Arbitrary Samples Research Articles

Shaping entangled photons through arbitrary scattering media using an advanced wave beacon

Entangled photons provide transformative new paths in the fields of communication, sensing, and computing. However, when entangled photons propagate through a complex medium, their correlations are scrambled. Using wavefront shaping to compensate for the scattering and retrieve the two-photon correlations is challenging due to the low signal-to-noise ratio of the two-photon signal. While previous works partly addressed this challenge by using feedback from a strong classical laser beam that co-propagates with the entangled photons, such methods frequently depend on assumptions about the complex medium, limiting the applicability of quantum wavefront shaping. In this work, we propose and demonstrate a new feedback mechanism that is inspired by Klyshko’s advanced wave picture: the classical laser beam is emitted in one of the detection modes, counter-propagates with one of the entangled photons, reflects at the crystal plane, and co-propagates with the other. The new Klyshko feedback allows compensation of scattering in arbitrary samples and even in situations where each photon propagates through a different scattering medium. Since the advanced wave picture applies whenever optical reciprocity is valid, such Klyshko optimization can be used across a wide range of configurations, offering a robust and alignment-free setup. We therefore believe this protocol will open the door for real-world applications of quantum wavefront shaping.

Forester: Approximate Processing of an Imperative Procedure for Query-Time Exploratory Data Analysis in a Relational Database

Query-time Exploratory Data Analysis (qEDA) is an increasingly demanding aspect of the data analysis process that entails visually and quantitatively summarizing, comprehending, and interpreting the primary characteristics of a dataset. Nowadays, an imperative procedure is popular in relational databases for EDA because it enables us to write multiple dependent declarative queries with imperative logic. As online analytical processing (OLAP) systems contain extremely large datasets, data scientists often need quick visualizations of data, using approximate processing of imperative procedures, before analyzing them in their entirety. We identify gaps in the existing techniques, in that they are unable to sample both declarative-dependent statements and control logic at the same time and perform multi-dependent sampling-based approximate processing within the permitted time in qEDA. Traditional approximate query processing (AQP) involves tuple sampling for a single query approximation and enables queries to be executed over arbitrary random samples of tables. However, available AQP methods cannot produce a further representative sample of the data distribution for the dependent statements to estimate accurately and quickly for multiple dependent statements. On the other hand, sampling control structures, like loops and conditional statements, are discussed separately, without regard to the imperative structure of statements in a procedure. In this study, we propose Forester, a novel agile approximate processing method for imperative procedures that performs imperative program-aware sampling, which includes both statements with control regions (i.e., branch and loop) and processes them approximately within the permitted time in qEDA. Our method produces more targeted samples for each relation, while maintaining the data and control flow of dependent queries and imperative logic and determining all the conditions for a relation across all the statements in the sample that guarantee the existence of relevant data for dependent data distribution. Utilizing a workload of multi-statement imperative procedures from the Transaction Processing Performance Council Decision Support (TPC-DS) database, our experiment demonstrates that Forester outperforms the existing system in sampling, producing minimum error, and improving response time.

Facile gamma-ray induced synthesis of reduced graphene oxide decorated with silver nanoparticles: a green approach for symmetric supercapacitor applications

This study presents a green, novel, one-pot, and single-step method for synthesizing reduced graphene oxide/silver nanocomposites (RGO/Ags) with different Ag ratios using Roselle extract and 80kGy of γ-ray for symmetric supercapacitor applications. As a natural antioxidant, Roselle extract served as a scavenger of oxidative free radicals to inhibit the oxidation of graphene oxide/silver nitrate suspensions (GO/AgO3), which were undergoing radiolysis under γ irradiation. An additional sample of GO/AgO3 was irradiated with the same dose in the absence of Roselle for comparability. The efficient reduction of GO/AgO3 has been assessed by several analytical techniques, including XRD, FTIR, TEM, and Raman spectroscopy. The presence of Roselle hindered the formation of the AgO phase while concurrently promoting the construction of the Ag phase. Furthermore, using Roselle extract results in a more homogeneous dispersion of spherical silver nanoparticles on reduced graphene oxide (RGO) sheets. Symmetric supercapacitor has been assembled from arbitrary samples amongst RGO/Ags, which showed good performance like low internal resistance and suitable specific capacitance (96 F/g at current density 0.2 A/g). The findings point to the merit of RGO/Ag nanocomposites prepared by the synergetic effect of γ-ray and Roselle extract as an efficient supercapacitor material.

Locating cellular contents during cryoFIB milling using cellular secondary-electron imaging

Cryo-electron tomography (cryoET) is a powerful technology that allows in-situ observation of the molecular structure of tissues and cells. Cryo-focused ion beam (cryoFIB) milling plays an important role in the preparation of high-quality thin lamellar samples for cryoET studies, thus, promoting the rapid development of cryoET in recent years. However, locating the regions of interest in a large cell or tissue during cryoFIB milling remains a major challenge limiting cryoET applications on arbitrary biological samples. Here, we report an on-the-fly localization method based on cellular secondary electron imaging (CSEI), which is derived from a basic imaging function of the cryoFIB instruments and enables high-contrast imaging of the cellular contents of frozen-hydrated biological samples. Moreover, CSEI does not require fluorescent labels and additional devices. The present study discusses the imaging principles and settings for optimizing CSEI. Tests on several commercially available cryoFIB instruments demonstrated that CSEI was feasible on mainstream instruments to observe all types of cellular contents and reliable under different milling conditions. We established a simple milling-localization workflow and tested it using the basal body of Chlamydomonas reinhardtii.

Self-Organizing Interval Type-2 Fuzzy Neural Network With Adaptive Discriminative Strategy

Covariate shift is a critical issue of interval type-2 fuzzy neural networks (IT2FNNs) due to the distribution discrepancy between training and testing samples. In this situation, IT2FNNs usually struggle to identify potential features from samples with explicit inductive biases. To address this problem, a self-organizing IT2FNN with an adaptive discriminative strategy (ADS-SOIT2FNN) is developed to maintain the identification performance in the presence of covariate shift. First, a granularity-based metric (GM), using higher-order statistics of local samples, is designed to distinguish the distribution discrepancy caused by covariate shift. The multiple kernels incorporated into GM are able to cover the sample features of the whole Hilbert space. Second, a self-organizing strategy, associated with GM-based discriminative information, is presented to alleviate the structural bias by growing and pruning fuzzy rules. Then, a compact structure of ADS-SOIT2FNN is achieved to adapt to the covariate shift of samples and further strengthen its inductive ability. Third, an adaptive risk mitigation learning algorithm (RMLA) is introduced to update the parameters of ADS-SOIT2FNN. RMLA can regulate the derivatives of parameters with arbitrary distribution samples, which is beneficial for maintaining the global accuracy by relieving the risk of parameter biases. Finally, the effectiveness of ADS-SOIT2FNN is verified by some experiments for identifying nonlinear systems with covariate shift.

Intelligent backpropagated predictive networks for dynamics of the power-law fluidic model with moving wedge and flat plate

A novel application of intelligent computing is presented to study the dynamics of the power-law fluidic problem (PLFP) of moving-wedge and flat-plate models using a multilayer structure of feedforward neural networks (FFNNs) optimized with the Levenberg-Marquardt method (LMM), i.e. FFNN-LMM. The reference dataset for inputs and targets of FFNNs is generated through the Adams numerical solver for the non-Newtonian power-law fluidic system in the case of dilatant, pseudo-plastic, and other thinning/thickening fluids with different values of wedge angle and velocity ratio parameters. The designed FFNNs backpropagated with LMM are constructed with the help of arbitrary samples for training, testing, and validation via mean squared error (MSE)-based figure of merit. Comparison of the proposed computing platform FFNN-LMM with reference standard solutions in terms of MSE, state transition statistics of the algorithm, error histogram studies, and regression outcomes endorse the accuracy and convergence for solving different scenarios in PLFPs.

A Multi-Dimensional Matrix Pencil-Based Channel Prediction Method for Massive MIMO With Mobility

This paper addresses the mobility problem in massive multiple-input multiple-output systems, which leads to significant performance losses in the practical deployment of the fifth generation mobile communication networks. We propose a novel channel prediction method based on multi-dimensional matrix pencil (MDMP), which estimates the path parameters by exploiting the angular-frequency-domain and angular-time-domain structures of the wideband channel. The MDMP method also entails a novel path pairing scheme to pair the delay and Doppler, based on the super-resolution property of the angle estimation. Our method is able to deal with the realistic constraint of time-varying path delays introduced by user movements, which has not been considered so far in the literature. We prove theoretically that in the scenario with time-varying path delays, the prediction error converges to zero with the increasing number of the base station (BS) antennas, providing that only two arbitrary channel samples are known. We also derive a lower-bound of the number of the BS antennas to achieve a satisfactory performance. Simulation results under the industrial channel model of 3GPP demonstrate that our proposed MDMP method approaches the performance of the stationary scenario even when the users’ velocity reaches 120 km/h and the latency of the channel state information is as large as 16 ms.

Accurate quantitative phase imaging by differential phase contrast with partially coherent illumination: beyond weak object approximation

Quantitative phase imaging (QPI) by differential phase contrast (DPC) with partially coherent illumination provides speckle-free imaging and lateral resolution beyond the coherent diffraction limit, demonstrating great potential in biomedical imaging applications. Generally, DPC employs weak object approximation to linearize the phase-to-intensity image formation, simplifying the solution to the phase retrieval as a two-dimensional deconvolution with the corresponding phase transfer function. Despite its widespread adoption, weak object approximation still lacks a precise and clear definition, suggesting that the accuracy of the QPI results, especially for samples with large phase values, is yet to be verified. In this paper, we analyze the weak object approximation condition quantitatively and explicitly give its strict definition that is applicable to arbitrary samples and illumination apertures. Furthermore, an iterative deconvolution QPI technique based on pseudo-weak object approximation is proposed to overcome the difficulty of applying DPC to large-phase samples without additional data acquisition. Experiments with standard microlens arrays and MCF-7 cells demonstrated that the proposed method can effectively extend DPC beyond weak object approximation to high-precision three-dimensional morphological characterization of large-phase technical and biological samples.

Measurement and correction of TEM image distortion using arbitrary samples.

We have developed a method to quantitatively measure image distortion, one of the five Seidel aberrations, in transmission electron microscopes without using a standard sample with a known structure. Displacements of small local segments in an image due to image distortion of the intermediate and projection lens system are first measured by comparing images taken before and after a given shift at the first image plane of the objective lens. Then, the sum of the second partial derivatives, or the Laplacian, of the displacement field is measured, and the radial and azimuthal distortion parameters are determined from the measured results. We confirmed using numerically distorted images that the proposed method can measure the image distortion within a relative error ratio of 0.04 for a wide range of distortion amount from 0.1% to 5.0%. The distortion measurement and correction were confirmed to work correctly by using the experimental images, and the iterative measurement and correction procedure could reduce the distortion to a level where the average image displacement was < 0.05 pixels.

Contrastive Feature Disentangling for Partial Aspect Angles SAR Noncooperative Target Recognition

Deep learning algorithms have achieved state-of-the-art progress in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. They theoretically assume that training and test samples are independent and identically distributed (i.i.d.) for generalization, but it is intractable for practical ATR scenarios. In this paper, we propose a novel contrastive feature disentangling framework termed ConFeDent to learn features with improved generalization performance under a condition of a weaker distribution consistency. More specifically, ConFeDent aims to describe the semantic interactions between two arbitrary SAR training samples instead of treating them independently. It can implicitly disentangle features encoding the pose and identity knowledge from the whole samples with a semi-parametric geometric transformation model and a second-order energy model. In particular, except for the identity label, we use deductive-based geometry knowledge as supervision to teach the model to learn the concept of aspect angle variation. A progressively amortized inference scheme is constructed for efficient feature learning and recognition in an end-to-end manner. Finally, we further release a strengthened version, called ConFeDent+, which can explicitly utilize and learn more information from cross-category samples. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) benchmark demonstrate the effectiveness of our proposed models in the SAR ATR. In particular, we validate the algorithms in a more challenging scenario where the range of aspect angles for training and testing samples is permitted to be disparate. Our model can achieve much higher recognition accuracy than other SAR ATR algorithms.

Mechanical Amorphization of Chitosan with Different Molecular Weights.

Mechanical amorphization of three chitosan samples with high, medium, and low molecular weight was studied. It is shown that there are no significant differences between the course of amorphization process in a planetary ball mill of chitosan with different molecular weights, and the maximum degree of amorphization was achieved in 600 s of high intensity mechanical action. Specific energy consumption was 28 kJ/g, being comparable to power consumption for amorphization of cellulose determined previously (29 kJ/g) and 5–7-fold higher than that for amorphization of starch (4–6 kJ/g). Different techniques for determining the crystallinity index (CrI) of chitosan (analysis of the X-ray diffraction (XRD) data, the peak height method, the amorphous standard method, peak deconvolution, and full-profile Rietveld analysis) were compared. The peak height method is characterized by a broader working range but provides deviated CrI values. The peak deconvolution method (with the amorphous Voigt function) makes it possible to calculate the crystallinity index of chitosan with greater accuracy, but the analysis becomes more difficult with samples subjected to mechanical processing. In order to refine the structure and calculation of CrI by the Rietveld method, an attempt to optimize the structure file by the density functional theory (DFT) method was performed. The averaged profile of amorphous chitosan approximated by an eighth-order Fourier model improved the correctness of the description of the amorphous contribution for XRD data processing. The proposed equation may be used as a universal standard model of amorphous chitosan to determine the crystallinity index both for the amorphous standard method and for peak deconvolution of XRD patterns for arbitrary chitosan samples.

Neural Flow Map Reconstruction

AbstractIn this paper we present a reconstruction technique for the reduction of unsteady flow data based on neural representations of time‐varying vector fields. Our approach is motivated by the large amount of data typically generated in numerical simulations, and in turn the types of data that domain scientists can generate in situ that are compact, yet useful, for post hoc analysis. One type of data commonly acquired during simulation are samples of the flow map, where a single sample is the result of integrating the underlying vector field for a specified time duration. In our work, we treat a collection of flow map samples for a single dataset as a meaningful, compact, and yet incomplete, representation of unsteady flow, and our central objective is to find a representation that enables us to best recover arbitrary flow map samples. To this end, we introduce a technique for learning implicit neural representations of time‐varying vector fields that are specifically optimized to reproduce flow map samples sparsely covering the spatiotemporal domain of the data. We show that, despite aggressive data reduction, our optimization problem — learning a function‐space neural network to reproduce flow map samples under a fixed integration scheme — leads to representations that demonstrate strong generalization, both in the field itself, and using the field to approximate the flow map. Through quantitative and qualitative analysis across different datasets we show that our approach is an improvement across a variety of data reduction methods, and across a variety of measures ranging from improved vector fields, flow maps, and features derived from the flow map.

A General Inheritance Algorithm for Calculating of Arbitrary Moving Samples During Microwave Heating

Moving models, such as translation, rotation, and other motion modes, are widely applied to improve the microwave heating uniformity. However, due to a variety of motion modes, the deformation of mesh, including discontinuity, crossing, and winding, usually occurs in the process of microwave heating calculation. It is a great challenge to numerical simulation of moving samples because of the lack of a common calculation method. In this article, a novel inheritance algorithm, which inherits both the grid and temperature distribution of the sample, can be used to calculate arbitrary moving models of microwave heating process. It is adopted to inherit the grid and temperature distribution in the finite element method to ensure that each temperature calculation result can perfectly correspond to the temperature value of these mesh nodes. By building a 3-D mathematical model of translation, swing, and rotation, the coupling process of electromagnetic field and thermal phenomena in microwave heating was performed and the proposed method was clearly illustrated. Finally, the calculated results are consistent with the experimental results, confirming the accuracy of the algorithm. Compared with the conventional algorithm, the proposed algorithm has a higher precision and practically and can be used to simulate microwave heating of arbitrary motions.

Chemical and Microbiological Assessment of Wastewater Discharged Along the Mediterranean Sea

The large discharges of wastewater in different natural receiving environments, particularly the marine environment, have massively contributed to pollution. This study examined the physicochemical and microbiological quality of wastewater discharged along the Mediterranean Sea. This investigation is one of the few works carried out by scientists along with the Mediterranean countries. Wastewater sampling was carried out in Egypt, Morocco, Algeria, Tunisia, and Italy. Analyses confirmed that the quality of treated wastewater was always better than the ones registered as raw wastewater. In addition, the quality of the treated wastewater and seawater appeared to be adequate and satisfied the standard and recommended limits in vigor in all these Mediterranean countries. These results showed that the wastewater treatment process used in Italy allowed a higher reduction in most tested parameters (COD: 80.44%, BOD5: 58.9%, OM: 70.74%, TOC: 78.4%). The bacteriological quality of these waters and their diversification was assessed by the denaturing gradient gel electrophoresis (DGGE) technique. Firmicutes, Gamma-Proteobacteria, and Beta-Proteobacteria in 42% of the DGGE DNA bands, and predominately some lineages affiliated to cultivable and non-cultivable clones of Bacteroides (58%), were found. The examination of antibiotic susceptibility at the level of 18 strains isolated from various arbitrary water samples showed that most of these isolates exhibited resistance to at least one antibiotic family, and 11 isolates exhibited 100% resistance against aztreonam. This result confirms the large antibiotic-resistance spreading and circulation registered these last decades in the natural environment, conditioned by the extensive and non-controlled use of antibiotics for human therapy and animal feeding.

МЕТОДИКА БАГАТОФАКТОРНОСТІ ПОРІВНЯННЯ ПОКАЗНИКІВ ЯКОСТІ РАДІОПОГЛИНАЮЧИХ ФЕРИТОВИХ КОМПОЗИТІВ

The article proposes a graph-analytical method of comparing four arbitrary alternative samples of composite radio-absorbing ferrite coating (CRFС) on sixteen time indicators of their quality, reflecting different aspects of consumer utility of such samples. The main (integrated) quality indicators of composite radio-absorbing ferrite coatings are determined such as: efficiency (level of usefulness in practical application); operational properties; manufacturability of production and application; mechanical strength. The multi-criteria choice task is reduced to single-criteria. Then the implementation of direct comparison of samples CRFС. Specific examples of application of the method of comparing a variant of the composite radio-absorbing ferrite coating OD-RAM of own development, with three other existing in practice radio-absorbing coatings are given. The application of generalized quality indicators of manufactured composites, which are determined by graphs of multiple factors, is proposed. This allows you to find out the problem factors. It is proved that the developed composite radio-absorbing ferrite coatings OD-RAM have a generalized quality index that exceeds the known foreign samples by (30 - 40)%. Further development of the methodology is proposed, which can be implemented for comparative evaluation of options for protection of armaments and military equipment, for comparison of the same type of protective samples of CRFС, in order to select an acceptable set of alternatives. The article contains new data on the practical application of multifactor comparison of manufactured composites on some new indicators of the quality of the composite being developed, according to its electrophysical and electromagnetic parameters. Keywords: radio-absorbing ferrite materials, comparison of coating options, basic quality indicators of composite radio-absorbing coatings, comparative evaluations of options for protection of armaments and military equipment, comparison of the same type of samples.

Low-temperature internal quantum efficiency of GaInN/GaN quantum wells under steady-state conditions

We compare the low-temperature photoluminescence (PL) intensities of a range of GaInN/GaN quantum well (QW) structures under identical excitation conditions, mounting the samples side by side. Normalizing the measured intensity to the absorbed power density in the QWs, we find that low-temperature PL efficiencies of several samples, which show close to 100% internal quantum efficiency (IQE) in time-resolved PL, saturate at nearly an identical value. Of course, this is strong indicative of being 100% IQE at low temperature for those efficient samples. Using the low-temperature PL efficiency as a ‘Reference’, on the other hand, we observe not only the effects of temperature-independent non-radiative losses on the low-temperature IQE, but also are able to determine the IQE of arbitrary samples on an absolute scale. Furthermore, we prove the experimental results by comparing the low-temperature efficiencies of a sample with an initial 100% IQE after intentionally introducing structural defects with argon-implantation.

Use of Racial and Ethnic Categories in Medical Testing and Diagnosis: Primum Non Nocere.

Use of race and ethnicity is common in medical tests and procedures, even though these categories are defined by sociological, historical, and political processes, and vary considerably in their definitions over time and place. Because all societies organize themselves around these constructs in some way, they are undeniable facets of the human experience, with myriad health consequences. In the biomedical literature, they are also commonly interpreted as representing biological heterogeneity that is relevant for health and disease. We review the use of race and ethnicity in medical practice, especially in the USA, and provide 2 specific examples to represent a large number of similar instances. We then critique these uses along a number of different dimensions, including limitations in measurement, within- versus between-group variance, and implications for informativeness of risk markers for individuals, generalization from arbitrary or nonrepresentative samples, perpetuation of myths and stereotypes, instability in time and place, crowding out of more relevant risk markers, stigmatization, and the tainting of medicine with the history of oppression. We conclude with recommendations to improve practice that are technical, ethical, and pragmatic. Medicine has evolved from a mystical healing art to a mature science of human health through a rigorous process of quantification, experimentation, and evaluation. Folkloric traditions, such as race- and ethnic-specific medicine will fade from use as we become increasingly critical of outdated and irrational clinical practices and replace these with personalized, evidenced-based tests, algorithms, and procedures that privilege patients' individual humanity over obsolete and misleading labels.

Development of a versatile, high-temperature, high-throughput ion irradiation system

Ion irradiation has long been used as a surrogate for neutron irradiation experiments which are comparatively slower, more expensive, and often can leave materials activated and difficult to handle post-irradiation. However, with the growing use of combinatorial synthesis methods and high-throughput, automated characterization techniques, ion irradiation facilities will need to upgrade their capabilities to be able to accommodate these new material platforms. To address this challenge, a high-throughput ion irradiation system has been developed at the University of Wisconsin-Madison Ion Beam Laboratory (IBL). The system is capable of accommodating large sample arrays of arbitrary geometries and heat samples individually using an infrared laser to enable high-temperature, high-throughput ion irradiation while mitigating the risk of annealing the irradiation damage in nearby samples. To control stage motion, laser power, and beam current measurement, among other tasks, the program Chronos was developed which enables automated high-throughput irradiation experiments.

Design and validation of fiber optic localized surface plasmon resonance sensor for thyroglobulin immunoassay with high sensitivity and rapid detection

A simple optical fiber sensor based on localized surface plasmon resonance was constructed for direct and rapid measurement of thyroglobulin (Tg). Specific tests for Tg in patients that have undergone thyroidectomy are limited because of insufficient sensitivity, complicated procedures, and in some cases, a long time to yield a result. A sensitive, fast, and simple method is necessary to relieve the psychological and physical burden of the patient. Various concentrations of Tg were measured in a microfluidic channel using an optical fiber sensor with gold nanoparticles. The sensor chip has a detection limit of 93.11 fg/mL with no specificity for other antigens. The potential applicability of the Tg sensing system was evaluated using arbitrary samples containing specific concentrations of Tg. Finally, the sensor can be employed to detect Tg in the patient’s serum, with a good correlation when compared with the commercial kit.

An Off-Policy Trust Region Policy Optimization Method With Monotonic Improvement Guarantee for Deep Reinforcement Learning.

In deep reinforcement learning, off-policy data help reduce on-policy interaction with the environment, and the trust region policy optimization (TRPO) method is efficient to stabilize the policy optimization procedure. In this article, we propose an off-policy TRPO method, off-policy TRPO, which exploits both on- and off-policy data and guarantees the monotonic improvement of policies. A surrogate objective function is developed to use both on- and off-policy data and keep the monotonic improvement of policies. We then optimize this surrogate objective function by approximately solving a constrained optimization problem under arbitrary parameterization and finite samples. We conduct experiments on representative continuous control tasks from OpenAI Gym and MuJoCo. The results show that the proposed off-policy TRPO achieves better performance in the majority of continuous control tasks compared with other trust region policy-based methods using off-policy data.