Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Optimal Filter for Linear Gaussian Time-invariant Systems

State estimation is critical to control systems, especially when the states cannot be directly measured. This paper presents an approximate optimal filter, which enables to use policy iteration technique to obtain the steady-state gain in linear Gaussian time-invariant systems. This design transforms the optimal filtering problem with minimum mean square error into an optimal control problem, called Approximate Optimal Filtering (AOF) problem. The equivalence holds given certain conditions about initial state distributions and policy formats, in which the system state is the estimation error, control input is the filter gain, and control objective function is the accumulated estimation error. We present a policy iteration algorithm to solve the AOF problem in steady-state. A classic vehicle state estimation problem finally evaluates the approximate filter. The results show that the policy converges to the steady-state Kalman gain, and its accuracy is within 2 %.
Towards Artefact-based Requirements Engineering for Data-Centric Systems

Many modern software-intensive systems employ artificial intelligence / machine-learning (AI/ML) components and are, thus, inherently data-centric. The behaviour of such systems depends on typically large amounts of data processed at run-time rendering such non-deterministic systems as complex. This complexity growth affects our understanding on needs and practices in Requirements Engineering (RE). There is, however, still little guidance on how to handle requirements for such systems effectively: What are, for example, typical quality requirements classes? What modelling concepts do we rely on or which levels of abstraction do we need to consider? In fact, how to integrate such concepts into approaches for a more traditional RE still needs profound investigations. In this research preview paper, we report on ongoing efforts to establish an artefact-based RE approach for the development of datacentric systems (DCSs). To this end, we sketch a DCS development process with the newly proposed requirements categories and data-centric artefacts and briefly report on an ongoing investigation of current RE challenges in industry developing data-centric systems.
Extended Task and Motion Planning of Long-horizon Robot Manipulation

—Task and Motion Planning (TAMP) requires the integration of symbolic reasoning with metric motion planning that accounts for the robot’s actions’ geometric feasibility. This hierarchical structure inevitably prevents the symbolic planners from accessing the environment’s low-level geometric description, vital to the problem’s solution. Most TAMP approaches fail to provide feasible solutions when there is missing knowledge about the environment at the symbolic level. The incapability of devising alternative high-level plans leads existing planners to a dead end. We propose a novel approach for decision-making on extended decision spaces over plan skeletons and action parameters. We integrate top-k planning for constructing an explicit skeleton space, where a skeleton planner generates a variety of candidate skeleton plans. Moreover, we effectively combine this skeleton space with the resultant motion parameter spaces into a single extended decision space. Accordingly, we use Monte-Carlo Tree Search (MCTS) to ensure an exploration-exploitation balance at each decision node and optimize globally to produce minimum-cost solutions. The proposed seamless combination of symbolic top-k planning with streams, with the proved optimality of MCTS, leads to a powerful planning algorithm that can handle the combinatorial complexity of long-horizon manipulation tasks. We empirically evaluate our proposed algorithm in challenging manipulation tasks with different domains that require multi-stage decisions and show how our method can overcome dead-ends through its effective alternate plans compared to its most competitive baseline method.
Small to large Fermi surface transition in a single-band model using randomly coupled ancillas

We describe a solvable model of a quantum transition in a single band model involving a change in the size of the electron Fermi surface without any symmetry breaking. In a model with electron density 1− p, we find a ‘large’ Fermi surface state with the conventional Luttinger volume 1− p of electrons for p > pc, and a first order transition to a ‘small’ Fermi surface state with a non-Luttinger volume p of holes for p < pc. As required by extended Luttinger theorems, the small Fermi surface state also has fractionalized spinon excitations. The model has electrons with strong local interactions in a single band; after a canonical transformation, the interactions are transferred to a coupling to two layers of ancilla qubits, as proposed by Zhang and Sachdev (Phys. Rev. Research 2, 023172 (2020)). Solvability is achieved by employing random exchange interactions within the ancilla layers, and taking the large M limit with SU(M) spin symmetry, as in the Sachdev-Ye-Kitaev models. The local electron spectral function of the small Fermi surface phase displays a particle-hole asymmetric pseudogap, and maps onto the spectral function of a lightly doped Kondo insulator of a Kondo-Heisenberg lattice model. We discuss connections to the physics of the hole-doped cuprates: the asymmetric pseudogap observed in STM, and the sudden change from incoherent to coherent anti-nodal spectra observed recently in photoemission. A holographic analogy to wormhole transitions between multiple black holes is briefly noted. 1 ar X iv :2 10 3. 05 00 9v 4 [ co nd -m at . s tr -e l] 2 J un 2 02 1
Characterizing total positivity: Single vector tests via linear complementarity, sign non‐reversal and variation diminution

A matrix A$A$ is called totally positive (or totally non‐negative) of order k$k$ , denoted by TPk$TP_k$ (or TNk$TN_k$ ), if all minors of size at most k$k$ are positive (or non‐negative). These matrices have featured in diverse areas in mathematics, including algebra, analysis, combinatorics, differential equations and probability theory. The goal of this article is to provide a novel connection between total positivity and optimization/game theory. Specifically, we draw a relationship between totally positive matrices and the linear complementarity problem (LCP), which generalizes and unifies linear and quadratic programming problems and bimatrix games — this connection is unexplored, to the best of our knowledge. We show that A$A$ is TPk$TP_k$ if and only if for every submatrix Ar$A_r$ of A$A$ formed from r$r$ consecutive rows and r$r$ consecutive columns (with r⩽k$r\leqslant k$ ), LCP(Ar,q)$\mathrm{LCP}(A_r,q)$ has a unique solution for each vector q<0$q<0$ . In fact this can be strengthened to check the solution set of the LCP at a single vector for each such square submatrix. These novel characterizations are in the spirit of classical results characterizing TP$TP$ matrices by Gantmacher–Krein [Compos. Math. 1937] and P$P$ ‐matrices by Ingleton [Proc. London Math. Soc. 1966].
Prediction of 5-year progression-free survival in advanced nasopharyngeal carcinoma with pretreatment PET/CT using multi-modality deep learning-based radiomics

Objective Deep learning-based radiomics (DLR) has achieved great success in medical image analysis and has been considered a replacement for conventional radiomics that relies on handcrafted features. In this study, we aimed to explore the capability of DLR for the prediction of 5-year progression-free survival (PFS) in advanced nasopharyngeal carcinoma (NPC) using pretreatment PET/CT images. Methods A total of 257 patients (170/87 patients in internal/external cohorts) with advanced NPC (TNM stage III or IVa) were enrolled. We developed an end-to-end multi-modality DLR model, in which a 3D convolutional neural network was optimized to extract deep features from pretreatment PET/CT images and predict the probability of 5-year PFS. The TNM stage, as a high-level clinical feature, could be integrated into our DLR model to further improve the prognostic performance. For a comparison between conventional radiomics and DLR, 1,456 handcrafted features were extracted, and optimal conventional radiomics methods were selected from 54 cross-combinations of six feature selection methods and nine classification methods. In addition, risk group stratification was performed with clinical signature, conventional radiomics signature, and DLR signature. Results Our multi-modality DLR model using both PET and CT achieved higher prognostic performance (area under the receiver operating characteristic curve (AUC) = 0.842 ± 0.034 and 0.823 ± 0.012 for the internal and external cohorts) than the optimal conventional radiomics method (AUC = 0.796 ± 0.033 and 0.782 ± 0.012). Furthermore, the multi-modality DLR model outperformed single-modality DLR models using only PET (AUC = 0.818 ± 0.029 and 0.796 ± 0.009) or only CT (AUC = 0.657 ± 0.055 and 0.645 ± 0.021). For risk group stratification, the conventional radiomics signature and DLR signature enabled significant difference between the high- and low-risk patient groups in both the internal and external cohorts (p < 0.001), while the clinical signature failed in the external cohort (p = 0.177). Conclusion Our study identified potential prognostic tools for survival prediction in advanced NPC, which suggests that DLR could provide complementary values to the current TNM staging.
On testing mean proportionality of multivariate normal variables

This short note considers the problem of testing the null hypothesis that the mean values of two multivariate normal variables are proportional. We show that the usual likelihood ratio χ2-test is valid nonasymptotically. Our proof relies on expressing the test statistic as the minimum eigenvalue of a Wishart variable and using a representation of its distribution using Legendre polynomials. MSC2020 subject classifications: 62H15.
Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning

In this paper, the circle formation control problem is addressed for a group of cooperative underactuated fish-like robots involving unknown nonlinear dynamics and disturbances. Based on the reinforcement learning and cognitive consistency theory, we propose a decentralized controller without the knowledge of the dynamics of the fish-like robots. The proposed controller can be transferred from simulation to reality. It is only trained in our established simulation environment, and the trained controller can be deployed to real robots without any manual tuning. Simulation results confirm that the proposed model-free robust formation control method is scalable with respect to the group size of the robots and outperforms other representative RL algorithms. Several experiments in the real world verify the effectiveness of our RL-based approach for circle formation control.
Circulating vascular endothelial growth factor receptor 2 levels and their association with lipid abnormalities in patients on hemodialysis

The aim of the present study was to examine the association between the levels of circulating vascular endothelial growth factor receptor (VEGFR)2 levels, serum lipid composition and plasma receptor for advanced glycation end-products (RAGE) expression in patients undergoing hemodialysis (HD). A total of 50 patients on HD (27 men and 23 women; median age, 66 years; age range 28-88 years; HD mean time, 29.0, 3.9-157.0 months) were enrolled. Age-matched healthy subjects (n=26) were used as the control group. Plasma VEGFR2 and RAGE levels were determined using ELISA. Dyslipidemia (D) in patients on HD was diagnosed according to the Kidney Disease Outcomes Quality Initiative Clinical Practice Guidelines for Managing Dyslipidemias in Chronic Kidney Disease. Circulating VEGFR2, RAGE and serum lipids were compared between dyslipidemic and non-dyslipidemic patients on HD and controls. In patients on HD, the plasma VEGFR2 levels were lower compared with those in the healthy population. D was associated with high plasma VEGFR2 levels. The triglyceride/HDL-cholesterol ratio was strongly associated with plasma VEGFR2 levels. The plasma VEGFR2 concentration was associated with circulating RAGE levels. Therefore, circulating VEGFR2 levels may be partly associated with lipid abnormalities and plasma RAGE levels in patients receiving HD.
Renal Involvement in COVID-19: A Review of the Literature

Abstract Kidney injury may be a severe complication of acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and contributes to worsen the prognosis. Various pathophysiological mechanisms can contribute to organ damage and impair renal function, proving the complexity of the virus activity and the resulting immunity response. We summarized the evidence of the literature on the prevalence of kidney involvement, on the pathogenic pathways and on its management.
Mutational signatures associated with exposure to carcinogenic microplastic compounds bisphenol A and styrene oxide

Abstract Microplastic pollutants in oceans and food chains are concerning to public health. Common plasticizing compounds Bisphenol-A (BPA) and Styrene-7,8-Oxide (SO) are now labeled as carcinogens. We show that BPA and SO cause deoxyribonucleic acid damage and mutagenesis in human cells, and analyze the genome-wide point mutation and genomic rearrangement patterns associated with BPA and SO exposure. A subset of the single- and doublet base substitutions shows mutagenesis near or at guanine, consistent with these compounds’ preferences to form guanosine adducts. Presence of other mutational signatures suggest additional mutagenesis probably due to complex effects of BPA and SO on diverse cellular processes. Analyzing data for 19 cancer cohorts, we find that tumors of digestive and urinary organs show relatively high similarity in mutational profiles, and the burden of such mutations increases with age. Even within the same cancer type, proportions of corresponding mutational patterns vary among the cohorts from different countries, as does the amount of microplastic waste in ocean waters. BPA and SO are relatively mild mutagens, and other environmental agents can also potentially generate similar, complex mutational patterns in cancer genomes. Nonetheless, our findings call for systematic evaluation of public health consequences of microplastic exposure worldwide.
Four potential microRNAs affect the progression of pancreatic ductal adenocarcinoma by targeting MET via the PI3K/AKT signaling pathway

Pancreatic ductal adenocarcinoma (PDAC) is the most common tumor subtype of pancreatic cancer, which exhibits poor patient prognosis due to the lack of effective biomarkers in the diagnosis and treatment. The present study aimed to identify the potential biomarkers of PDAC carcinogenesis and progression using three microarray datasets, GSE15471, GSE16515 and GSE28735, which were downloaded from the Gene Expression Omnibus database. The datasets were analyzed to screen out differentially expressed genes (DEGs) in PDAC tissues and adjacent normal tissues. A total of 143 DEGs were identified, including 132 upregulated genes and 11 downregulated genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional and signaling pathway enrichment analyses were performed on the DEGs, and the Search Tool for the Retrieval of Interacting Genes/Proteins database was used to construct a protein-protein interaction network. The main functions of DEGs include extracellular matrix degradation, and regulation of matrix metalloproteinase activity and the PI3K-Akt signaling pathway. The five hub genes were subsequently screened using Cytoscape software, and survival analysis demonstrated that abnormal expression levels of the hub genes was associated with poor disease-free survival and overall survival. Biological experiments were performed to confirm whether mesenchymal-to-epithelial transition (MET) factors promote the proliferation, migration and invasion of PDAC cells via the PI3K/AKT signaling pathway. In addition, six MET-targeted microRNAs (miRNAs) were identified, four of which had conserved binding sites with MET. Based on the signaling pathway enrichment analysis of these miRNAs, it is suggested that they can affect the progression of PDAC by targeting MET via the PI3K/AKT signaling pathway. In conclusion, the hub genes and miRNAs that were identified in the present study contribute to the molecular mechanisms of PDAC carcinogenesis and progression. They also provide candidate biomarkers for early diagnosis and treatment of patients with PDAC.
Nanosonosensitizer-Augmented Sonodynamic Therapy Combined with Checkpoint Blockade for Cancer Immunotherapy

Introduction Sonodynamic therapy (SDT) has good targeting and non-invasive advantages in the treatment of solid cancers, and checkpoint blockade immunotherapy is also a promising treatment to cure cancer. However, their antitumor effects are not sufficient due to some inherent factors. Some studies that combined SDT with immunotherapy or nanoparticles have managed to enhance its efficiency to treat cancers. Methods In this work, an effective therapeutic strategy that can potentiate the antitumor efficacy of anti-PD-L1 antibody (aPD-L1) is developed by the use of cascade immuno-sonodynamic therapy (immuno-SDT). Titanium dioxide (TiO2), a nanostructured agent for SDT, sonosensitizer Chlorin e6 (Ce6), and immunological adjuvant CpG oligonucleotide (CpG ODN), are used to construct a multifunctional nanosonosensitizer (TiO2-Ce6-CpG). Then, we conducted in vitro and in vivo experiments to explore the antitumor effect of TiO2-Ce6-CpG under ultrasound (US) treatment. Results The characterization tests showed that the nanosonosensitizers are polycrystalline structure with homogeneous sizes, resulting in a good drug loading efficiency. The innovative nanosonosensitizers (TiO2-Ce6-CpG) can not only effectively inhibit tumor growth but also stimulate the immune system to activate the adaptive immune responses, using the TiO2-Ce6 to augment SDT and the immune adjuvant CpG to enhance the immune response. After combined with the aPD-L1, the synergistic effect could not only efficiently inhibit the primary tumor growth but also lead to an inhibition of the non-irradiated pre-existing distant tumors by inducing a strong tumor-specific immune response. Conclusion In this study, we present an effective strategy for tumor treatment by combining nanosonosensitizer-augmented SDT and aPD-L1 checkpoint blockade. This work provides a promising strategy and offers a new vision for treating malignant tumors.
High pressure and temperature behaviour of electrical resistivity of hcp metals Ti, Zr and Gd

High pressure measurements of electrical resistivity of polycrystalline Ti, Zr and Gd have been performed in the temperature range 22-1000 °C and in the pressure range 4-48 kbar. The pressure and temperature coefficients of electrical resistivity of the hcp phase have been determined and new measurements of the boundaries into hcp-neighbouring phases are presented. All three metals show typical pressure and temperature dependences of resistivity behaviour in the hcp region, with Gd displaying the greatest sensitivity to pressure due to its high compressibility.
Artificial Neural Network Modeling of Deposition Thickness Through the Pipeline

Modeling and prediction of deposition thickness is a key to developing crude oil transportation process. Usually, the heat transfer method has been used to calculate deposition thickness through the pipeline, but this method requires vast information and cumbersome calculations. To eliminate this problem, a feed-forward multilayer perceptron neural network (MLPNN) algorithm has been applied to predict the deposition thickness through the pipeline. We present an optimized one-layer feed-forward neural network using properties of the oil pipeline such as inlet and outlet oil temperatures, environmental (mixture of water and ethylene glycol) temperature, and oil Reynolds’ numbers. Different networks are considered and trained using 15313 data sets; the accuracy of the proposed network is validated by 5104 testing data sets. Using validating data set, the network that is having the lowest mean square error (MSE) and average absolute relative deviation percent (AARD%) and the highest regression coefficient (R2) is selected as an optimal configuration. Statistical analysis show that the artificial neural network (ANN) predictions have an excellent agreement (MSE = 9.4949e-006, AARD% = 1.0109 and R2 = 0.9999) with the heat transfer method. In the other words, accuracy of the proposed ANN model is approximately equal to the heat transfer method without required too much information and cumbersome calculations.
Resolving the Dynamic Motions of SARS-CoV-2 nsp7 and nsp8 Proteins Using Structural Proteomics

Coronavirus (CoV) non-structural proteins (nsps) assemble to form the replication-transcription complex (RTC) responsible for viral RNA synthesis. nsp7 and nsp8 are important cofactors of the RTC, as they interact and regulate the activity of RNA-dependent RNA polymerase (RdRp) and other nsps. To date, no structure of full-length SARS-CoV-2 nsp7:nsp8 complex has been published. Current understanding of this complex is based on structures from truncated constructs or with missing electron densities and complexes from related CoV species with which SARS-CoV-2 nsp7 and nsp8 share upwards of 90% sequence identity. Despite available structures being solved using crystallography and cryo-EM representing detailed snapshots of the nsp7:nsp8 complex, it is evident that the complex has a high degree of structural plasticity. However, relatively little is known about the conformational dynamics of the complex and how it assembles to interact with other nsps. Here, the solution-based structural proteomic techniques, hydrogen-deuterium exchange mass spectrometry (HDX-MS) and crosslinking mass spectrometry (XL-MS), illuminate the structural dynamics of the SARS-CoV-2 full-length nsp7:nsp8 complex. The results presented from the two techniques are complementary and validate the interaction surfaces identified from the published three-dimensional heterotetrameric crystal structure of SARS-CoV-2 truncated nsp7:nsp8 complex. Furthermore, mapping of XL-MS data onto higher order complexes suggests that SARS-CoV-2 nsp7 and nsp8 do not assemble into a hexadecameric structure as implied by the SARS-CoV full-length nsp7:nsp8 crystal structure. Instead our results suggest that the nsp7:nsp8 heterotetramer can dissociate into a stable dimeric unit that might bind to nsp12 in the RTC without altering nsp7-nsp8 interactions.