Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy Generalization for Optimal Operation of Processes with Large Operating Condition Changes

The proposed method in this paper deals with optimal operation. The objective is to handle optimality in case of variation in local approximation of optimization problem. If a disturbance moves the process far from the nominal point (large disturbance), the local model approximation used for the calculation of optimality by linearization of the nominal operating point and the local assumption of the quadratic cost function may be poor. In this paper the optimal operation is generalized to cover large changes in operating conditions by modelling with Takage-Sugeno (T-S) type fuzzy inference engine. For parameter tuning of this model, a hybrid GA (genetic algorithm)-LS (least square) algorithm is used. Also, the performance of the proposed method is studied in simulation of a nonquadratic objective function with large disturbance. The proposed fuzzy based method provided near optimal operation and showed very low loss for large magnitude of disturbances in compare with conventional method. Keywords— T-S fuzzy inference system; optimal operation; nonquadratic objective function; operating condition changes
Application of F-WASPAS in the Ranking of Crops for Agro-Processing: The Case of Ikondo Ward in Njombe, Tanzania

The purpose of this work was to develop and apply a Fuzzy Weighted Aggregated Sum Product Assessment (F-WASPAS) method in ranking selected crops for agro-processing at Ikondo Ward in Njombe Region, Tanzania. The fuzzy technique for order preferences by similarity to ideal solution (TOPSIS) was applied in determining the fuzzy importance weights of criteria, while the fuzzy WASPAS successfully ranked the crops, and maize was ranked the highest.
TRANSLATION QUALITY CONTROL IS MAINTAINED BY THE PENICILLIN RESISTANCE FACTOR MURM IN STREPTOCOCCUS PNEUMONIAE

TRANSLATION QUALITY CONTROL IS MAINTAINED BY THE PENICILLIN RESISTANCE FACTOR MURM IN STREPTOCOCCUS PNEUMONIAE Jennifer Shepherd and Michael Ibba* Department of Microbiology and Center for RNA Biology, Ohio State University, Columbus, Ohio 43210, USA. Running title: MurM functions in translation quality control Address correspondence to: Michael Ibba, Department of Microbiology, The Ohio State University, 484 West 12th Ave., Columbus, Ohio 43210. Tel. 614-292-2120; Fax. 614-292-8120; E-mail:ibba.1@osu.edu
Room Geometry Estimation from Acoustic Echoes

Estimating a room geometry using multiple microphones rises an echoes labeling problem. Two recent methods called the graph-based and the subspace-greedy methods have shown their capability in solving this problem. The graph-based method attains a good accuracy but suffers in maintaining the computational cost when the number of microphones is larger than 7. On the other hand, the subspacegreedy method provides suboptimal accuracy with much lower computational time. Here we construct the hybrid combination methods using those two baseline methods by interchanging their intermediate steps: the refinement step and the source localization step. To assess their practicability in a real-life application such as virtual reality games and robot navigation, the performance of these hybrid methods were tested against the close microphones arrangement on the sphere’s surface. However, this new microphones’ constellation brings up a low dimensional problem. To deal with this matter, we use the weighted least squares as the source localization procedure. Finally, experiments on synthetic squared distance data demonstrate the feasibility of all hybrid methods for estimating the room geometry with centimeter precision within seconds. Room Geometry Estimation from Acoustic Echoes My Subtitle
Arbitrage-free Asset Class Independent Volatility Surface Interpolation on Probability Space using Normed Call Prices

We consider arbitrage-free interpolation of arbitrage-free input data of European option prices. The method derived is independent of the underlying (equity, rates, FX, etc.). A particular contribution of the paper is that for the chosen coordinate system and a wide variety of interpolation methods, we prove that the method results in arbitrage-free interpolations. This result is achieved in two steps: first, we transform option prices to Normed Call Prices (NCP), which are independent of the application. Using NCPs as interpolants, the interpolation problem can be formulated independently of assumptions of deterministic rates. Besides, the constraints necessary for ensuring absence of arbitrage are much simpler in this interpolant system, as they do not involve any scaling at all. Having proposed our choice of the dependent variable, we turn to the independent variables, and transform the strike-maturity or moneyness-maturity coordinate system to a probability-maturity coordinate system, which results in an interpolation method called probability-space interpolation in maturity dimension on the proposed NCP system that we prove actually leads to arbitrage-free surfaces in both strike and maturity dimensions. The computational tractability is an added advantage of our interpolation scheme.
COMET: Clustering Observables Modelled by Emulated perturbation Theory

In this paper we present COMET, a Gaussian process emulator of the galaxy power spectrum multipoles in redshift-space. The model predictions are based on one-loop perturbation theory and we consider two alternative descriptions of redshift-space distortions: one that performs a full expansion of the real- to redshift-space mapping, as in recent effective field theory models, and another that preserves the non-perturbative impact of small-scale velocities by means of an effective damping function. The outputs of COMET can be obtained at arbitrary redshifts (up to $z \sim 3$), for arbitrary fiducial background cosmologies, and for a large parameter space that covers the shape parameters $\omega_c$, $\omega_b$, and $n_s$, as well as the evolution parameters $h$, $A_s$, $\Omega_K$, $w_0$, and $w_a$. This flexibility does not impair COMET's accuracy, since we exploit an exact degeneracy between the evolution parameters that allows us to train the emulator on a significantly reduced parameter space. While the predictions are sped up by at least two orders of magnitude, validation tests reveal an accuracy of $0.1\,\%$ for the monopole and quadrupole ($0.3\,\%$ for the hexadecapole), or alternatively, better than $0.25\,\sigma$ for all three multipoles in comparison to statistical uncertainties expected for the Euclid survey with a tenfold increase in volume. We show that these differences translate into shifts in mean posterior values that are at most of the same size, meaning that COMET can be used with the same confidence as the exact underlying models. COMET is a publicly available Python package that also provides the tree-level bispectrum multipoles in redshift-space and Gaussian covariance matrices.
ViP3D: End-to-End Visual Trajectory Prediction via 3D Agent Queries

Perception and prediction are two separate modules in the existing autonomous driving systems. They interact with each other via hand-picked features such as agent bounding boxes and trajectories. Due to this separation, prediction, as a downstream module, only receives limited information from the perception module. To make matters worse, errors from the perception modules can propagate and accumulate, adversely affecting the prediction results. In this work, we propose ViP 3D, a query-based visual trajectory prediction pipeline that exploits rich information from raw videos to directly predict future trajectories of agents in a scene. ViP3D employs sparse agent queries to detect, track, and predict throughout the pipeline, making it the first fully differentiable vision-based trajectory prediction approach. Instead of using historical feature maps and trajectories, useful information from previous timestamps is encoded in agent queries, which makes ViP3D a concise streaming prediction method. Furthermore, extensive experimental results on the nuScenes dataset show the strong vision-based prediction performance of ViP 3D over traditional pipelines and previous end-to-end models.11Code and demos are available on the project page: https://tsinghua-mars-lab.github. io/ViP3D
2022 A self-dual boundary phase transition of the 2d Z N topological order

Recently, a uniﬁed mathematical theory of the 1d gapped / gapless quantum phase has been developed. In particular, we have a powerful tool to study the topological phase transitions between 1d gapped boundaries of an anomaly-free topological order, whose critical point is a gappable non-chiral gapless edge. In this work, we will study a self-dual critical point of a pure boundary topological phase transition of the 2d Z N topological order by constructing an enriched fusion category that describes the gappable non-chiral gapless edge. And we construct a lattice model to recover the mathematical data of this enriched fusion category. The construction processfurthershow that the symmetryof the boundary isdeterminedby the topological sector in the bulk. It is a particular example of the boundary-bulk corresponding. The results in this paper can be generalized to the ﬁnite Abelian group.
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning

Vehicular edge computing (VEC) can learn and cache most popular contents for vehicular users (VUs) in the roadside units (RSUs) to support real-time vehicular applications. Federated learning (FL) can protect VUs' privacy by sharing vehicles' local models instead of data. In traditional FL, the global model is periodically updated by aggregating all vehicles' local models. However, vehicles may frequently drive out of the coverage area of VEC before they finish the local model training and thus the traditional FL cannot upload all local models as expected, which would degrade the accuracy of global model. The asynchronous FL can be performed without aggregating all vehicles' local models, thus more local models can be uploaded to improve the accuracy of global model. The vehicle mobility significantly impacts the asynchronous FL. There is no published work considering the vehicle mobility to design the cooperative caching in VEC based on asynchronous FL. In addition, the caching capacity of RSU is limited and the size of the predicted popular contents usually exceeds the cache capacity of RSU. Hence, VEC should cache the predicted popular contents in different RSUs while considering content transmission delay. In this paper, we consider vehicle mobility and propose a cooperative caching scheme in the VEC based on asynchronous federated and deep reinforcement learning (CAFR) to predict popular contents and further obtain the optimal cooperative caching location for the predicted popular contents. Extensive experimental results have demonstrated that CAFR scheme outperforms other baseline caching schemes.
Late Fusion Multi-view Clustering via Global and Local Alignment Maximization

Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in various applications, most of existing approaches directly fuse multiple pre-specified similarities to learn an optimal similarity matrix for clustering, which could cause over-complicated optimization and intensive computational cost. In this paper, we propose late fusion MVC via alignment maximization to address these issues. To do so, we first reveal the theoretical connection of existing k-means clustering and the alignment between base partitions and the consensus one. Based on this observation, we propose a simple but effective multi-view algorithm termed LF-MVC-GAM. It optimally fuses multiple source information in partition level from each individual view, and maximally aligns the consensus partition with these weighted base ones. Such an alignment is beneficial to integrate partition level information and significantly reduce the computational complexity by sufficiently simplifying the optimization procedure. We then design another variant, LF-MVC-LAM to further improve the clustering performance by preserving the local intrinsic structure among multiple partition spaces. After that, we develop two three-step iterative algorithms to solve the resultant optimization problems with theoretically guaranteed convergence. Further, we provide the generalization error bound analysis of the proposed algorithms. Extensive experiments on eighteen multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed LF-MVC-GAM and LF-MVC-LAM, ranging from small to large-scale data items. The codes of the proposed algorithms are publicly available at https://github.com/wangsiwei2010/latefusionalignment.
A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Application to COVID-19

Many approaches using compartmental models have been used to study the COVID-19 pandemic, with machine learning methods applied to these models having particularly notable success. We consider the Susceptible–Infected–Confirmed–Recovered–Deceased (SICRD) compartmental model, with the goal of estimating the unknown infected compartment I, and several unknown parameters. We apply a variation of a “Physics Informed Neural Network” (PINN), which uses knowledge of the system to aid learning. First, we ensure estimation is possible by verifying the model’s identifiability. Then, we propose a wavelet transform to process data for the network training. Finally, our central result is a novel modification of the PINN’s loss function to reduce the number of simultaneously considered unknowns. We find that our modified network is capable of stable, efficient, and accurate estimation, while the unmodified network consistently yields incorrect values. The modified network is also shown to be efficient enough to be applied to a model with time-varying parameters. We present an application of our model results for ranking states by their estimated relative testing efficiency. Our findings suggest the effectiveness of our modified PINN network, especially in the case of multiple unknown variables.
Mimicking Production Behavior with Generated Mocks

Mocking in the context of automated software tests allows testing program units in isolation. Designing realistic interactions between a unit and its environment, and understanding the expected impact of these interactions on the behavior of the unit, are two key challenges that software testers face when developing tests with mocks. In this paper, we propose to monitor an application in production to generate tests that mimic realistic execution scenarios through mocks. Our approach operates in three phases. First, we instrument a set of target methods for which we want to generate tests, as well as the methods that they invoke, which we refer to as mockable method calls. Second, in production, we collect data about the context in which target methods are invoked, as well as the parameters and the returned value for each mockable method call. Third, offline, we analyze the production data to generate test cases with realistic inputs and mock interactions. The approach is automated and implemented in an open-source tool called RICK. We evaluate our approach with 3 real-world, open-source Java applications. RICK monitors the invocation of 128 methods in production across the 3 applications and captures their behavior. Based on this captured data, RICK generates test cases that include realistic initial states and test inputs, mocks, and stubs. The three kinds of mock-based oracles generated by RICK verify the actual interactions between the method and its environment. All the generated test cases are executable, and 52.4% of them successfully mimic the complete execution context of the methods observed in production. The mock-based oracles are effective at detecting regressions within the target methods, complementing each other in their fault-finding ability. We interview 5 developers from the industry who confirm the relevance of using production observations to design mocks and stubs.
Modeling kilonova afterglows: Effects of the thermal electron population and interaction with GRB outflows

Given an increasing number of gamma-ray bursts accompanied by potential kilonovae there is a growing importance to advance modelling of kilonova afterglows. In this work, we investigate how the presence of two electron populations that follow a Maxwellian (thermal) and a power-law (non-thermal) distributions affect kilonova afterglow light curves. We employ semi-analytic afterglow model, PyBlastAfterglow. We consider kilonova ejecta profiles from ab-initio numerical relativity binary neutron star merger simulations, targeted to GW170817. We do not perform model selection. We find that the emission from thermal electrons dominates at early times. If the interstellar medium density is high (≃ 0.1 cm−3) it adds an early time peak to the light curve. As ejecta decelerates the spectral and temporal indexes change in a characteristic way that, if observed, can be used to reconstruct the ejecta velocity distribution. For the low interstellar medium density, inferred for GRB 170817A, the emission from the non-thermal electron population generally dominates. We also assess how kilonova afterglow light curves change if the interstellar medium has been partially removed and pre-accelerated by laterally expanding gamma-ray burst ejecta. For the latter we consider properties informed by observations of GRB170817A. We find that the main effect is the emission suppression at early time ≲ 103 days, and at its maximum it reaches ${\sim }40{{\%}}$ when the fast tail of the kilonova ejecta moves subsonically through the wake of laterally spreading gamma-ray burst ejecta. The subsequent rebrightening, when these ejecta break through and shocks form, is very mild (${\lesssim }10{{\%}}$), and may not be observable.
Learning to estimate a surrogate respiratory signal from cardiac motion by signal-to-signal translation

In this work, we develop a neural network-based method to convert a noisy motion signal generated from segmenting rebinned list-mode cardiac SPECT images, to that of a high-quality surrogate signal, such as those seen from external motion tracking systems (EMTs). This synthetic surrogate will be used as input to our pre-existing motion correction technique developed for EMT surrogate signals. In our method, we test two families of neural networks to translate noisy internal motion to external surrogate: 1) fully connected networks and 2) convolutional neural networks. Our dataset consists of cardiac perfusion SPECT acquisitions for which cardiac motion was estimated (input: center-of-count-mass - COM signals) in conjunction with a respiratory surrogate motion signal acquired using a commercial Vicon Motion Tracking System (GT: EMT signals). We obtained an average R-score of 0.76 between the predicted surrogate and the EMT signal. Our goal is to lay a foundation to guide the optimization of neural networks for respiratory motion correction from SPECT without the need for an EMT.
Testing and alignment of freeform-based multi-mirror telescopes

Three Mirror Anastigmat (TMA) systems including both on-axis and off-axis configurations have been widely used in space applications. In some designs, to correct for high order aberrations and realize large FOV, freeform surfaces are used to provide more design freedoms. This trend brings challenges to optical manufacturing and testing community. Since testing is critical to make high accurate aspheres and freeform surfaces, the paper addressed Computer Generated Hologram (CGH) design and implement to measure large freeform mirrors. In particular, CGH assisted alignment procedure for TMA telescopes were discussed in detail.
Detecting and Characterizing Propagation of Security Weaknesses in Puppet-Based Infrastructure Management

Despite being beneficial for managing computing infrastructure automatically, Puppet manifests are susceptible to security weaknesses, e.g., hard-coded secrets and use of weak cryptography algorithms. Adequate mitigation of security weaknesses in Puppet manifests is thus necessary to secure computing infrastructure that are managed with Puppet manifests. A characterization of how security weaknesses propagate and affect Puppet-based infrastructure management, can inform practitioners on the relevance of the detected security weaknesses, as well as help them take necessary actions for mitigation. We conduct an empirical study with 17,629 Puppet manifests with Taint Tracker for Puppet Manifests (TaintPup). We observe 2.4 times more precision, and 1.8 times more F-measure for TaintPup, compared to that of a state-of-the-art security static analysis tool. From our empirical study, we observe security weaknesses to propagate into 4,457 resources, i.e, Puppet-specific code elements used to manage infrastructure. A single instance of a security weakness can propagate into as many as 35 distinct resources. We observe security weaknesses to propagate into 7 categories of resources, which include resources used to manage continuous integration servers and network controllers. According to our survey with 24 practitioners, propagation of security weaknesses into data storage-related resources is rated to have the most severe impact for Puppet-based infrastructure management.
BAYESIAN APPROACH TO SENSORY PREFERENCE, DIFFERENCE AND EQUIVALENCE TESTS

Bayesian theory provides another philosophy of statistics and reasoning for data analysis. It offers an alternative to classical statistical inference. Bayesian approach is of theoretical and methodological significance to sensory discipline and has potentially wide application in sensory and consumer research. This paper discusses Bayesian approach to nonreplicated sensory preference, difference and equivalence tests based on proportions. Bayesian approach to the tests involves Bayesian inference for one proportion with and without background effect (i.e., guessing probability) and for two proportions. Thurstonian discriminal distance d′ is used for measure of sensory difference in Bayesian approach. A set of R and S-Plus codes implementing the Bayesian estimates and tests introduced in the paper is available from the supporting information in the online version of this paper. PRACTICAL APPLICATIONS Sensory preference, difference and equivalence tests are widely used in sensory analysis and consumer research. Bayesian approach provides an alternative to classical statistical inference for the tests. If sample size is large enough or prior distribution is less informative, the results from classical statistical inference and Bayesian approach may not differ greatly; while if sample size is small and prior distribution is more informative, the results may be dramatically different.
A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models

The rapid advancement of social networks and the convenience of internet availability have accelerated the rampant spread of false news and rumors on social media sites. Amid the COVID-19 epidemic, this misleading information has aggravated the situation by putting people’s mental and physical lives in danger. To limit the spread of such inaccuracies, identifying the fake news from online platforms could be the first and foremost step. In this research, the authors have conducted a comparative analysis by implementing five transformer-based models such as BERT, BERT without LSTM, ALBERT, RoBERTa, and a Hybrid of BERT & ALBERT in order to detect the fraudulent news of COVID-19 from the internet. COVID-19 Fake News Dataset has been used for training and testing the models. Among all these models, the RoBERTa model has performed better than other models by obtaining an F1 score of 0.98 in both real and fake classes.