Miracle-dz/UniFAR-FTUs huggingface dataset:
Millimetre Wave Emission by Discrete Method in Sea-foam Layer at WindSat Frequencies

Computation of millimetre wave scattering by sea foam is a significant query which contributes immensely to measurement of sea surface emissivity and brightness temperature by passive microwave radiometers. For efficient evaluation of scattering by foam covered sea surface and measurement of brightness temperatures in milli-Kelvins, a discrete based physical model of sea foam is developed to provide accurate estimate of the complex effective dielectric constant of sea foam. The sea foam layer is modelled as sequences of thin phase screens in two dimensional (2D) slices of sea foam layer with equal depth d (mm). Each layer comprised of random distribution of bubbles that follows a log-normal distribution pattern with geometrical and optical properties such as foam layer thickness, foam void fraction, foam volume fraction, sea surface temperature and sea surface salinity. Results of sea surface emissivity as a function of polarization, angle of incidence, WindSat frequencies and thickness of sea foam are presented.
Review of SCADA systems for Photovoltaic Power Plants

Supervisory control and data acquisition (SCADA) is a control system architecture which makes use of computers, networked data communications and graphical user interfaces for high level process supervisory management, also uses other peripheral devices such as programmable logic controllers and discrete PID controllers to interface to the process plant or machinery. The operator interfaces which enables monitoring and the issuing of process commands, such as controller set point changes, are handled though the SCADA computer system. However the real time control logic or controller calculations are performed by networked modules which are connected to the field sensors and actuators. The proposed review paper provides an insight of SCADA systems for electrical and power systems. The review also provides a comparative analysis of various research methodologies that have been accomplished in the said area.
Dear Chris, Thank you for your swift action on our revision and for the feedback from the reviewers

I am, however, more worried about the sample sizes for the latter three hypotheses (manipulation checks), all of which are N=11 or less. That said, since they are manipulation checks, it is in some ways your risk to take as to whether you genuinely believe these samples are sufficiently large, because failure of critical manipulation checks is one of the few grounds for rejection at Stage 2 based on outcomes (see criterion 2A here). If you feel there is risk of failure in any of these checks, I would strongly advise increasing the sample size to avoid a rare Stage 2 rejection.
Stance Detection in the Context of Fake News

Online Social Networks (OSNs) are overwhelmed by the daily volume of news reported by humans from all around the world. The diffusion of information can start from any OSNs user and spread rapidly. The process of news fact-checking is very labor and resource intensive. Researchers are looking for machine-learning approaches to automate the detection of fake news. Toward such goal, this paper focused on stance detection of content producers, whether they are in favor or against the content subject. In this study, our goal is to develop and evaluate advanced text-mining models that are enhanced with the cosine similarity between the headline and body of news articles to predict users' stance from the articles' content. We specifically aim to explore if the cosine distance feature will enhance the models' prediction performance.
Ultrasound-assisted extraction (UAE) of Quercetin from Thespesia populnea leaves

The search for naturally occurring quercetin has a great interest in industries as well as in scientific research. There was no qualitative and quantitative information on effective isolation on the flavonoid, particularly quercetin content in the leaves of the plant. As flavonoids are one of the major natural product subgroups in all the plants, it is useful to explore their content in the T. populnea leaves. Furthermore, there is a growing interest for naturally derived medications; effective methodology should be developed to extract the bioactive compounds from the leaves [1-5].
First detection of Powassan Virus lineage I in field-collected Dermacentor variabilis from New York, USA

Powassan virus (POWV) is a tick-borne flavivirus that can cause lethal or debilitating neurological illness. It is canonically transmitted by Ixodes genus ticks but may interact with sympatric Dermacentor species. Here, we report the first detection of POWV lineage I from a pool of field-collected D. variabilis in New York state.
Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium

BACKGROUND: Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia (N = 11,095), using a single image analysis protocol. METHODS: We included T1-weighted data from 46 datasets (5,080 affected individuals and 6,015 controls) from the ENIGMA Consortium. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Analyses were also performed with respect to the use of antipsychotic medication and other clinical variables, as well as age and sex. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029). RESULTS: Small average differences between cases and controls were observed for asymmetries in cortical thickness, specifically of the rostral anterior cingulate (d = -0.08, pFDR = 0.047) and the middle temporal gyrus (d = -0.07, pFDR = 0.048), both driven primarily by thinner cortices in the left hemisphere in schizophrenia. These asymmetries were not significantly associated with the use of antipsychotic medication or other clinical variables. Older individuals with schizophrenia showed a stronger average leftward asymmetry of pallidum volume than older controls (d = 0.08, pFDR = 9.0 x 10-3). The multivariate analysis revealed that 7% of the variance across all structural asymmetries was explained by case-control status (F = 1.87, p = 1.25 x 10-5). CONCLUSIONS: Altered trajectories of asymmetrical brain development and/or lifespan asymmetry may contribute to schizophrenia pathophysiology. Small case-control differences of brain macro-structural asymmetry may manifest due to more substantial differences at the molecular, cytoarchitectonic or circuit levels, with functional relevance for lateralized cognitive processes.
Study on electro-pneumatic proportional valve based on PWM technology

The theory of fluid PWM technique is described and the mathematical model of the electro-pneumatic proportional valveis built. The scheme for the valve based on PWM technology is proposed. The digital proportional controller is designed withTMS320LF2407A singlechip and test is done. The test results show that the electro-pneumatic proportional valve based on PWMtechnology features smooth performance and conforms to the requirement by electro-pneumatic braking system.
Running Title: Markers for Thyroid Cancer Diagnosis Currently Used and New Molecular Markers for Thyroid Cancer Diagnosis

Thyroid cancer is highly common all around the world. Its prevalence has rapidly increased over the last 30 years in the United States and other developing countries. Fine-needle aspiration biopsy has become the cornerstone of thyroid nodule diagnosis, whose general reliability is outstanding; however, some aspirates have shown undetermined cytological findings that do not provide a definitive malignancy diagnosis. At least 70 molecular and genetic markers in thyroid nodules have been analyzed in an effort to identify molecular markers to differentiate malignant and benign (BN) thyroid nodules. The present review focused on the currently used markers in thyroid cancer diagnosis. A rising number of studies have investigated immunohistochemical markers, such as galectin-3 (GAL3), cytokeratin 19 (CK19), Hector Battifora Mesothelial-1 (HBME-1), and thyroid peroxidase, along with DNA alterations, including mainly BRAF (BRaf proto-oncogene, serine/threonine kinase) and RAS (Ras proto-oncogene, GTPase) point mutations, Telomerase reverse transcriptase mutations, ret proto-oncogene/papillary thyroid carcinoma (RET/PTC) and PAX8/PPARG rearrangements, and miRNA signatures and circulating tumor cells for thyroid cancer diagnosis. Although certain markers are promising for differential diagnosis, due to limitations of the substantial prevalence of BN thyroid tumors, none of them is specifically definitive to a large extent. Herein, we also discussed the studies that have supported the use of combinations of several markers, like KIT, TC1, miR-222, and miR-146b combination, as well as GAL3, CK19, and HBME-1 combination, in enhancing the diagnostic accuracy in differentiating malignant and BN tumors.
The application of AIS in the study of rule for the OTHR

The detecting range of OTHR(over the horizon radar)are affected by a few factors,such as sea cluster,work frequency and background noise,the key of which is to choose a moderate frequency of the radar.This paper introduces the message transmitted by the AIS and its component,analyzes the factors affecting the range of radar,discusses the way of choosing the compatible frequency based on the AIS and data mining.The implementation has been proved that the method is feasible and of high military and economic value,meet the current training demand of naval OTHR.
Multi-Objective Latent Space Optimization of Generative Molecular Design Models

Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency for exploring high-dimensional molecular space to identify molecules with desired properties. While the efficacy of the initial model strongly depends on the training data, the sampling efficiency of the model for suggesting novel molecules with enhanced properties can be further enhanced via latent space optimization. In this paper, we propose a multi-objective latent space optimization (LSO) method that can significantly enhance the performance of generative molecular design (GMD). The proposed method adopts an iterative weighted retraining approach, where the respective weights of the molecules in the training data are determined by their Pareto efficiency. We demonstrate that our multi-objective GMD LSO method can significantly improve the performance of GMD for jointly optimizing multiple molecular properties.
First-generation Memory Disaggregation for Cloud Platforms

In Azure, up to 25% of memory is stranded , i.e., it is leftover after the servers’ cores have been rented to VMs. Memory disaggregation promises to reduce this stranding. However, making disaggregation practical for production cloud deployment remains challenging. For example, RDMA-based disaggregation involves too much overhead for common workloads and transparent latency management is incompatible with virtualization acceleration. The emerging Compute Express Link (CXL) standard offers a low-overhead substrate to build memory disaggregation while overcoming these challenges. This paper proposes a ﬁrst-generation CXL-based disaggregation system that meets the requirements of cloud providers. Our system includes a memory pool controller, and prediction-based system software and distributed control plane designs. Its predictions of VM latency sensitivity and memory usage allow it to split work-loads across local and pooled memory while mitigating the higher pool latency. Our analysis of production clusters shows that small pools of 8-32 sockets are sufﬁcient to reduce stranding signiﬁ-cantly. It also shows that ∼ 50% of all VMs never touch 50% of their rented memory. In emulated experiments with 150+ workloads, we show our pooling approach incurs a conﬁg-urable performance loss between 1-5%. Finally, we show that disaggregation can achieve a 9-10% reduction in over-all DRAM, which represents hundreds of millions of dollars in cost savings for a large cloud provider.
MRI-GAN: A Generalized Approach to Detect DeepFakes using Perceptual Image Assessment

DeepFakes are synthetic videos generated by swapping a face of an original image with the face of somebody else. In this paper, we describe our work to develop general, deep learning-based models to classify DeepFake content. We propose a novel framework for using Generative Adversarial Network (GAN)-based models, we call MRI-GAN, that utilizes perceptual differences in images to detect synthesized videos. We test our MRI-GAN approach and a plain-frames-based model using the DeepFake Detection Challenge Dataset. Our plain frames-based-model achieves 91% test accuracy and a model which uses our MRI-GAN framework with Structural Similarity Index Measurement (SSIM) for the perceptual differences achieves 74% test accuracy. The results of MRI-GAN are preliminary and may be improved further by modifying the choice of loss function, tuning hyper-parameters, or by using a more advanced perceptual similarity metric.
On Orthogonal Approximate Message Passing

Approximate Message Passing (AMP) is an efficient iterative parameter-estimation technique for certain high-dimensional linear systems with non-Gaussian distributions, such as sparse systems. In AMP, a so-called Onsager term is added to keep estimation errors approximately Gaussian. Orthogonal AMP (OAMP) does not require this Onsager term, relying instead on an orthogonalization procedure to keep the current errors uncorrelated with (i.e., orthogonal to) past errors. \LL{In this paper, we show the generality and significance of the orthogonality in ensuring that errors are"asymptotically independently and identically distributed Gaussian"(AIIDG). } This AIIDG property, which is essential for the attractive performance of OAMP, holds for separable functions. \LL{We present a simple and versatile procedure to establish the orthogonality through Gram-Schmidt (GS) orthogonalization, which is applicable to any prototype. We show that different AMP-type algorithms, such as expectation propagation (EP), turbo, AMP and OAMP, can be unified under the orthogonal principle. } The simplicity and generality of OAMP provide efficient solutions for estimation problems beyond the classical linear models. \LL{As an example, we study the optimization of OAMP via the GS model and GS orthogonalization. } More related applications will be discussed in a companion paper where new algorithms are developed for problems with multiple constraints and multiple measurement variables.
Riemannian Statistics Meets Random Matrix Theory: Toward Learning From High-Dimensional Covariance Matrices

Riemannian Gaussian distributions were initially introduced as basic building blocks for learning models which aim to capture the intrinsic structure of statistical populations of positive-definite matrices (here called covariance matrices). While the potential applications of such models have attracted significant attention, a major obstacle still stands in the way of these applications: there seems to exist no practical method of computing the normalising factors associated with Riemannian Gaussian distributions on spaces of high-dimensional covariance matrices. The present paper shows that this missing method comes from an unexpected new connection with random matrix theory. Its main contribution is to prove that Riemannian Gaussian distributions of real, complex, or quaternion covariance matrices are equivalent to orthogonal, unitary, or symplectic log-normal matrix ensembles. This equivalence yields a highly efficient approximation of the normalising factors, in terms of a rather simple analytic expression. The error due to this approximation decreases like the inverse square of dimension. Numerical experiments are conducted which demonstrate how this new approximation can unlock the difficulties which have impeded applications to real-world datasets of high-dimensional covariance matrices. The paper then turns to Riemannian Gaussian distributions of block-Toeplitz covariance matrices. These are equivalent to yet another kind of random matrix ensembles, here called “acosh-normal” ensembles. Orthogonal and unitary “acosh-normal” ensembles correspond to the cases of block-Toeplitz with Toeplitz blocks, and block-Toeplitz (with general blocks) covariance matrices, respectively.
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes

Reinforcement Learning (RL) has recently received significant attention from the process systems engineering and control communities. Recent works have investigated the application of RL to identify optimal scheduling decision in the presence of uncertainty. In this work, we present a RL methodology tailored to efficiently address production scheduling problems in the presence of uncertainty. We consider commonly imposed restrictions on these problems such as precedence and disjunctive constraints which are not naturally considered by RL in other contexts. Additionally, this work naturally enables the optimization of risk-sensitive formulations such as the conditional value-at-risk (CVaR), which are essential in realistic scheduling processes. The proposed strategy is investigated thoroughly in a parallel batch production environment, and benchmarked against mixed integer linear programming (MILP) strategies. We show that the policy identified by our approach is able to account for plant uncertainties in online decision-making, with expected performance comparable to existing MILP methods. Additionally, the framework gains the benefits of optimizing for risk-sensitive measures, and identifies online decisions orders of magnitude faster than the most efficient optimization approaches. This promises to mitigate practical issues and ease in handling realizations of process uncertainty in the paradigm of online production scheduling.
Large-charge conformal dimensions at the $O(N)$ Wilson-Fisher fixed point

Recent work using a large-charge expansion for the O(N) Wilson-Fisher conformal field theory has shown that the anomalous dimensions of large-charge operators can be expressed in terms of a few low-energy constants (LECs) of a large-charge effective field theory (EFT). By performing lattice Monte Carlo computations at the O(N) Wilson-Fisher fixed point, we compute the anomalous dimensions of large-charge operators up to N = 8 and charge Q = 10, and extract the leading and subleading LECs of the O(N) large-charge EFT. To alleviate the signal-to-noise ratio problem present in the large-charge sector of conventional lattice formulations of the O(N) theory, we employ a recently developed qubit formulation of the O(N) nonlinear sigma models with a worm algorithm. This enables us to test the validity of the large-charge expansion and the recent large-N predictions for the coefficients of the large-charge EFT.
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition

The MultiCoNER shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of contexts makes the recognition of ambiguous named entities challenging. To alleviate this issue, our team DAMO-NLP proposes a knowledge-based system, where we build a multilingual knowledge base based on Wikipedia to provide related context information to the named entity recognition (NER) model. Given an input sentence, our system effectively retrieves related contexts from the knowledge base. The original input sentences are then augmented with such context information, allowing significantly better contextualized token representations to be captured. Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
Analysis of digitalized ECG signals based on artificial intelligence and spectral analysis methods specialized in ARVC

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that appears between the second and forth decade of a patient's life, being responsible for 20% of sudden cardiac deaths before the age of 35. The effective and punctual diagnosis of this disease based on electrocardiograms (ECGs) could have a vital role in reducing premature cardiovascular mortality. In our analysis, we first outline the digitalization process of paper‐based ECG signals enhanced by a spatial filter aiming to eliminate dark regions in the dataset's images that do not correspond to ECG waveform, producing undesirable noise. Next, we propose the utilization of a low‐complexity convolutional neural network for the detection of an arrhythmogenic heart disease, that has not been studied through the usage of deep learning methodology to date, achieving high classification accuracy, namely 99.98% training and 98.6% testing accuracy, on a disease the major identification criterion of which are infinitesimal millivolt variations in the ECG's morphology, in contrast with other arrhythmogenic abnormalities. Finally, by performing spectral analysis we investigate significant differentiations in the field of frequencies between normal ECGs and ECGs corresponding to patients suffering from ARVC. In 16 out of the 18 frequencies where we encounter statistically significant differentiations, the normal ECGs are characterized by greater normalized amplitudes compared to the abnormal ones. The overall research carried out in this article highlights the importance of integrating mathematical methods into the examination and effective diagnosis of various diseases, aiming to a substantial contribution to their successful treatment.
Maximum Flow and Minimum-Cost Flow in Almost-Linear Time

We give an algorithm that computes exact maximum flows and minimum-cost flows on directed graphs with m edges and polynomially bounded integral demands, costs, and capacities in $m^{1+o(1)}$ time. Our algorithm builds the flow through a sequence of $m^{1+o(1)}$ approximate undirected minimum-ratio cycles, each of which is computed and processed in amortized $m^{o(1)}$ time using a new dynamic graph data structure. Our framework extends to algorithms running in $m^{1+o(1)}$ time for computing flows that minimize general edge-separable convex functions to high accuracy. This gives almost-linear time algorithms for several problems including entropy-regularized optimal transport, matrix scaling, p-norm flows, and p-norm isotonic regression on arbitrary directed acyclic graphs.
Neural Ordinary Differential Equations for Nonlinear System Identification

Neural ordinary differential equations (NODE) have been recently proposed as a promising approach for nonlinear system identification tasks. In this work, we systematically compare their predictive performance with current state-of-the-art nonlinear and classical linear methods. In particular, we present a quantitative study comparing NODE’s performance against neural state-space models and classical linear system identification methods. We evaluate the inference speed and prediction performance of each method on open-loop errors across eight different dynamical systems. The experiments show that NODEs can consistently improve the prediction accuracy by an order of magnitude compared to benchmark methods. Besides improved accuracy, we also observed that NODEs are less sensitive to hyperparameters compared to neural state-space models. On the other hand, these performance gains come with a slight increase of computation at the inference time.