been extensively analyzed, and their theoretical justification remains uncertain. In this paper, we propose a multistep operator that can express both per-decision and trajectory-aware methods. We prove convergence conditions for our operator in the tabular setting, establishing the first guarantees for several existing methods as well as many new ones. Finally, we introduce Recency-Bounded Importance Sampling (RBIS), which leverages trajectory awareness to perform robustly across $\lambda$-values in an off-policy control task.
New Approach to Malware Detection Using Optimized Convolutional Neural Network

Cyber-crimes have become a multi-billion-dollar industry in the recent years. Most cybercrimes/attacks involve deploying some type of malware. Malware that viciously targets every industry, every sector, every enterprise and even individuals has shown its capabilities to take entire business organizations offline and cause significant financial damage in billions of dollars annually. Malware authors are constantly evolving in their attack strategies and sophistication and are developing malware that is difficult to detect and can lay dormant in the background for quite some time in order to evade security controls. Given the above argument, Traditional approaches to malware detection are no longer effective. As a result, deep learning models have become an emerging trend to detect and classify malware. This paper proposes a new convolutional deep learning neural network to accurately and effectively detect malware with high precision. This paper is different than most other papers in the literature in that it uses an expert data science approach by developing a convolutional neural network from scratch to establish a baseline of the performance model first, explores and implements an improvement model from the baseline model, and finally it evaluates the performance of the final model. The baseline model initially achieves 98% accurate rate but after increasing the depth of the CNN model, its accuracy reaches 99.183 which outperforms most of the CNN models in the literature. Finally, to further solidify the effectiveness of this CNN model, we use the improved model to make predictions on new malware samples within our dataset.
Train Hard, Fight Easy: Robust Meta Reinforcement Learning

A major challenge of reinforcement learning (RL) in real-world applications is the variation between environments, tasks or clients. Meta-RL (MRL) addresses this issue by learning a meta-policy that adapts to new tasks. Standard MRL methods optimize the average return over tasks, but often suffer from poor results in tasks of high risk or difficulty. This limits system reliability since test tasks are not known in advance. In this work, we define a robust MRL objective with a controlled robustness level. Optimization of analogous robust objectives in RL is known to lead to both *biased gradients* and *data inefficiency*. We prove that the gradient bias disappears in our proposed MRL framework. The data inefficiency is addressed via the novel Robust Meta RL algorithm (RoML). RoML is a meta-algorithm that generates a robust version of any given MRL algorithm, by identifying and over-sampling harder tasks throughout training. We demonstrate that RoML achieves robust returns on multiple navigation and continuous control benchmarks.
Cross modal global local representation learning from radiology reports and x-ray chest images

Deep learning models can be applied successfully in real-work problems; however, training most of these models requires massive data. Recent methods use language and vision, but unfortunately, they rely on datasets that are not usually publicly available. Here we pave the way for further research in the multimodal language-vision domain for radiology. In this paper, we train a representation learning method that uses local and global representations of the language and vision through an attention mechanism and based on the publicly available Indiana University Radiology Report (IU-RR) dataset. Furthermore, we use the learned representations to diagnose five lung pathologies: atelectasis, cardiomegaly, edema, pleural effusion, and consolidation. Finally, we use both supervised and zero-shot classifications to extensively analyze the performance of the representation learning on the IU-RR dataset. Average Area Under the Curve (AUC) is used to evaluate the accuracy of the classifiers for classifying the five lung pathologies. The average AUC for classifying the five lung pathologies on the IU-RR test set ranged from 0.85 to 0.87 using the different training datasets, namely CheXpert and CheXphoto. These results compare favorably to other studies using UI-RR. Extensive experiments confirm consistent results for classifying lung pathologies using the multimodal global local representations of language and vision information.
Full trajectory optimizing operator inference for reduced-order modeling using differentiable programming

Accurate and computationally inexpensive Reduced Order Models (ROMs) capable of forecasting turbulent ﬂows can facilitate rapid design iterations and thus prove critical for predictive control strategies in engineering problems. Galerkin projection based Reduced Order Models (GP-ROMs), derived by projecting the Navier-Stokes equations on a truncated Proper Orthogonal Decomposition (POD) basis, are popular because of their low computational costs and theoretical foundations in the governing ﬂuid ﬂow equations. However, the accuracy of traditional GP-ROMs degrades over long time prediction horizons. While calibration can somewhat mitigate this issue, the procedure is cumbersome and requires insight into the full order solver. To address these issues, we extend the recently proposed Neural Galerkin Projection (NeuralGP) data driven framework to compressibility-dominated transonic ﬂow, considering a prototypical problem of a buﬀeting NACA0012 airfoil governed by the full Navier-Stokes equations. The algorithm maintains the form of the ROM-ODE obtained from the Galerkin projection; however coeﬃcients are learned directly from the data using gradient descent facilitated by diﬀerentiable programming. This eﬀectively blends the strengths of the physics driven GP-ROM and purely data driven neural network-based techniques, resulting in a computationally cheaper model that is easier to interpret. While both NeuralGP and traditional GP-ROMs yield accurate forecasts over short time prediction horizons, the former accurately tracks the reference POD coeﬃcients over longer prediction horizons, in time ranges where the calibrated GP-ROM displays the tendency to diverge in amplitude and frequency. We show that the distinct long-term behavior of the blended NeuralGP method minimizes a more rigorous full trajectory error norm compared to a linearized error deﬁnition optimized by the calibration procedure. We also ﬁnd that while both procedures stabilize the ROM by displacing the eigenvalues of the linear dynamics matrix of the ROM-ODE to the complex left half-plane, the NeuralGP algorithm adds more dissipation
On unifying randomized methods for inverse problems

This work unifies the analysis of various randomized methods for solving linear and nonlinear inverse problems with Gaussian priors by framing the problem in a stochastic optimization setting. By doing so, we show that many randomized methods are variants of a sample average approximation (SAA). More importantly, we are able to prove a single theoretical result that guarantees the asymptotic convergence for a variety of randomized methods. Additionally, viewing randomized methods as an SAA enables us to prove, for the first time, a single non-asymptotic error result that holds for randomized methods under consideration. Another important consequence of our unified framework is that it allows us to discover new randomization methods. We present various numerical results for linear, nonlinear, algebraic, and PDE-constrained inverse problems that verify the theoretical convergence results and provide a discussion on the apparently different convergence rates and the behavior for various randomized methods.
Interpreting Knowledge-based Programs (Extended Version with Proofs)

Knowledge-based programs specify multi-agent protocols with epistemic guards that abstract from how agents learn and record facts or information about other agents and the environment. Their interpretation involves a non-monotone mutual dependency between the evaluation of epistemic guards over the reachable states and the derivation of the reachable states depending on the evaluation of epistemic guards. We apply the technique of a must/cannot analysis invented for synchronous programming languages to the interpretation problem of knowledge-based programs and demonstrate that the resulting constructive interpretation is monotone and has a least fixed point. We relate our approach with existing interpretation schemes for both synchronous and asynchronous programs. Finally, we describe an implementation of the constructive interpretation and illustrate the procedure by several examples and an application to the Java memory model.
Optimisation of Power Grid Stability Under Uncertainty

The increased integration of intermittent and decentralised forms of power production has eroded the stability margins of power grids and made it more challenging to ensure reliable and secure power transmission. Reliable grid operation requires system-scale stability in response to perturbations in supply or load; previous studies have shown that this can be achieved by tuning the eﬀective damping parameters of the generators in the grid. In this paper, we present and analyse the problem of tuning damping parameters when there is some uncertainty in the underlying system. We show that sophisticated methods that assume no uncertainty can yield results that are less robust than those produced by simpler methods. We deﬁne a quantile-based metric of stability that ensures that power grids remain stable even as worst-case scenarios are approached, and we develop optimisation methods for tuning damping parameters to achieve this stability. By comparing optimisation methods that rely on diﬀerent assumptions, we suggest eﬃcient heuristics for ﬁnding parameters that achieve highly stable and robust grids.