Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical Methods for the Detection and Quantification of Trenbolone Acetate Metabolites, Altrenogest and Related Photoproducts via Liquid Chromatography Tandem Mass Spectrometry

9 11 1.0 Introduction.10 12 2.0 Experimental.12 13 2.1 Reagents and Materials.12 14 2.2 Sample Preparation.13 15 2.3 Photoreaction.13 16 2.4 Solid Phase Extraction.14 17 2.5 Instrumental Analysis.15 18 3.0 Results and Discussion.15 19 3.1 Optimization of LC-MS/MS Conditions.15 20 3.2 Cationic Adduct Formation.17 21
Dynamic Loading Directed Neural Stem Cell Differentiation

iv Chapter 1: Introduction . 1 1.1 Motivation . 1 1.2 The Need of in Vitro Stem Cell Differentiation to Generate Neural Cells . 2 1.3 Factor Affecting the Cell Differentiation towards Neural Cells . 4 1.4 PC-12 Cell Lines . 6 Chapter 2: Experiment . 8 2.1 Materials . 8 2.2 Equipment . 9 2.3 Dynamic Loading Model . 10 2.3.1 Design of The Loading Device . 10 2.3.2 3D Printing of The Device . 10 2.3. 3 Assembly and Characterization of Device . 11 2.4 Scaffold Design and Manufacturing . 12 2.4. 1 TPU Scaffolds Design and Manufacturing . 12 2.4. 2 PCL Scaffolds Design and Manufacturing . 13 2.4. 3 PDMS Scaffolds Design and Manufacturing . 14 2.5 Scaffold Surface Modification . 15 2.6 Stem Cell Culturing and Differentiation . 22 2.7 Characterization . 24 Chapter 3: Dynamic Load Enhanced Stem Cell Differentiation to Neuron Cells . 28 3.1 Cell Preparation . 28 3.2 Differentiation Results . 28 Chapter 4: Conclusion. 33 Chapter 5: Future Vision and Limitations . 34 References . 35
Challenges of Curating for Reproducible and FAIR Research Output

: The goal of the CURE-FAIR WG is to establish standards-based guidelines for curating for reproducible and FAIR data and code. The final deliverable of the Working Group (WG) is a document outlining CURE-FAIR standards-based guidelines for best practices in publishing and archiving computationally reproducible studies. To support the deliverable, the WG created four subgroups, each tasked with studying and summarizing a particular aspect of this work: CURE-FAIR Definitions, Practices, Challenges
Impact of Adding Ellagic Acid to Red Grapes on the Phenolic Composition and Chromatic Quality of Cabernet Sauvignon Wines from a Warm Climate

In this study, the effect of adding ellagic acid (Ea) before alcoholic fermentation on the phenolic composition and chromatic quality of Cabernet Sauvignon wines were investigated. The evolution of the color and polyphenolics was determined by tristimulus colorimetry and HPLC-MS during the industrial-scale winemaking process, which showed that the prefermentation addition of Ea could obtain more saturated and vivid hue wines, consistent with the significant increase in anthocyanins concentration. The copigmentation effect induced by Ea was also confirmed by the higher copigmentation degree in the treated samples. Thus, the addition of Ea could provide a simple and useful enological technique to enhance the red wine quality, improve the intensity and stability of wine color, and can be employed as an alternative strategy for obtaining high-quality red wines from a warm climate region (e.g., Xinjiang Uygur Autonomous Region, northwest China). Practical Application The main objective of this work is to evaluate the effect of adding Ea before alcoholic fermentation on color and phenolic compounds of red wines, which could be helpful in reasonably application of copigment additives and enlargement the understanding of the chemistry of interaction between this copigment and anthocyanins. Although the Ea is currently not approved for use in wines in somewhere of world, this addition treatment could provide a useful enological technique to enhance the intensity and stability of red wine color. For this reason, we believe that this study is valuable in providing useful insights for future research.
The Effects of Transformational Leadership on Organizational Performance: Testing the Mediating Effects of Knowledge Management

Transformational leadership is a sustainable and exemplary form of leadership that liberates subordinates’ ideas, enables subordinates to view problems from a different perspective, helps adaptation to changing environments, and increases Human Resources Management effectiveness in public and service institutions. The effects of transformational leadership on organizational performance by means of knowledge management have been examined in this study on the northern Cyprus Security Forces. In addition, the study aims to analyze the relationships between transformational leadership, knowledge management, organizational performance, job satisfaction, organizational learning, and knowledge creation processes. The questionnaire was conducted in three different periods using a time-lag method, and data were collected from 1229 employees for this study. The collected data were analyzed using structural equation modeling (Lisrel 8.54). The findings of the research include 17 hypotheses that were supported, and 1 hypothesis that was not. In addition, it was determined that organizational learning, knowledge management, and job satisfaction mediate the effect of transformational leadership on organizational performance.
Crystallization of Calcium Oxalate and Calcium Phosphate at Supersaturation Levels Corresponding to Those in Different Parts of the Nephron

The risk of crystallization in solutions, with a composition corresponding to that of urine in various parts of the nephron, was assessed by studying urine samples containing standardized increments of calcium and oxalate concentrations. The experiments were carried out in salt solutions with and without dialysed urine and the appearance of crystals was detected and measured with a Coulter counter. With increased concentrations of calcium, crystals of calcium phosphate were predominantly formed in solutions with a composition corresponding to that in the proximal and distal tubuli, whereas, calcium oxalate was the primary crystallization product in solutions with a composition corresponding to the collecting duct. These conclusions were based on calculations of ion-activity products of calcium oxalate, hydroxyapatite, and calcium hydrogen phosphate, at the first appearance of crystals; studies on crystal morphology with scanning electron microscopy, and precipitation of [14C]-oxalate following addition of calcium. The ionactivity products of calcium oxalate at the first appearance of crystals following addition of oxalate to solutions with a composition corresponding to the collecting duct were significantly lower in the presence of dialysed urine. This might reflect a promoting effect of some macromolecules on the nucleation of calcium oxalate. Dialysed urine in these samples also had a pronounced influence on the development of the crystals by markedly reducing the mean crystal volume during the first hour following the formation of 100 crystals with a diameter between 3.5 and 5 μm. Such an effect was not observed in those solutions in which calcium phosphate crystals had formed.
A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks

In this paper, we implement a combination of technical analysis and machine/deep learning-based analysis to build a trend classification model. The goal of the paper is to apprehend short-term market movement, and incorporate it to improve the underlying stochastic model. Also, the analysis presented in this paper can be implemented in a \emph{model-independent} fashion. We execute a data-science-driven technique that makes short-term forecasts dependent on the price trends of current stock market data. Based on the analysis, three different labels are generated for a data set: $+1$ (buy signal), $0$ (hold signal), or $-1$ (sell signal). We propose a detailed analysis of four major stocks- Amazon, Apple, Google, and Microsoft. We implement various technical indicators to label the data set according to the trend and train various models for trend estimation. Statistical analysis of the outputs and classification results are obtained.
Speeding Up Permutation Invariant Training for Source Separation

Permutation invariant training (PIT) is a widely used training criterion for neural network-based source separation, used for both utterance-level separation with utterance-level PIT (uPIT) and separation of long recordings with the recently proposed Graph-PIT. When implemented naively, both suffer from an exponential complexity in the number of utterances to separate, rendering them unusable for large numbers of speakers or long realistic recordings. We present a decomposition of the PIT criterion into the computation of a matrix and a strictly monotonously increasing function so that the permutation or assignment problem can be solved efficiently with several search algorithms. The Hungarian algorithm can be used for uPIT and we introduce various algorithms for the Graph-PIT assignment problem to reduce the complexity to be polynomial in the number of utterances.
Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics

We study the convergence in total variation and $V$-norm of discretization schemes of the underdamped Langevin dynamics. Such algorithms are very popular and commonly used in molecular dynamics and computational statistics to approximatively sample from a target distribution of interest. We show first that, for a very large class of schemes, a minorization condition uniform in the stepsize holds. This class encompasses popular methods such as the Euler-Maruyama scheme and the schemes based on splitting strategies. Second, we provide mild conditions ensuring that the class of schemes that we consider satisfies a geometric Foster--Lyapunov drift condition, again uniform in the stepsize. This allows us to derive geometric convergence bounds, with a convergence rate scaling linearly with the stepsize. This kind of result is of prime interest to obtain estimates on norms of solutions to Poisson equations associated with a given numerical method.
Distinguishing threshold of graphs

A vertex coloring of a graph G $G$ is called distinguishing if no nonidentity automorphisms of G $G$ can preserve it. The distinguishing number of G $G$ , denoted by D ( G ) $D(G)$ , is the minimum number of colors required for such a coloring, and the distinguishing threshold of G $G$ , denoted by θ ( G ) $\theta (G)$ , is the minimum number k $k$ such that every k $k$ ‐coloring of G $G$ is distinguishing. As an alternative definition, θ ( G ) $\theta (G)$ is one more than the maximum number of cycles in the cycle decomposition of automorphisms of G $G$ . In this paper, we characterize θ ( G ) $\theta (G)$ when G $G$ is disconnected. Afterwards, we prove that, although for every positive integer k ≠ 2 $k\ne 2$ there are infinitely many graphs whose distinguishing thresholds are equal to k $k$ , we have θ ( G ) = 2 $\theta (G)=2$ if and only if ∣ V ( G ) ∣ = 2 $| V(G)| =2$ . Moreover, we show that if θ ( G ) = 3 $\theta (G)=3$ , then either G $G$ is isomorphic to one of the four graphs on three vertices or it is of order 2 p $2p$ , where p ≠ 3 , 5 $p\ne 3,5$ is a prime number. Furthermore, we prove that θ ( G ) = D ( G ) $\theta (G)=D(G)$ if and only if G $G$ is asymmetric, K n ${K}_{n}$ or K n ¯ $\bar{{K}_{n}}$ . Finally, we consider all generalized Johnson graphs, J ( n , k , i ) $J(n,k,i)$ , which are the graphs on all k $k$ ‐subsets of { 1 , … , n } $\{1,\ldots ,n\}$ where two vertices A $A$ and B $B$ are adjacent if ∣ A ∩ B ∣ = k − i $| A\cap B| =k-i$ . After studying their automorphism groups and distinguishing numbers, we calculate their distinguishing thresholds as θ ( J ( n , k , i ) ) = n k − n − 2 k − 1 + 1 $\theta (J(n,k,i))=\left(\genfrac{}{}{0ex}{}{n}{k}\right)-\left(\genfrac{}{}{0ex}{}{n-2}{k-1}\right)+1$ , unless k = n 2 $k=\frac{n}{2}$ and i ∈ { k 2 , k } $i\in \{\frac{k}{2},k\}$ in which case we have θ ( J ( n , k , i ) ) = n k $\theta (J(n,k,i))=\left(\genfrac{}{}{0ex}{}{n}{k}\right)$ .
WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks

The Word-in-Context (WiC) task has attracted considerable attention in the NLP community, as demonstrated by the popularity of the recent MCL-WiC SemEval shared task. Systems and lexical resources from word sense disambiguation (WSD) are often used for the WiC task and WiC dataset construction. In this paper, we establish the exact relationship between WiC and WSD, as well as the related task of target sense verification (TSV). Building upon a novel hypothesis on the equivalence of sense and meaning distinctions, we demonstrate through the application of tools from theoretical computer science that these three semantic classification problems can be pairwise reduced to each other, and therefore are equivalent. The results of experiments that involve systems and datasets for both WiC and WSD provide strong empirical evidence that our problem reductions work in practice.
Theory of the line shape of the 1S–2S transition for magnetically trapped antihydrogen

The physics that determines the line shape of the 1S–2S transition in magnetically trapped H¯ is explored. Besides obtaining an understanding of the line shape, one goal is to replace the dependence on large scale simulations of H¯ with a simpler integration over well defined functions. For limiting cases, analytic formulas are obtained. Example calculations are performed to illustrate the limits of simplifying assumptions. We also describe a χ 2 method for choosing experimental parameters that can lead to the most accurate determination of the transition frequency.
Temporal Dependencies in Feature Importance for Time Series Predictions

Time series data introduces two key challenges for explainability methods: firstly, observations of the same feature over subsequent time steps are not independent, and secondly, the same feature can have varying importance to model predictions over time. In this paper, we propose Windowed Feature Importance in Time (WinIT), a feature removal based explainability approach to address these issues. Unlike existing feature removal explanation methods, WinIT explicitly accounts for the temporal dependence between different observations of the same feature in the construction of its importance score. Furthermore, WinIT captures the varying importance of a feature over time, by summarizing its importance over a window of past time steps. We conduct an extensive empirical study on synthetic and real-world data, compare against a wide range of leading explainability methods, and explore the impact of various evaluation strategies. Our results show that WinIT achieves significant gains over existing methods, with more consistent performance across different evaluation metrics. The code for our work is publicly available at \url{https://github.com/layer6ai-labs/WinIT}.
Geometrical dynamics of edge-driven accretive surface growth

Accretion of mineralized thin wall-like structures via localized growth along their edges is observed in physical and biological systems ranging from molluscan and brachiopod shells to carbonate–silica composite precipitates. To understand the shape of these mineralized structures, we develop a mathematical framework that treats the thin-walled shells as a smooth surface left in the wake of the growth front that can be described as an evolving space curve. Our theory then takes an explicit geometric form for the prescription of the velocity of the growth front curve, along with compatibility relations and a closure equation related to the nature of surface curling. Solutions of these equations capture a range of geometric precipitate patterns seen in abiotic and biotic forms across scales. In addition to providing a framework for the growth and form of these thin-walled morphologies, our theory suggests a new class of dynamical systems involving moving space curves that are compatible with non-Euclidean embeddings of surfaces.
Fabry–Pérot cavities and quantum dot formation at gate-defined interfaces in twisted double bilayer graphene

The rich and electrostatically tunable phase diagram exhibited by moiré materials has made them a suitable platform for hosting single material, multi-purpose devices. To engineer such devices, understanding electronic transport and localization across electrostatically defined interfaces is of fundamental importance. Little is known, however, about how the interplay between the band structure originating from the moiré lattice and electric potential gradients affects electronic confinement. Here, we electrostatically define a cavity across a twisted double bilayer graphene sample. We observe two kinds of Fabry–Pérot (FP) oscillations. The first, independent of charge polarity, stems from confinement of electrons between dispersive-band/flat-band interfaces. The second arises from junctions between regions tuned into different flat bands. When tuning the out-of-plane electric field across the device, we observe Coulomb blockade resonances in transport. This is an indication of strong electronic confinement. From the gate, magnetic field and source-drain voltage dependences of the resonances, we conclude that quantum dots form at the interfaces of the FP cavity. Our results constitute a first step toward better understanding interfacial phenomena in single crystal moiré devices.
Spectroscopic modelling of two high-mass X-ray binaries, Cyg X–3 and 4U 1538–522

We report a detailed modelling of soft X-ray emission lines from two stellar wind-fed Galactic high-mass X-ray binary (HMXB) systems, Cyg X-3 and 4U 1538-522, and estimate physical parameters, e.g. hydrogen density, radiation field, chemical abundances, wind velocity, etc. The spectral synthesis code cloudy is utilized for this modelling. We model highly ionized X-ray spectral lines, such as Fe XXV (6.700 keV) and Fe XXVI (6.966 keV), and reproduce the observed line flux values. We find that for Cyg X–3 and 4U 1538-522, the inner radius of the ionized gas is at a distance of 1012.25 cm and 1010.43 cm, respectively, from the primary star, which is the main source of ionization. The densities of the ionized gas for Cyg X–3 and 4U 1538–522 are found to be ∼1011.35 cm−3 and 1011.99 cm−3, respectively. The corresponding wind velocities are 2000 km s−1 and 1500 km s−1. The respective predicted hydrogen column densities for Cyg X–3 and 4U 1538–522 are 1023.2 cm−2 and 1022.25 cm−2. In addition, we find that magnetic field affects the strength of the spectral lines through cyclotron cooling. Hence, we perform separate model comparisons including magnetic field for both the sources. Most of the parameters, except the hydrogen column density, have similar values with and without magnetic field. We estimate that the most probable strength of the magnetic field for Cyg X–3 and 4U 1538–522, where the Fe XXV and Fe XXVI lines originate, is ∼102.5 G.
Artist Similarity with Graph Neural Networks

Artist similarity plays an important role in organizing, understanding, and subsequently, facilitating discovery in large collections of music. In this paper, we present a hybrid approach to computing similarity between artists using graph neural networks trained with triplet loss. The novelty of using a graph neural network architecture is to combine the topology of a graph of artist connections with content features to embed artists into a vector space that encodes similarity. To evaluate the proposed method, we compile the new OLGA dataset, which contains artist similarities from AllMusic, together with content features from AcousticBrainz. With 17,673 artists, this is the largest academic artist similarity dataset that includes content-based features to date. Moreover, we also showcase the scalability of our approach by experimenting with a much larger proprietary dataset. Results show the superiority of the proposed approach over current state-of-the-art methods for music similarity. Finally, we hope that the OLGA dataset will facilitate research on data-driven models for artist similarity.
DQ-SGD: Dynamic Quantization in SGD for Communication-Efficient Distributed Learning

Gradient quantization is an emerging technique in reducing communication costs in distributed learning. Existing gradient quantization algorithms often rely on engineering heuristics or empirical observations, lacking a systematic approach to dynamically quantize gradients. This paper addresses this issue by proposing a novel dynamically quantized SGD (DQ-SGD) framework, enabling us to dynamically adjust the quantization scheme for each gradient descent step by exploring the trade-off between communication cost and convergence error. We derive an upper bound, tight in some cases, of the convergence error for a restricted family of quantization schemes and loss functions. We design our DQSGD algorithm via minimizing the communication cost under the convergence error constraints. Finally, through extensive experiments on large-scale natural language processing and computer vision tasks on AG-News, CFAR-10, and CIFAR-100 datasets, we demonstrate that our quantization scheme achieves better tradeoffs between the communication cost and learning performance than other state-of-the-art gradient quantization methods.
Assessing the signatures imprinted by star-forming galaxies in the cosmic gamma-ray background

In recent years, γ-ray emission has been detected from star-forming galaxies (SFGs) in the local universe, including M82, NGC 253, Arp 220 and M33. The bulk of this emission is thought to be of hadronic origin, arising from the interactions of cosmic rays (CRs) with the interstellar medium of their host galaxy. Distant SFGs are presumably also bright in γ-rays. Although they would not be resolvable as point sources, distant unresolved SFG populations contribute γ-rays to the extra-galactic γ-ray background (EGB). Despite the wealth of high-quality all-sky EGB data collected over more than a decade of operation with the Fermi-LAT γ-ray space telescope, the exact contribution of SFGs to the EGB remains unsettled. In this study, we model the γ-ray emission from SFG populations and demonstrate that such emission can be characterized by just a small number of physically-motivated parameters. We further show that source populations would leave anisotropic signatures in the EGB, which could be used to yield information about the underlying properties, dynamics and evolution of CR-rich SFGs.
Higher-order topological phase induced by hybrid magneto-electric resonances

Rapid development of topological concepts in photonics unveils exotic phenomena such as unidirectional propagation of electromagnetic waves resilient to backscattering at sharp bends and disorder-immune localization of light at stable frequencies. Recently introduced higher-order topological insulators (HOTIs) bring in additional degrees of control over light confinement and steering. However, designs of photonic HOTIs reported so far are solely exploiting lattice geometries which are hard to reconfigure thus limiting tunability. Here, we elaborate a conceptually new mechanism to engineer higher-order topological phases which relies on the dual nature of electromagnetic field and exploits both electric and magnetic responses of the meta-atoms. Hybridization between these responses gives rise to the difference in the effective coupling which is controlled by the meta-atoms mutual orientations. This feature facilitates us to tailor photonic band topology exclusively via particle alignment and to flexibly reconfigure the topological phase. Focusing on the kagome array of split-ring resonators, we experimentally demonstrate topological edge and corner states in the microwave domain. Our findings provide a new promising route to induce and control higher-order topological phases and states.