Noise Spikes Research Articles

Novel accelerated Stochastic Progressive Photon Mapping rendering with neural network

Recently, deep learning-based approaches have led to dramatic improvements for Monte Carlo rendering at the low sampling rate. Most of these approaches are aimed at path tracing. However, they are not suitable for photon mapping. In this paper, we develop a novel accelerate stochastic progressive photon mapping approaches with neural network. First, our framework utilizes the particle-based rendering and focuses on photon density estimation. We train a neural network to predict a kernel function to aggregate photon contributions at shading point. Then we construct a estimation images with the prediction network. During experiments, we could find that there are spike pixels and noises in estimation images sometimes. So we present the improved denoising network to post-process the estimation images. Finally, we can obtain the high-quality reconstructions of complex global illumination effects like caustics with an order of magnitude fewer photons compared with previous photon mapping methods. Besides, our denoising network can reduce most multi-scale noises on both low-frequency and high-frequency areas while preserving more illumination details, especially caustics, compared with other state-of-the-art learning-based denoising methods.

Spoofed replay attack detection by Multidimensional Fourier transform on facial micro-expression regions

Facial replay attacks have been a topic of interest in recent past due to the vulnerability of intrusive nature in biometric security systems. In order to build a robust biometric system many safeguard approaches have already been developed by the researchers to nullify spoofing activities like print and replay attacks. This paper proposes a comprehensive study on the application of Multidimensional Fourier transform to combat replay attacks. Since the higher frequency in Multidimensional Fourier transform contains the major feature variations, liveness of a face is mostly reflected in the high frequency spectrum. The spontaneous facial expressions like micro-expression(μE) carries the detailed inner facial variations. In this novel approach a modified high frequency descriptor is used for proper discrimination between a live and fake facial video streams. The descriptor in particular works efficiently for a change in facial μE. Inclusion of noise along with the feature variation is trivial in higher frequency spectrum. The method, therefore, during the pre-processing phase not only extracts the video frames with major μE changes but also filters out frames carrying any abrupt expression change (macro expression) or spike noise. The selected frame sequence are thereafter fed into the multi dimensional Fourier plane in order to detect the liveness. The experiment is performed on the self created dataset and also being tested on standard play back attack dataset. The result obtained by the proposed anti spoofing approach is satisfactory and verified to be statistically significant.

Event Matching Classification Method for Non-Intrusive Load Monitoring

Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters.

Measuring Phase-Amplitude Coupling Based on the Jensen-Shannon Divergence and Correlation Matrix.

Phase-amplitude coupling (PAC) measures the relationship between the phase of low-frequency oscillations (LFO) and the amplitude of high-frequency oscillations (HFO). It plays an important functional role in neural information processing and cognition. Thus, we propose a novel method based on the Jensen-Shannon (JS) divergence and correlation matrix. The method takes the amplitude distributions of the HFO located in the corresponding phase bins of the LFO as multichannel inputs to construct a correlation matrix, where the elements are calculated based on the JS divergence between pairs of amplitude distributions. Then, the omega complexity extracted from the correlation matrix is used to estimate the PAC strength. The simulation results demonstrate that the proposed method can effectively reflect the PAC strength and slightly vary with the data length. Moreover, it outperforms five frequently used algorithms in the performance against additive white Gaussian noise and spike noise and the ability of detecting PAC in wide frequency ranges. To validate our proposed method with real data, it was applied to analyze the local field potential recorded from the dorsomedial striatum in a male Sprague-Dawley rat. It can replicate previous results obtained with other PAC metrics. In conclusion, these results suggest that our proposed method is a powerful tool for measuring the PAC between neural oscillations.

Processing of noise contaminated magnetotelluric data using digital filters based on MATLAB

Magnetotelluric (MT) method is known as an effective geophysical method for imaging the condition of the subsurface resistivity distribution, especially in the geothermal system. However, this method is very sensitive to noise. The noise can occur due to mechanical activity such as vibration and electrical activity generated by powerline electric current near the point of measurement. To eliminate this noise is used digital filter consisting of despike filter and notch filter. Despike filter is used to eliminate irregular spike noise, while the notch filter is used to remove regular powerline noise. Furthermore, D+ can be applied to enhance the impedance data trend. Both of filters and D+ feature are built and then arranged into MTFilter program. The result of data processing using MTFilter program shows the improvement of MT data quality. Moreover, the inversion processing results of MT field data shows the resistivity distribution of structure which is more reliable with the characteristics of the geothermal system.

Dealing with noise in crowdsourced GPS human trajectory logging data

SummaryAs a crowdsourcing map platform, OpenStreetMap (OSM) relies on public contributions to enhance its dataset where the contributors can create, modify or remove features from the maps or share their trajectory trips in the repository. The majority of the data provided in a crowdsourcing platform are manually created and reviewed to suit real‐world conditions, hence human perception is the key indicator to consider the correctness of the data. One of the data that is provided by crowdsourcing platform is public trajectory. Public trajectory data contains details of historical trips obtained from contributors' GPS logger devices that are embedded in mobile devices, wearable devices, satnavs, or vehicle GPS trackers to record the user's trajectory path. While public trajectory data can be used as an alternate data source for human movement analysis, this crowdsourced dataset is also prone to noise and inaccuracy which makes the preprocessing step an important phase prior of any processing step. In this article, we discuss the characteristics and the most common noise from crowdsourcing GPS trajectories and utilize a non‐map‐matching approach convex hull‐based reduction method to minimize spike noise, followed by granularity reduction to reduce the number of trajectory points while maintaining the nature of the trajectories.

Fundamental elements of constant volt/hertz induction motor drives based on dSPACE DS1104 controller

An induction motor (IM) has been the workhorse of the industry for decades. It is due to its robustness, simple construction, requiring less maintenance and cheap. One of the most widely adopted IM drive control schemes for industrial applications is the open-loop constant V/Hz. In this paper, the important elements of an open-loop constant V/Hz drives are presented. These include the fundamental concept of a constant V/Hz scheme, the voltage source inverter (VSI) and its modulation schemes. Techniques that are commonly used to solve problems of low-speed operation and rotor speed regulation are briefly described. Simulations using MATLAB/Simulink package are used to help in illustrating these fundamental concepts. Finally, simple laboratory-scale experiments are conducted to implement the constant V/Hz control scheme on a ¼ hp induction motor. The constant V/Hz control is implemented using the DS1104 controller board with the C codes automatically generated from the Simulink model. A closer look at the current waveform when fref=25 Hz and 12 Hz and its respective Fourier analysis are presented. The results show that the high-frequency harmonic contents around the switching frequency can be observed for both cases, and the voltage waveform presents more spike noises in constrast to current waveform.

Blind Source Separation of Retinal Pulsatile Patterns in Optic Nerve Head Video-Recordings.

Dynamic optical imaging of retinal hemodynamics is a rapidly evolving technique in vision and eye-disease research. Video-recording, which may be readily accessible and affordable, captures several distinct functional phenomena such as the spontaneous venous pulsations (SVP) of central vein or local arterial blood supply etc. These phenomena display specific dynamic patterns that have been detected using manual or semi-automated methods. We propose a pioneering concept in retina video-imaging using blind source separation (BSS) serving as an automated localizer of distinct areas with temporally synchronized hemodynamics. The feasibility of BSS techniques (such as spatial principal component analysis and spatial independent component analysis) and K-means based post-processing method were successfully tested on the monocular and binocular video-ophthalmoscopic (VO) recordings of optic nerve head (ONH) in healthy subjects. BSSs automatically detected three spatially distinct reproducible areas, i.e. SVP, optic cup pulsations (OCP) that included areas of larger vessels in the nasal part of ONH, and "other" pulsations (OP). The K-means post-processing reduced a spike noise from the patterns' dynamics while high linear dependence between the non-filtered and post-processed signals was preserved. Although the dynamics of all patterns were heart rate related, the morphology analysis demonstrated significant phase shifts between SVP and OCP, and between SVP and OP. In addition, we detected low frequency oscillations that may represent respiratory-induced effects in time-courses of the VO recordings.

Passive Nonlinear Dendritic Interactions as a Computational Resource in Spiking Neural Networks.

Nonlinear interactions in the dendritic tree play a key role in neural computation. Nevertheless, modeling frameworks aimed at the construction of large-scale, functional spiking neural networks, such as the Neural Engineering Framework, tend to assume a linear superposition of postsynaptic currents. In this letter, we present a series of extensions to the Neural Engineering Framework that facilitate the construction of networks incorporating Dale's principle and nonlinear conductance-based synapses. We apply these extensions to a two-compartment LIF neuron that can be seen as a simple model of passive dendritic computation. We show that it is possible to incorporate neuron models with input-dependent nonlinearities into the Neural Engineering Framework without compromising high-level function and that nonlinear postsynaptic currents can be systematically exploited to compute a wide variety of multivariate, band-limited functions, including the Euclidean norm, controlled shunting, and nonnegative multiplication. By avoiding an additional source of spike noise, the function approximation accuracy of a single layer of two-compartment LIF neurons is on a par with or even surpasses that of two-layer spiking neural networks up to a certain target function bandwidth.

Detecting Ground Deformation in the Built Environment Using Sparse Satellite InSAR Data With a Convolutional Neural Network

The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of nonexpert stakeholders a challenge. Here, we explore the applicability of deep learning approaches by adapting a pretrained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. For our proof-of-concept, we focus on the U.K. where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides, and tunneling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exist to construct a balanced training data set, and the deformation signals are slower and more localized than in previous applications. We propose three enhancement methods to tackle these problems: 1) spatial interpolation with modified matrix completion; 2) a synthetic training data set based on the characteristics of the real U.K. velocity map; and 3) enhanced overwrapping techniques. Using velocity maps spanning 2015-2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides, and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.

A Family of Spread-Spectrum Modulation Schemes Based on Distribution Characteristics to Reduce Conducted EMI for Power Electronics Converters

Spread-spectrum modulation strategy is recognized as an effective solution to reduce conducted electromagnetic interference (EMI). This article proposes a family of spread-spectrum modulation strategies that can effectively reduce the conducted EMI for power electronics converters. The introduced modulation strategies eliminate the noise spikes in conducted EMI spectrum, reducing the harsh requirements for designing EMI filters. The proposed spread-spectrum methods are based on the distribution characteristic of switching frequency, which determine the waveform function of switching frequency. In addition, the design principles take reduced switching losses and fixed variation range of switching frequency into consideration. The differential equation that makes the switching frequency subject to arbitrary statistical distribution is derived in detail together with the corresponding solutions. Three typical statistical distributions represented by uniform distribution, exponential distribution and normal distribution are performed. Relevant experiments have verified that the proposed spread-spectrum modulation schemes can reduce the conducted EMI about 20 dB. Finally, several other typical spread-spectrum modulation schemes such as random pulsewidth modulation (PWM), periodic PWM, and programmed PWM are implemented in converters for comprehensive comparison to evaluate the proposed spread-spectrum modulation schemes.

Blind deconvolution for spike inference from fluorescence recordings

The parallel developments of genetically-encoded calcium indicators and fast fluorescence imaging techniques allows one to simultaneously record neural activity of extended neuronal populations in vivo. To fully harness the potential of functional imaging, one needs to infer the sequence of action potentials from fluorescence traces. Here we build on recently proposed computational approaches to develop a blind sparse deconvolution (BSD) algorithm based on a generative model for inferring spike trains from fluorescence traces. BSD features, (1) automatic (fully unsupervised) estimation of the hyperparameters, such as spike amplitude, noise level and rise and decay time constants, (2) a novel analytical estimate of the sparsity prior, which yields enhanced robustness and computational speed with respect to existing methods, (3) automatic thresholding for binarizing spikes that maximizes the precision-recall performance, (4) super-resolution capabilities increasing the temporal resolution beyond the fluorescence signal acquisition rate. BSD also uniquely provides theoretically-grounded estimates of the expected performance of the spike reconstruction in terms of precision-recall and temporal accuracy for each recording. The performance of the algorithm is established using synthetic data and through the SpikeFinder challenge, a community-based initiative for spike-rate inference benchmarking based on a collection of joint electrophysiological and fluorescence recordings. Our method outperforms classical sparse deconvolution algorithms in terms of robustness, speed and/or accuracy and performs competitively in the SpikeFinder challenge. This algorithm is modular, easy-to-use and made freely available. Its novel features can thus be incorporated in a straightforward way into existing calcium imaging packages.

IONOSPHERIC DISTURBANCES OVER EASTERN SIBERIA DURING APRIL 12–15, 2016 GEOMAGNETIC STORMS

We present the results of the complex study of ionospheric parameter variations during two geomagnetic storms, which occurred on April 12–15, 2016. The study is based on data from a set of radiophysical and optical instruments. Both the storms with no sudden commencement were generated by high-speed streams from a coronal hole. Despite the minor intensity of the storms (Dst ≥ –55 and –59 nT), we have revealed a distinct ionospheric response to these disturbances. A negative response of electron density and F2-layer critical frequency was observed during the main phase of both the storms. The amplitude of the negative response was higher for the second storm. The period of negative electron density deviations was accompanied by an increase in the peak height, as well as by the downward plasma drift in the evening and night hours, which is not typical of quiet conditions. We have also recorded sharp peaks in the AATR (Along Arc TEC Rate) index and in total electron content noise spikes on average 2–2.5 times. This indicates an intensification of small-scale ionospheric disturbances caused by disturbed geomagnetic conditions and high substorm activity.

Возмущения ионосферы над Восточной Сибирью во время геомагнитных бурь 12–15 апреля 2016 г.

We present the results of the complex study of ionospheric parameter variations during two geomagnetic storms, which occurred on April 12–15, 2016. The study is based on data from a set of radiophysical and optical instruments. Both the storms with no sudden commencement were generated by high-speed streams from a coronal hole. Despite the minor intensity of the storms (Dst ≥ –55 and –59 nT), we have revealed a distinct ionospheric response to these disturbances. A negative response of electron density and F2-layer critical frequency was observed during the main phase of both the storms. The amplitude of the negative response was higher for the second storm. The period of negative electron density deviations was accompanied by an increase in the peak height, as well as by the downward plasma drift in the evening and night hours, which is not typical of quiet conditions. We have also recorded sharp peaks in the AATR (Along Arc TEC Rate) index and in total electron content noise spikes on average 2–2.5 times. This indicates an intensification of small-scale ionospheric disturbances caused by disturbed geomagnetic conditions and high substorm activity.

Digital pulse analyzer for simultaneous measurement of pulse height, pulse width, and interval time on an optical particle counter

This study examined the development of a digital pulse analyzer (DPA) used for obtaining the pulse height distribution (PHD), pulse width distribution (PWD), and interval time distribution (ITD) simultaneously by processing output signals from an optical particle counter (OPC). The characterization was performed by testing the response of the DPA as a function of its input frequency and pulse height using dummy signals from a signal generator (model DG1022, Rigol Technologies Inc., People's Republic of China) and actual OPC signals (Model KC-03, Rion Co. Ltd, Japan). Polystyrene latex particles of various sizes yielded by a scanning mobility particle sizer were used during the calibration of the DPA. It was found that the geometric mean of the PHDs was in accordance with Mie theory and could be used for converting pulse height into data which represents the size of particles. Furthermore, by using the PWD and ITD, we could estimate the viewing volume and dead time of the OPC. The DPA could successfully discriminate particles from baseline noise or voltage spikes by applying a novel algorithm to detect pulses based on the pulse area (integral of the voltage–time curve). The results showed that the ranges of particle diameter, viewing volume, and dead time measured by the OPC-DPA system were 0.2–1 µm, (0.78 ± 0.09) mm3, and (45.9 ± 12.6) µs, respectively. The new algorithm can be applied to pulse height analyzers in general, in the measurement of aerosol particles or scintillator-based particle detectors.

Modeling and Optimization of Impedance Balancing Technique for Common Mode Noise Attenuation in DC-DC Boost Converters

As an effective means of suppressing electromagnetic interference (EMI) noise, the impedance balancing technique has been adopted in the literature. By suppressing the noise source, this technique can theoretically reduce the noise to zero. Nevertheless, its effect is limited in practice and also suffers from noise spikes. Therefore, this paper introduces an accurate frequency modeling method to investigate the attenuation degree of noise source and redesign the impedance selection accordingly in order to improve the noise reduction capability. Based on a conventional boost converter, the common mode (CM) noise model was built by identifying the noise source and propagation paths at first. Then the noise source model was extracted through capturing the switching voltage waveform in time domain and then calculating its Fourier series in frequency domain. After that, the conventional boost converter was modified with the known impedance balancing techniques. This balanced circuit was analyzed with the introduced modeling method, and the equivalent noise source was precisely estimated by combining the noise spectra and impedance information. Furthermore, two optimized schemes with redesigned impedances were proposed to deal with the resonance problem. A hardware circuit was designed and built to experimentally validate the proposed concepts. The experimental results demonstrate the feasibility and effectiveness of the proposed schemes.

A Pulse Signal Preprocessing Method Based on the Chauvenet Criterion.

Pulse signals are widely used to evaluate the status of the human cardiovascular, respiratory, and circulatory systems. In the process of being collected, the signals are usually interfered by some factors, such as the spike noise and the poor-sensor-contact noise, which have severely affected the accuracy of the subsequent detection models. In recent years, some methods have been applied to processing the above noisy signals, such as dynamic time warping, empirical mode decomposition, autocorrelation, and cross-correlation. Effective as they are, those methods are complex and difficult to implement. It is also found that the noisy signals are tightly related to gross errors. The Chauvenet criterion, one of the gross error discrimination criterions, is highly efficient and widely applicable for being without the complex calculations like decomposition and reconstruction. Therefore, in this study, based on the Chauvenet criterion, a new pulse signal preprocessing method is proposed, in which adaptive thresholds are designed, respectively, to discriminate the abnormal signals caused by spike noise and poor-sensor-contact noise. 81 hours of pulse signals (with a sleep apnea annotated every 30 seconds and 9,720 segments in total) from the MIT-BIH Polysomnographic Database are used in the study, including 35 minutes of poor-sensor-contact noises and 25 minutes of spike noises. The proposed method was used to preprocess the pulse signals, in which 9,684 segments out of a total of 9,720 were correctly discriminated, and the accuracy of the method reached 99.63%. To quantitatively evaluate the noise removal effect, a simulation experiment is conducted to compare the Jaccard Similarity Coefficient (JSC) calculated before and after the noise removal, respectively, and the results show that the preprocessed signal obtains higher JSC, closer to the reference signal, which indicates that the proposed method can effectively improve the signal quality. In order to evaluate the method, three back-propagation (BP) sleep apnea detection models with the same network structure and parameters were established, respectively. Through comparing the recognition rate and the prediction rate of the models, higher rates were obtained by using the proposed method. To prove the efficiency, the comparison experiment between the proposed Chauvenet-based method and a Romanovsky-based method was conducted, and the execution time of the proposed method is much shorter than that of the Romanovsky method. The results suggest that the superiority in execution time of the Chauvenet-based method becomes more significant as the date size increases.

A Uniform Search for Thermonuclear Burst Oscillations in the RXTE Legacy Data Set

Abstract We describe a blind uniform search for thermonuclear burst oscillations (TBOs) in the majority of Type I bursts observed by the Rossi X-ray Timing Explorer (RXTE) (2118 bursts from 57 neutron stars). We examined 2–2002 Hz power spectra from the Fourier transform in sliding 0.5–2 s windows, using fine-binned light curves in the 2–60 keV energy range. The significance of the oscillation candidates was assessed by simulations which took into account light-curve variations, dead time, and the sliding time windows. Some of our sources exhibited multi-frequency variability at ≲15 Hz that cannot be readily removed with light-curve modeling and may have an astrophysical (non-TBO) nature. Overall, we found that the number and strength of potential candidates depends strongly on the parameters of the search. We found candidates from all previously known RXTE TBO sources, with pulsations that had been detected at similar frequencies in multiple independent time windows, and discovered TBOs from SAX J1810.8−2658. We could not confirm most previously reported tentative TBO detections or identify any obvious candidates just below the detection threshold at similar frequencies in multiple bursts. We computed fractional amplitudes of all TBO candidates and placed upper limits on non-detections. Finally, for a few sources we noted a small excess of candidates with powers comparable to fainter TBOs, but appearing in single independent time windows at random frequencies. At least some of these candidates may be noise spikes that appear interesting due to selection effects. The potential presence of such candidates calls for extra caution if claiming single-window TBO detections.

An Online ESR Estimation Method for Output Capacitor of Boost Converter

Aluminum electrolytic capacitor (AEC) is one of the most age-affected components in power electronic converters and its reliability has been a major concern. As the growth of service life, the equivalent series resistance (ESR) of AEC increases due to the loss of electrolyte. Therefore, online ESR estimation is critical for condition monitoring of AEC. This paper proposes an online ESR estimation method for output capacitor of boost converter. By analyzing the output voltage ripple, an ESR calculation model is derived. Based on the model, only output voltage and inductor current need to be measured, which can be obtained directly from double closed-loops converter. To implement this proposed method, a detailed extraction scheme based on wavelet transform denoising is presented. Non-ideal factors (noise and voltage spikes) are analyzed and the superiority of the proposed parameter identification scheme is verified. The proposed estimation method can obtain ESR for the converter operating at different working conditions such as resistive loads and converter loads. Effect of temperature on ESR is incorporated for accurate determination of the health of capacitor. Simulation and experimental results are provided to verify the effectiveness of the method.

High-Density Electrical Recording and Impedance Imaging With a Multi-Modal CMOS Multi-Electrode Array Chip.

Multi-electrode arrays, both active or passive, emerged as ideal technologies to unveil intricated electrophysiological dynamics of cells and tissues. Active MEAs, designed using complementary metal oxide semiconductor technology (CMOS), stand over passive devices thanks to the possibility of achieving single-cell resolution, the reduced electrode size, the reduced crosstalk and the higher functionality and portability. Nevertheless, most of the reported CMOS MEA systems mainly rely on a single operational modality, which strongly hampers the applicability range of a single device. This can be a limiting factor considering that most biological and electrophysiological dynamics are often based on the synergy of multiple and complex mechanisms acting from different angles on the same phenomena. Here, we designed a CMOS MEA chip with 16,384 titanium nitride electrodes, 6 independent operational modalities and 1,024 parallel recording channels for neuro-electrophysiological studies. Sixteen independent active areas are patterned on the chip surface forming a 4 × 4 matrix, each one including 1,024 electrodes. Electrodes of four different sizes are present on the chip surface, ranging from 2.5 × 3.5 μm2 up to 11 × 11.0 μm2, with 15 μm pitch. In this paper, we exploited the impedance monitoring and voltage recording modalities not only to monitor the growth and development of primary rat hippocampal neurons, but also to assess their electrophysiological activity over time showing a mean spike amplitude of 144.8 ± 84.6 μV. Fixed frequency (1 kHz) and high sampling rate (30 kHz) impedance measurements were used to evaluate the cellular adhesion and growth on the chip surface. Thanks to the high-density configuration of the electrodes, as well as their dimension and pitch, the chip can appreciate the evolutions of the cell culture morphology starting from the moment of the seeding up to mature culture conditions. The measurements were confirmed by fluorescent staining. The effect of the different electrode sizes on the spike amplitudes and noise were also discussed. The multi-modality of the presented CMOS MEA allows for the simultaneous assessment of different physiological properties of the cultured neurons. Therefore, it can pave the way both to answer complex fundamental neuroscience questions as well as to aid the current drug-development paradigm.