jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Linh Nghiem | Francis K. C. Hui | Samuel Müller | A. H. Welsh
Source: Scandinavian Journal of Statistics (article, 2022-01-08)
Field: Chemistry — Analytical Chemistry

Estimation of graphical models for skew continuous data Abstract We consider a new approach for estimating non‐Gaussian undirected graphical models. Specifically, we model continuous data from a class of multivariate skewed distributions, whose conditional dependence structure depends on both a precision matrix and a shape vector. To estimate the graph, we propose a novel estimation method based on nodewise regression: we first fit a linear model, and then fit a one component projection pursuit regression model to the residuals obtained from the linear model, and finally threshold appropriate quantities. Theoretically, we establish error bounds for each nodewise regression and prove the consistency of the estimated graph when the number of variables diverges with the sample size. Simulation results demonstrate the strong finite sample performance of our new method over existing methods for estimating Gaussian and non‐Gaussian graphical models. Finally, we demonstrate an application of the proposed method on observations of physicochemical properties of wine. Mathematics ; Graphical model ; Gaussian ; Graph ; Projection pursuit ; Applied mathematics ; Skew ; Algorithm ; Statistics ; Computer science ; Discrete mathematics Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Bayesian Methods and Mixture Models
Authors: Danilo B. Liarte | Stephen J. Thornton | Eric Schwen | Itai Cohen | Debanjan Chowdhury | James P. Sethna
Source: arXiv (Cornell University) (preprint, 2022-02-28)
Field: Materials Science — Materials Chemistry

Universal scaling for disordered viscoelastic matter II: Collapses, global behavior and spatio-temporal properties Disordered viscoelastic materials are ubiquitous and exhibit fascinating invariant scaling properties. In a companion article, we have presented comprehensive new results for the critical behavior of the dynamic susceptibility of disordered elastic systems near the onset of rigidity. Here we provide additional details of the derivation of the singular scaling forms of the longitudinal response near both jamming and rigidity percolation. We then discuss global aspects associated with these forms, and make scaling collapse plots for both undamped and overdamped dynamics in both the rigid and floppy phases. We also derive critical exponents, invariant scaling combinations and analytical formulas for universal scaling functions of several quantities such as transverse and density responses, elastic moduli, viscosities, and correlation functions. Finally, we discuss tentative experimental protocols to measure these behaviors in colloidal suspensions. Scaling ; Rigidity (electromagnetism) ; Viscoelasticity ; Statistical physics ; Invariant (physics) ; Jamming ; Physics ; Percolation (cognitive psychology) ; Scale invariance ; Moduli ; Classical mechanics ; Condensed matter physics ; Mathematics ; Mathematical physics ; Quantum mechanics ; Geometry Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Weiwen Sun | Yuan Lin | Yue Huang | Josolyn Chan | Sonia Terrillon | Anton I. Rosenbaum | Kévin Contrepois
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-11-30)
Field: Chemistry — Spectroscopy

Robust and High-Throughput Analytical Flow Proteomics Analysis of Cynomolgus Monkey and Human Matrices with Zeno SWATH Data Independent Acquisition ABSTRACT Modern mass spectrometers routinely allow deep proteome coverage in a single experiment. These methods are typically operated at nano and micro flow regimes, but they often lack throughput and chromatographic robustness, which is critical for large-scale studies. In this context, we have developed, optimized and benchmarked LC-MS methods combining the robustness and throughput of analytical flow chromatography with the added sensitivity provided by the Zeno trap across a wide range of cynomolgus monkey and human matrices of interest for toxicological studies and clinical biomarker discovery. SWATH data independent acquisition (DIA) experiments with Zeno trap activated (Zeno SWATH DIA) provided a clear advantage over conventional SWATH DIA in all sample types tested with improved sensitivity, quantitative robustness and signal linearity as well as increased protein coverage by up to 9-fold. Using a 10-min gradient chromatography, up to 3,300 proteins were identified in tissues at 2 µg peptide load. Importantly, the performance gains with Zeno SWATH translated into better biological pathway representation and improved the ability to identify dysregulated proteins and pathways associated with two metabolic diseases in human plasma. Finally, we demonstrate that this method is highly stable over time with the acquisition of reliable data over the injection of 1,000+ samples (14.2 days of uninterrupted acquisition) without the need for human intervention or normalization. Altogether, Zeno SWATH DIA methodology allows fast, sensitive and robust proteomic workflows using analytical flow and is amenable to large-scale studies. This work provides detailed method performance assessment on a variety of relevant biological matrices and serves as a valuable resource for the proteomics community. Robustness (evolution) ; Computer science ; Biomarker discovery ; Systems biology ; Biological system ; Proteomics ; Artificial intelligence ; Computational biology ; Chemistry ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Yongmei Li | Hailing Liu | Shijie Pan | Su‐Juan Qin | Fei Gao | Qiaoyan Wen
Source: arXiv (Cornell University) (preprint, 2022-06-11)
Field: Chemistry — Spectroscopy

Quantum discriminative canonical correlation analysis Discriminative Canonical Correlation Analysis (DCCA) is a powerful supervised feature extraction technique for two sets of multivariate data, which has wide applications in pattern recognition. DCCA consists of two parts: (i) mean-centering that subtracts the sample mean from the sample; (ii) solving the generalized eigenvalue problem. The cost of DCCA is expensive when dealing with a large number of high-dimensional samples. To solve this problem, here we propose a quantum DCCA algorithm. Specifically, we devise an efficient method to compute the mean of all samples, then use block-Hamiltonian simulation and quantum phase estimation to solve the generalized eigenvalue problem. Our algorithm achieves a polynomial speedup in the dimension of samples under certain conditions over its classical counterpart. Discriminative model ; Canonical correlation ; Eigenvalues and eigenvectors ; Algorithm ; Hamiltonian (control theory) ; Speedup ; Quantum ; Dimension (graph theory) ; Mathematics ; Computer science ; Dimensionality reduction ; Pattern recognition (psychology) ; Applied mathematics ; Artificial intelligence ; Mathematical optimization ; Combinatorics ; Quantum mechanics Molecular spectroscopy and chirality ; Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Ryad Belhakem
Source:  (preprint, 2022-06-10)
Field: Chemistry — Analytical Chemistry

Statistical study of functional principal component analysis in uni and multivariate frameworks Ce travail est la concaténation de deux parties, ayant pour point comment de porter sur l&amp;#039;analyse de données fonctionnelle et en particulier de s&amp;#039;intéresser aux questions liées à la grande dimension dans ce contexte. La première partie concerne l&amp;#039;analyse en composante principale fonctionnelle dans le cas univarié. Notre approche vise à donner des résultats non-asymptotiques pour différents estimateurs de projection des éléments propres d&amp;#039;un opérateur de covariance. Nous définissons d&amp;#039;abord un estimateur basé sur un opérateur de projection. Cet opérateur peut être vu comme une étape de reconstruction des données brutes dans le contexte de l&amp;#039;analyse des données fonctionnelles. Nous montrons que l&amp;#039;estimateur naïf, qui calcule les éléments propres sans régularisation après l&amp;#039;étape de projection, est optimal au sens minimax pour un bon choix de base. À cette fin, nous établissons à la fois une limite inférieure et supérieure sur l&amp;#039;erreur quadratique moyenne de reconstruction des éléments propres. Nous prouvons également des résultats généraux pour les bases générales de Lipschitz et de Daubechies qui n&amp;#039;atteignent pas les vitesses minimax. Dans le cas de Daubechies, un seuillage est nécessaire pour atteindre son taux optimal. Cette partie est conclue par des simulations numériques qui confirment l&amp;#039;acuité de l&amp;#039;approche et une application à des données génomique. La seconde partie concerne la généralisation du modèle au cas fonctionnelle multivarié. Comme en première partie notre approche vise à donner des résultats non-asymptotiques pour l&amp;#039;estimation de la première composante principale d&amp;#039;un processus aléatoire multivarié. Nous définissons d&amp;#039;abord la fonction de covariance et l&amp;#039;opérateur de covariance dans le cas multivarié. On définit alors un opérateur de projection. Cet opérateur peut être vu comme une étape de reconstruction à partir des données brutes dans le contexte d&amp;#039;analyse de données fonctionnelles. Ensuite, nous montrons que les éléments propres peuvent être exprimés comme la solution d&amp;#039;un problème d&amp;#039;optimisation, et nous introduisons la variante LASSO de ce problème d&amp;#039;optimisation et l&amp;#039;estimateur de plugin associé. Enfin, nous évaluons la précision de l&amp;#039;estimateur. Nous établissons une borne inférieure minimax sur l&amp;#039;erreur quadratique moyenne de reconstruction de l&amp;#039;élément propre, ce qui prouve que la procédure a une variance optimale au sens minimax. Principal component analysis ; Multivariate statistics ; Multivariate analysis ; Component (thermodynamics) ; Functional principal component analysis ; Statistics ; Computer science ; Mathematics ; Physics Spectroscopy and Chemometric Analyses
Authors: Óscar Cáceres Moreno | Diego Manuel Martínez Gila | D. Aguilera Puerto | Javier Gámez García | Juan Gómez Ortega
Source: Actas de las XXXVII Jornadas de Automática 7, 8 y 9 de septiembre de 2016, Madrid (article, 2022-02-18)
Field: Chemistry — Analytical Chemistry

Predicción automática de la calidad del aceite de oliva virgen emplendo visión por computador sobre aceitunas a la entrada del proceso de elaboración Los parámetros más importantes para medir la calidad del aceite de oliva virgen (AOV) son la acidez y el índice de peróxidos y actualmente son de- terminados en laboratorios que utilizan las mues- tras de aceitunas extraídas de cada lote en el pro- ceso de recepción de las almazaras. El objetivo de este trabajo es proporcionar un sistema de inspección automático, basado en visión por computador en los canales visible e infrarrojo (IR), para inferir automáticamente estos parámetros. La propuesta utiliza como características principales las diferencias de textura superficial, los defectos en las imágenes en IR y una valoración de color en el espacio CIELab. Por otra parte dos redes neuronales artificiales se han utilizado como técnica de estimación de los parámetros de calidad mencionados. El sistema ha alcanzado buenos resultados con R = 96.3 para acidez y R = 93.9 para índice de peróxidos. Humanities ; Art ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Samanhudi Samanhudi | Amalia Tetrani Sakya | Andriyana Setyawati | MUMTAZ MUAWANAH
Source: Biodiversitas Journal of Biological Diversity (article, 2022-04-27)
Field: Chemistry — Organic Chemistry

Agarwood (Aquilaria malaccensis) diversity conservation by in vitro culture with IAA and yeast extract Abstract. Samanhudi, Sakya AT, Setyawati A, Muawanah M. 2022. Agarwood (Aquilaria malaccensis) diversity conservation by in vitro culture with IAA and yeast extract. Biodiversitas 23: 2457-2463. Agarwood (Aquilaria malaccensis) is a scare plant with high economic value and many benefits. Tissue culture is an effective way of producing plants in large quantities, in a short time with uniform results. This study aims to get the proper concentration of IAA and yeast extract for agarwood growth in vitro. The research was conducted in December 2019-June 2020 at the Laboratory of Plant Physiology and Plant Biotechnology, Faculty of Agriculture, Universitas Sebelas Maret, Surakarta. The design used is Randomized Complete Block Design (RCBD) in 2 factors: first factor was IAA concentration with 4 level: 0; 0,5; 1; 1.5 mgL-1 and second factor was yeast extract concentration with 4 levels: 0; 200; 400; 800 mgL-1. Obtained 16 combinations of treatments repeated 3 times so that there are 48 units of experiments. Observed variables: the time of appearance of shoots, the number of shoots, the height of shoots, the number of leaves, and the number of roots. Root count data is described and data other than root count is analyzed ANOVA, if real influence is further tested DMRT 5%. The IAA affects the height of shoots and the number of agarwood leaves in vitro rooted plantlets has 5 plantlets. Agarwood ; Shoot ; Randomized block design ; Horticulture ; Biology ; Yeast ; Completely randomized design ; Botany ; Medicine Wood and Agarwood Research ; Agricultural and Environmental Management ; Plant tissue culture and regeneration
Authors: Adrià Luque | David Parent | Adrià Colomé | Carlos Ocampo‐Martínez | Carme Torras
Source: arXiv (Cornell University) (preprint, 2022-09-13)
Field: Materials Science — Polymers and Plastics

Model Predictive Control for Dynamic Cloth Manipulation: Parameter Learning and Experimental Validation Robotic cloth manipulation is a relevant challenging problem for autonomous robotic systems. Highly deformable objects as textile items can adopt multiple configurations and shapes during their manipulation. Hence, robots should not only understand the current cloth configuration but also be able to predict the future possible behaviors of the cloth. This paper addresses the problem of indirectly controlling the configuration of certain points of a textile object, by applying actions on other parts of the object through the use of a Model Predictive Control (MPC) strategy, which also allows to foresee the behavior of indirectly controlled points. The designed controller finds the optimal control signals to attain the desired future target configuration. The explored scenario in this paper considers tracking a reference trajectory with the lower corners of a square piece of cloth by grasping its upper corners. To do so, we propose and validate a linear cloth model that allows solving the MPC-related optimization problem in real time. Reinforcement Learning (RL) techniques are used to learn the optimal parameters of the proposed cloth model and also to tune the resulting MPC. After obtaining accurate tracking results in simulation, the full control scheme was implemented and executed in a real robot, obtaining accurate tracking even in adverse conditions. While total observed errors reach the 5 cm mark, for a 30x30 cm cloth, an analysis shows the MPC contributes less than 30% to that value. Model predictive control ; Computer science ; Controller (irrigation) ; Trajectory ; Reinforcement learning ; Control theory (sociology) ; Object (grammar) ; Robot ; Control (management) ; Artificial intelligence ; Tracking (education) Textile materials and evaluations
Authors: Said Mikki
Source:  (preprint, 2022-07-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Shannon Information Capacity of an Arbitrary Radiating Surface: An Electromagnetic Approach &lt;p&gt;Utilizing a crossdisciplinary approach, we explore Shannon information-theoretic characterizations of the information capacity limits of generic electromagnetic surfaces intended for possible use in wireless communication links. Our principal task is to first formulate at a general and rigorous level the electromagnetic theory of the information that can be extracted from the Maxwellian fields radiated by an arbitrarily-shaped continuous surface. This is then followed by a detailed derivation and illustration of practical physics-informed algorithms for computing approximations of the Shannon capacity of surfaces with any given geometry operating in Gaussian (AWGN) channels. Our formalism can address both near- and far-field information capacity scenarios, with a mathematical treatment that includes a complete characterization of the source-field polarization structure, mutual coupling, and interactions.&lt;/p&gt; Information theory ; Mutual information ; Additive white Gaussian noise ; Channel capacity ; Gaussian ; Electromagnetic field ; Wireless ; Formalism (music) ; Conditional mutual information ; Computer science ; Electromagnetic theory ; Polarization (electrochemistry) ; Mathematics ; Physics ; Topology (electrical circuits) ; Telecommunications ; Channel (broadcasting) ; Quantum mechanics ; Optics ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Advanced Antenna and Metasurface Technologies
Authors: Yu Shi | Shuxin Zheng | Guolin Ke | Yifei Shen | Jiacheng You | Jiyan He | Shengjie Luo | Chang Liu | Di He | Tie‐Yan Liu
Source: arXiv (Cornell University) (preprint, 2022-02-28)
Field: Materials Science — Materials Chemistry

An Empirical Study of Graphormer on Large-Scale Molecular Modeling Datasets This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation. The "Graphormer-V2" could attain better results on large-scale molecular modeling datasets than the vanilla one, and the performance gain could be consistently obtained on downstream tasks. In addition, we show that with a global receptive field and an adaptive aggregation strategy, Graphormer is more powerful than classic message-passing-based GNNs. Graphormer-V2 achieves much less MAE than the vanilla Graphormer on the PCQM4M quantum chemistry dataset used in KDD Cup 2021, where the latter one won the first place in this competition. In the meanwhile, Graphormer-V2 greatly outperforms the competitors in the recent Open Catalyst Challenge, which is a competition track on NeurIPS 2021 workshop, and aims to model the catalyst-adsorbate reaction system with advanced AI models. All models could be found at \url{https://github.com/Microsoft/Graphormer}. Computer science ; Competition (biology) ; Competitor analysis ; Scale (ratio) ; Downstream (manufacturing) ; Molecular dynamics ; Field (mathematics) ; Data science ; Chemistry ; Mathematics ; Biology Machine Learning in Materials Science ; Cloud Computing and Resource Management ; Advanced Graph Neural Networks
Authors: Vikas Tiwari | Sonali Garg | Tarak Karmakar
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-11-28)
Field: Materials Science — Materials Chemistry

Insights into the Interactions of Peptides with Monolayer-Protected Metal Nanoclusters Abstract Monolayer-protected atomically precise metal nanoclusters (MPC) are an important class of molecules that have potential applications in catalysis, imaging, and drug delivery. Recent studies have shown that peptide-based drugs can be complexed with MPCs to avoid enzymatic degradation and get delivered to targeted cells. Although the MPCs potential role in imaging and drug delivery processes have been studied, for their impactful use, specific molecular interactions between MPCs and biomolecules, mainly proteins and peptides should be explored in detail. In this work, we have carried out atomistic molecular dynamics simulations to investigate the interactions between Au-based MPCs and an anticancer peptide, melittin. The MEL peptides get attached to the MPCs surface by the formation of multiple hydrogen bonds between the peptide amino acid residues with MPCs ligands. Additionally, the positively charged residues such as Lys and Arg, the Trp, and the N-terminal of the peptide anchor strongly to the MPC core playing a crucial role in the peptide’s overall stabilization on the MPC surface. Nanoclusters ; Peptide ; Biomolecule ; Melittin ; Monolayer ; Drug delivery ; Chemistry ; Molecular dynamics ; Combinatorial chemistry ; Molecule ; Biophysics ; Hydrogen bond ; Peptide sequence ; Nanotechnology ; Materials science ; Biochemistry ; Computational chemistry ; Organic chemistry ; Biology Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Huaiqin Ma | Qingwen Shi | Xuhua Li | Junli Ren | Zhijian Li | Lulu Ning
Source: Research Square (preprint, 2022-09-21)
Field: Materials Science — Biomaterials

Molecular and Thermodynamic Insights into Interfacial Interactions Between Collagen and Cellulose Investigated by Molecular Dynamics Simulation and Umbrella Sampling Abstract Cellulose/collagen composites have been widely used in biomedicine and tissue engineering. Interfacial interactions are crucial in determining the final properties of cellulose/collagen composite. Molecular dynamics simulations were carried out to gain insights into the interactions between cellulose and collagen. It has been found that the structure of collagen remained intact during adsorption. The results derived from umbrella sampling showed that (110) and (1-10) faces exhibited the strongest affinity with collagen, (100) face came the second and (010) the last, which could be attributed to the surface roughness and hydrogen-bonding linkers involved water molecules. Cellulose planes with flat surfaces and the capability to form hydrogen-bonding linkers produce stronger affinity with collagen. The occupancy of hydrogen bonds formed between cellulose and collagen was low and not significantly contributive to the binding affinity. These findings provided insights into the interactions between cellulose and collagen at the molecular level, which may guide the design and fabrication of cellulose/collagen composites. Cellulose ; Hydrogen bond ; Molecular dynamics ; Adsorption ; Molecule ; Umbrella sampling ; Chemical engineering ; Materials science ; Composite number ; Chemistry ; Polymer chemistry ; Composite material ; Computational chemistry ; Organic chemistry Advanced Cellulose Research Studies ; Material Properties and Processing