Tunable Thresholds Research Articles

Photon discerner: adaptive quantum optical sensing near the shot noise limit

Photon statistics of an optical field can be used for quantum optical sensing in low light level scenarios free of bulky optical components. However, photon-number-resolving detection to unravel the photon statistics is challenging. Here, we propose a novel detection approach, that we call ‘photon discerning’, which uses adaptive photon thresholding for photon statistical estimation without recording exact photon numbers. Our photon discerner is motivated by the field of neural networks where tunable thresholds have proven efficient for information extraction in machine learning tasks. The photon discerner maximizes Fisher information per photon by iteratively choosing the optimal threshold in real-time to approach the shot noise limit. Our proposed scheme of adaptive photon thresholding leads to unique remote-sensing applications of quantum degree of linear polarization camera and quantum LiDAR. We investigate optimal thresholds and show that the optimal photon threshold can be counter-intuitive (not equal to 1) even for weak signals (mean photon number much less than 1), due to the photon bunching effect. We also put forth a superconducting nanowire realization of the photon discerner which can be experimentally implemented in the near-term. We show that the adaptivity of our photon discerner enables it to beat realistic photon-number-resolving detectors with limited photon-number resolution in certain applications. Our work suggests a new class of detectors for information-theory driven, compact, and learning-based quantum optical sensing.

Coherent random laser in Enteromorpha prolifera

This study presents a novel approach in coherent dye random laser (RL) generation by utilizing the special properties of Enteromorpha prolifera (EP) within a micro-glass tube configuration. The methodological simplicity of injecting a mixed solution of dye and EP into the tube at room temperature achieves a novel way of realizing the RL system, characterized by tunable random lasing thresholds contingent upon the concentration of EP. This tunability stems from the synergistic interplay between photon enhancement and fluorescent dye absorption facilitated by the unique nanoporous structure of EP, culminating in the attainment of a low lasing threshold of 21.7 μJ/mm2 at an optimized EP concentration of 2 mg/mL. With an excess EP concentration, the nanoporous structure could absorb large amounts of dye, resulting in dye fluorescence reabsorption, and causing the threshold to increase. A power Fourier transition was performed to calculate the effective optical cavity of the RL system, depicting the process of the coherent RL generation. Furthermore, a comprehensive investigation into the emission stability of the RL system revealed remarkable consistency in emission intensity over a prolonged excitation duration of 20 min, underscoring the potential for integration with optoelectronic devices. The L-shape mask imaging of the RL provides high-quality images with low spatial coherence, paving a useful application for speckle-free imaging. These results offer an affordable approach to achieving high-quality and low-threshold RLs using novel biomaterials.

Machine learning-based test smell detection

Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of these detectors is still limited and dependent on tunable thresholds. We design and experiment with a novel test smell detection approach based on machine learning to detect four test smells. First, we develop the largest dataset of manually-validated test smells to enable experimentation. Afterward, we train six machine learners and assess their capabilities in within- and cross-project scenarios. Finally, we compare the ML-based approach with state-of-the-art heuristic-based techniques. The key findings of the study report a negative result. The performance of the machine learning-based detector is significantly better than heuristic-based techniques, but none of the learners able to overcome an average F-Measure of 51%. We further elaborate and discuss the reasons behind this negative result through a qualitative investigation into the current issues and challenges that prevent the appropriate detection of test smells, which allowed us to catalog the next steps that the research community may pursue to improve test smell detection techniques.

Polysome propensity and tunable thresholds in coding sequence length enable differential mRNA stability.

The half-life of mRNAs, as well as their translation, increases in proportion to the optimal codons, indicating a tight coupling of codon-dependent differential translation and degradation. Little is known about the regulation of this coupling. We found that the mRNA stability gain in yeast depends on the mRNA coding sequence length. Below a critical length, codon optimality fails to affect the stability of mRNAs although they can be efficiently translated into short peptides and proteins. Above this threshold length, codon optimality-dependent differential mRNA stability emerges in a switch-like fashion, which coincides with a similar increase in the polysome propensity of the mRNAs. This threshold length can be tuned by the untranslated regions (UTR). Some of these UTRs can destabilize mRNAs without reducing translation, which plays a role in controlling the amplitude of the oscillatory expression of cell cycle genes. Our findings help understand the translation of short peptides from noncoding RNAs and the translation by localized monosomes in neurons.

Adaptive Refining-Aggregation-Separation Framework for Unsupervised Domain Adaptation Semantic Segmentation

Unsupervised domain adaptation has attracted widespread attention as a promising method to solve the labeling difficulties of semantic segmentation tasks. It trains a segmentation network for unlabeled real target images using easily available labeled virtual source images. To improve performance, clustering is used to obtain domain-invariant feature representations. However, most clustering-based methods indiscriminately cluster all features mapped by category from both domains, causing the centroid shift and affecting the generation of discriminative features. We propose a novel clustering-based method that uses an adaptive refining-aggregation-separation framework, which learns the discriminative features by designing different adaptive schemes for different domains and features. The clustering does not require any tunable thresholds. To estimate more accurate domain-invariant centroids, we design different ways to guide the adaptive refinement of different domain features. A critic is proposed to directly evaluate the confidence of target features to solve the absence of target labels. We introduce a domain-balanced aggregation loss and two adaptive separation losses for distance and similarity respectively, which can discriminate clustering features by combining the refinement strategy to improve segmentation performance. Experimental results on GTA5→Cityscapes and SYNTHIA→Cityscapes benchmarks show that our method outperforms existing state-of-the-art methods.

"Enzyme-Like" Spatially Fixed Polyhydroxyl Microenvironment-Activated Hydrochromic Molecular Switching for Naked Eye Detection of ppm Level Humidity.

The detection and monitoring of ultralow humidity (<100ppm) are critical in many important industries, such as high-tech manufacturing, scientific research, and aerospace. However, the development of ppm level humidity sensors with portability, low cost, and ease of regeneration remains a significant challenge. Herein, an innovative "enzyme-like" construction strategy is proposed to address this problem by employing suitable molecular-level humidity-sensitive units and chemically constructing a multilevel spatial synergistic sensitization microenvironment around it. The as-prepared ultralow humidity-sensitive paper (UHSP) achieved a naked eye recognition humidity of 0.01-100ppm. UHSP not only is simple to prepare, handy and low-cost, but can also be simply and efficiently regenerated as well as recycled many times by skillfully utilizing the "unconventional sublimation" and "lime slaked" of calcium oxide. The molecular reaction mechanisms involved in the humidity response and regeneration of UHSP have been demonstrated in detail. UHSP can provide a promising new method for ultralow humidity detection in the form of portable kits or sirens. The demonstrated "enzyme-like" construction strategy can bring unlimited ideas and implications to the design and development of sensors with tunable response thresholds, particularly high sensitivity.

Using machine learning to predict dimensions and qualify diverse part designs across multiple additive machines and materials

One of the most critical decisions in a factory is whether to accept or reject manufactured parts, where the acceptance criteria usually includes part geometry and the presence of defects. Additive manufacturing (AM) produces parts having geometric defects that depend upon the part size and shape as well as manufacturing process parameters. There is a need to develop methods that can effectively predict these defects and aid the part qualification process. This study addresses two problems: first, the prediction of part geometry; and second, the use of these predictions to qualify parts. To mimic an AM production scheme, we produced 405 parts across nine builds, using two machines, three polymer materials, and two different arrangements of part designs within a build. For each of three part designs, we identified and measured five critical features per part for a total of 2025 feature measurements. We developed a data model that describes design information and manufacturing process information using both continuous and categorical variables and used this data to train a support vector regression (SVR) machine learning (ML) model to predict part geometry. The models are trained on a random selection of the 405 parts and predict the geometry of unsampled parts with high accuracy. The SVR models predict feature geometry to within 53 µm, which is close to the manufacturer reported process repeatability and much smaller than the 180 µm standard deviation of the data. An exploration of the data model shows that categorical information about the part design significantly improves the model prediction compared to models that do not capture this information. In the second part of the study, we leverage the same data model to develop a part qualification tool and explore three strategies for accepting or rejecting parts based on ML predictions. The strategies are based on: SVR feature geometry predictions and tunable acceptance thresholds, direct feature classifications with a part acceptance threshold, and direct part classifications. In all cases, the only model inputs are the manufacturing parameters and feature descriptors. The SVR-based part qualification strategy achieves the highest accuracy at 81% and is more data efficient than the other approaches. The research demonstrates the utility of ML in predicting AM part geometry and accurately classifying parts, and provides opportunities to better understand geometry variability in production AM.

Tunable Duplex Semiquantitative Detection of Nucleic Acids with a Visual Lateral Flow Immunoassay Readout.

Quantitative nucleic acid amplification testing (NAAT) is a key enabling technology for infectious disease management, especially in instances where viral load informs therapeutic decisions. Inadequate access to quantitative NAATs remains a challenge to the successful deployment of antiretroviral therapy (ART) regimens for patients with chronic hepatitis B virus (CHB) in low resourced settings (LRS). Current field-deployable NAATs are generally qualitative (yes/no) rather than quantitative in nature, making them ill-suited for viral load monitoring programs for CHB patients. Here, we report the development of a proof-of-concept molecular diagnostic test, the semiquantitative ligation and amplification (SQLA) assay, which achieves semiquantitative detection of input target DNA at two independently tunable detection thresholds with a simple visual readout. The SQLA assay utilizes a duplex competitive thermophilic helicase-dependent amplification (tHDA) chemistry and can be performed in under 1 h.

Accurate Heuristic Terrain Prediction in Powered Lower-Limb Prostheses Using Onboard Sensors.

This study describes the development and offline validation of a heuristic algorithm for accurate prediction of ground terrain in a lower limb prosthesis. This method is based on inference of the ground terrain geometry using estimation of prosthetic limb kinematics during gait with a single integrated inertial measurement unit. We asked five subjects with below-knee amputations to traverse level ground, stairs, and ramps using a high-range-of-motion powered prosthesis while internal sensor data were remotely logged. We used these data to develop three terrain prediction algorithms. The first two employed state-of-the-art machine learning approaches, while the third was a directly tuned heuristic using thresholds on estimated prosthetic ankle joint translations and ground slope. We compared the performance of these algorithms using resubstitution error for the machine learning algorithms and overall error for the heuristic algorithm. Our optimal machine learning algorithm attained a resubstitution error of 3.4% using 45 features, while our heuristic method attained an overall prediction error of 2.8% using only 5 features derived from estimation of ground slope and horizontal and vertical ankle joint displacement. Compared with pattern recognition, the heuristic performed better on each individual subject, and across both level and non-level strides. These results demonstrate a method for heuristic prediction of ground terrain in a powered prosthesis. The method is more accurate, more interpretable, and less computationally expensive than machine learning methods considered state-of-the-art for intent recognition, and relies only on integrated prosthesis sensors. Finally, the method provides intuitively tunable thresholds to improve performance for specific walking conditions.

Reconfigurable all-optical nonlinear activation functions for neuromorphic photonics.

We experimentally demonstrate all-optical reconfigurable nonlinear activation functions in a cavity-loaded Mach-Zehnder interferometer device on a silicon photonics platform, via the free-carrier dispersion effect. Our device is programmable to generate various nonlinear activation functions, including sigmoid, radial-basis, clamped rectified linear unit, and softplus, with tunable thresholds. We simulate benchmark tasks such as XOR and MNIST handwritten digit classifications with experimentally measured activation functions and obtain accuracies of 100% and 94%, respectively. Our device can serve as nonlinear units in photonic neural networks, while its nonlinear transfer function can be flexibly programmed to optimize the performance of different neuromorphic tasks.

DUETT quantitatively identifies known and novel events in nascent RNA structural dynamics from chemical probing data.

MotivationRNA molecules can undergo complex structural dynamics, especially during transcription, which influence their biological functions. Recently developed high-throughput chemical probing experiments that study RNA cotranscriptional folding generate nucleotide-resolution ‘reactivities’ for each length of a growing nascent RNA that reflect structural dynamics. However, the manual annotation and qualitative interpretation of reactivity across these large datasets can be nuanced, laborious, and difficult for new practitioners. We developed a quantitative and systematic approach to automatically detect RNA folding events from these datasets to reduce human bias/error, standardize event discovery and generate hypotheses about RNA folding trajectories for further analysis and experimental validation.ResultsDetection of Unknown Events with Tunable Thresholds (DUETT) identifies RNA structural transitions in cotranscriptional RNA chemical probing datasets. DUETT employs a feedback control-inspired method and a linear regression approach and relies on interpretable and independently tunable parameter thresholds to match qualitative user expectations with quantitatively identified folding events. We validate the approach by identifying known RNA structural transitions within the cotranscriptional folding pathways of the Escherichia coli signal recognition particle RNA and the Bacillus cereus crcB fluoride riboswitch. We identify previously overlooked features of these datasets such as heightened reactivity patterns in the signal recognition particle RNA about 12 nt lengths before base-pair rearrangement. We then apply a sensitivity analysis to identify tradeoffs when choosing parameter thresholds. Finally, we show that DUETT is tunable across a wide range of contexts, enabling flexible application to study broad classes of RNA folding mechanisms.Availability and implementation https://github.com/BagheriLab/DUETT.Supplementary information Supplementary data are available at Bioinformatics online.

Threshold-Type Binary Memristor Emulator Circuit

A floating memristor emulator composed of off-the-shelf devices is presented to mimic the dynamics of threshold-type binary memristor. The diodes combined with proportional amplifier circuits are used to model the threshold sensitive behavior. By utilizing a bistable circuit, non-volatility and binary characteristic of the memristor emulator are implemented. The impacts of circuit parameters including resistance and capacitance on the memristor properties are explicated. For instance, the ratio of high memristance to low memristance can be changed by altering the specified resistances. Simulation results validate the feasibility of the proposed memristor emulator circuit. Tunable thresholds, a great memductance ratio, binary characteristic, and non-volatility of the memristor emulator are demonstrated experimentally under the inputs of sinusoidal signal and a series of pulses. In addition, state switching rule of the memristor are tested experimentally through applying pulse signals with different frequency or amplitude. Finally, memristor ratioed logic (MRL) circuits containing OR, AND, and XOR logic gates are used to verify the application of the proposed emulator. The emulator can be operated with floating connection and applied in various memristive circuits. Hence, it can offer a platform for the research of the memristor-based applications.

An optimized photoelectron track reconstruction method for photoelectric X-ray polarimeters

We present a data processing algorithm for angular reconstruction and event selection applied to 2-D photoelectron track images from X-ray polarimeters. The method reconstructs the initial emission angle of a photoelectron from the initial portion of the track, which is obtained by continuously cutting a track until the image moments or number of pixels fall below tunable thresholds. In addition, event selection which rejects round tracks quantified with eccentricity and circularity is performed so that polarimetry sensitivity considering a trade-off between the modulation factor and signal acceptance is maximized. The modulation factors with applying track selection are 26.6±0.4, 46.1±0.4, 62.3±0.4, and 61.8±0.3% at 2.7, 4.5, 6.4, and 8.0 keV, respectively, using the same data previously analyzed by Iwakiri et al. (2016), where the corresponding numbers are 26.9±0.4, 43.4±0.4, 54.4±0.3, and 59.1±0.3%. The method improves polarimeter sensitivity by 5%–10% at the high energy end of the band previously presented (Iwakiri et al. 2016).

Excitability in the p53 network mediates robust signaling with tunable activation thresholds in single cells

Cellular signaling systems precisely transmit information in the presence of molecular noise while retaining flexibility to accommodate the needs of individual cells. To understand design principles underlying such versatile signaling, we analyzed the response of the tumor suppressor p53 to varying levels of DNA damage in hundreds of individual cells and observed a switch between distinct signaling modes characterized by isolated pulses and sustained oscillations of p53 accumulation. Guided by dynamic systems theory we show that this requires an excitable network structure comprising positive feedback and provide experimental evidence for its molecular identity. The resulting data-driven model reproduced all features of measured signaling responses and is sufficient to explain their heterogeneity in individual cells. We present evidence that heterogeneity in the levels of the feedback regulator Wip1 sets cell-specific thresholds for p53 activation, providing means to modulate its response through interacting signaling pathways. Our results demonstrate how excitable signaling networks can provide high specificity, sensitivity and robustness while retaining unique possibilities to adjust their function to the physiology of individual cells.

A Generalized Logistic Link Function for Cumulative Link Models in Ordinal Regression

Ordinal regression is a kind of regression analysis used for predicting an ordered response variable. In these problems, the patterns are labelled by a set of ranks with an ordering among the different categories. The most common type of ordinal regression model is the cumulative link model. The cumulative link model relates an unobserved continuous latent variable with a monotone link function. Logit and probit functions are examples of link functions used in cumulative link models. In this paper, a novel generalized link function based on a generalization of the logistic distribution is proposed. The generalized link function proposed is able to reproduce other different link functions by changing two real parameters: \(\alpha \) and \(\lambda \). The generalized link function has been included in a cumulative link model where the latent function is determined by a standard neural network in order to test the performance of the proposal. For this model, a reformulation of the tunable thresholds and distribution parameters was applied to convert the constrained optimization problem into an unconstrained optimization problem. Experimental results demonstrate that our proposed approach can achieve competitive generalization performance.

Increasing color saturation by optimizing light spectra constrained on color rendering properties

Enhancing the color saturation of objects via illumination with specially designed light spectra is promising in many commercial and scientific applications. Existing literature has focused on studying the colors that can be rendered by white light with various correlated color temperatures, and by the mixed light from various monochromatic light-emitting diodes. However, very little literature has been devoted to methods that can actively configure the light spectrum to enhance the color saturation of an arbitrary object. This paper proposes an optimization-based method to extend the gamut that can be achieved by a given polychromatic lamp toward a chosen direction, i.e., to increase the saturation of a specific color. Moreover, to trade-off the increased saturation of a color sample with the reduced colorfulness of other samples, constraints with tunable thresholds are imposed to the optimization problem to bound the variation of a contrast color sample. In addition, the effect of uncontrollable ambient light can be taken into account by mild modifications of the optimization problem. Simulation results show that the optimized light spectrum can effectively enhance the saturation of any specific color, while maintaining the other rendered colors as intact as possible. Visual experiments have also been conducted with 22 human subjects, whose responses agree with the simulation results.

Generalized boundary condition memristor model

SUMMARYA number of resistive switching memories exhibit activation‐based dynamical behavior, which makes them suitable for neuromorphic and programmable analog filtering applications. Because the Boundary Condition Memristor (BCM) model accounts for tunable activation thresholds only at the on and off boundary states, it is not quantitatively accurate in the description of these kinds of memristors and in the investigation of their circuit applications. This paper introduces the Generalized Boundary Condition Memristor (GBCM) model, preserving the features of the BCM model while allowing, further, an ad‐hoc tuning of activation‐based dynamics, which enables an appropriate modulation of the conditions under which memristors may operate as storage elements or data processors. A simple circuit implementation of the novel model is presented, and time‐efficient simulations confirming the improvement in modeling accuracy over the BCM model are shown. As a proof‐of‐concept for the suitability of the GBCM model in the exploration of the full potential of memristors in neuromorphic circuits and programmable analog filters, this paper adopts it to model fundamental synaptic rules governing the mechanisms of learning in neural systems and to gain some insight into key issues in the design of a couple of filters. Copyright © 2015 John Wiley &amp; Sons, Ltd.

Next Generation Mechanophores for Unraveling the Role of Integrin Tension in Focal Adhesion Assembly

Mechanical stimuli profoundly alter cell fate, yet the mechanisms underlying mechanotransduction remain obscure due to a lack of methods for molecular force imaging. In this presentation, I will describe the development of second and third generation mechanophores (molecules that fluoresce upon experiencing force) for imaging molecular forces exerted by individual integrin cell surface receptors. Specifically, we developed a new class of molecular tension probes that function as a switch to generate a 20-30-fold increase in fluorescence upon experiencing a threshold piconewton force. The probes employ immobilized DNA-hairpins with tunable force response thresholds, ligands, and fluorescence reporters. Quantitative imaging reveals that integrin tension is highly dynamic and increases with an increasing integrin density during adhesion formation. Mixtures of fluorophore-encoded probes show integrin mechanical preference for cyclized-RGD over linear-RGD peptides. Multiplexed probes with variable guanine-cytosine content within their hairpins reveal integrin preference for the more stable probes at the leading tip of growing adhesions near the cell edge. DNA-based tension probes are among the most sensitive optical force reporters to date, overcoming the force and spatial-resolution limitations of traction force microscopy. The application of these sensors to image forces associated with a range of mechano-regulatory processes that occur at the lipid membrane of the cell, such as endocytosis, Notch receptor activation, and integrin adhesion receptors will be described as well.

DNA-based digital tension probes reveal integrin forces during early cell adhesion.

Mechanical stimuli profoundly alter cell fate, yet the mechanisms underlying mechanotransduction remain obscure due to a lack of methods for molecular force imaging. Here, to address this need, we develop a new class of molecular tension probes that function as a switch to generate a 20–30-fold increase in fluorescence upon experiencing a threshold piconewton force. The probes employ immobilized DNA-hairpins with tunable force response thresholds, ligands, and fluorescence reporters. Quantitative imaging reveals that integrin tension is highly dynamic and increases with an increasing integrin density during adhesion formation. Mixtures of fluorophore-encoded probes show integrin mechanical preference for cyclized-RGD over linear-RGD peptides. Multiplexed probes with variable guanine-cytosine content within their hairpins reveal integrin preference for the more stable probes at the leading tip of growing adhesions near the cell edge. DNA-based tension probes are among the most sensitive optical force reporters to date, overcoming the force and spatial-resolution limitations of traction force microscopy.

Negative correlation ensemble learning for ordinal regression.

In this paper, two neural network threshold ensemble models are proposed for ordinal regression problems. For the first ensemble method, the thresholds are fixed a priori and are not modified during training. The second one considers the thresholds of each member of the ensemble as free parameters, allowing their modification during the training process. This is achieved through a reformulation of these tunable thresholds, which avoids the constraints they must fulfill for the ordinal regression problem. During training, diversity exists in different projections generated by each member is taken into account for the parameter updating. This diversity is promoted in an explicit way using a diversity-encouraging error function, extending the well-known negative correlation learning framework to the area of ordinal regression, and inheriting many of its good properties. Experimental results demonstrate that the proposed algorithms can achieve competitive generalization performance when considering four ordinal regression metrics.