Temporal Drift Research Articles

Development and temporal validation of a clinical prediction model of transition to psychosis in individuals at ultra-high risk in the UHR 1000+ cohort.

The concept of ultra-high risk for psychosis (UHR) has been at the forefront of psychiatric research for several decades, with the ultimate goal of preventing the onset of psychotic disorder in high-risk individuals. Orygen (Melbourne, Australia) has led a range of observational and intervention studies in this clinical population. These datasets have now been integrated into the UHR 1000+ cohort, consisting of a sample of 1,245 UHR individuals with a follow-up period ranging from 1 to 16.7 years. This paper describes the cohort, presents a clinical prediction model of transition to psychosis in this cohort, and examines how predictive performance is affected by changes in UHR samples over time. We analyzed transition to psychosis using a Cox proportional hazards model. Clinical predictors for transition to psychosis were investigated in the entire cohort using multiple imputation and Rubin's rule. To assess performance drift over time, data from 1995-2016 were used for initial model fitting, and models were subsequently validated on data from 2017-2020. Over the follow-up period, 220 cases (17.7%) developed a psychotic disorder. Pooled hazard ratio (HR) estimates showed that the Comprehensive Assessment of At-Risk Mental States (CAARMS) Disorganized Speech subscale severity score (HR=1.12, 95% CI: 1.02-1.24, p=0.024), the CAARMS Unusual Thought Content subscale severity score (HR=1.13, 95% CI: 1.03-1.24, p=0.009), the Scale for the Assessment of Negative Symptoms (SANS) total score (HR=1.02, 95% CI: 1.00-1.03, p=0.022), the Social and Occupational Functioning Assessment Scale (SOFAS) score (HR=0.98, 95% CI: 0.97-1.00, p=0.036), and time between onset of symptoms and entry to UHR service (log transformed) (HR=1.10, 95% CI: 1.02-1.19, p=0.013) were predictive of transition to psychosis. UHR individuals who met the brief limited intermittent psychotic symptoms (BLIPS) criteria had a higher probability of transitioning to psychosis than those who met the attenuated psychotic symptoms (APS) criteria (HR=0.48, 95% CI: 0.32-0.73, p=0.001) and those who met the Trait risk criteria (a first-degree relative with a psychotic disorder or a schizotypal personality disorder plus a significant decrease in functioning during the previous year) (HR=0.43, 95% CI: 0.22-0.83, p=0.013). Models based on data from 1995-2016 displayed good calibration at initial model fitting, but showed a drift of 20.2-35.4% in calibration when validated on data from 2017-2020. Large-scale longitudinal data such as those from the UHR 1000+ cohort are required to develop accurate psychosis prediction models. It is critical to assess existing and future risk calculators for temporal drift, that may reduce their utility in clinical practice over time.

Study of Ion-to-Electron Transducing Layers for the Detection of Nitrate Ions Using FPSX(TDDAN)-Based Ion-Sensitive Electrodes.

The development of ISE-based sensors for the analysis of nitrates in liquid phase is described in this work. Focusing on the tetradodecylammonium nitrate (TDDAN) ion exchanger as well as on fluoropolysiloxane (FPSX) polymer-based layers, electrodeposited matrixes containing double-walled carbon nanotubes (DWCNTs), embedded in either polyethylenedioxythiophene (PEDOT) or polypyrrole (PPy) polymers, ensured improved ion-to-electron transducing layers for NO3- detection. Thus, FPSX-based pNO3-ElecCell microsensors exhibited good detection properties (sensitivity up to 55 mV/pX for NO3 values ranging from 1 to 5) and acceptable selectivity in the presence of the main interferent anions (Cl-, HCO3-, and SO42-). Focusing on the temporal drift bottleneck, mixed results were obtained. On the one hand, relatively stable measurements and low temporal drifts (~1.5 mV/day) were evidenced on several days. On the other hand, the pNO3 sensor properties were degraded in the long term, being finally characterized by high response times, low detection sensitivities, and important measurement instabilities. These phenomena were related to the formation of some thin water-based layers at the polymer-metal interface, as well as the physicochemical properties of the TDDAN ion exchanger in the FPSX matrix. However, the improvements obtained thanks to DWCNT-based ion-to-electron transducing layers pave the way for the long-term analysis of NO3- ions in real water-based solutions.

Plasmodium vivax genomic surveillance in the Peruvian Amazon with Pv AmpliSeq assay.

Plasmodium vivax is the most predominant malaria species in Latin America, constituting 71.5% of malaria cases in 2021. With several countries aiming for malaria elimination, it is crucial to prioritize effectiveness of national control programs by optimizing the utilization of available resources and strategically implementing necessary changes. To support this, there is a need for innovative approaches such as genomic surveillance tools that can investigate changes in transmission intensity, imported cases and sources of reintroduction, and can detect molecular markers associated with drug resistance. Here, we apply a modified highly-multiplexed deep sequencing assay: Pv AmpliSeq v2 Peru. The tool targets a newly developed 41-SNP Peru barcode for parasite population analysis within Peru, the 33-SNP vivaxGEN-geo panel for country-level classification, and 11 putative drug resistance genes. It was applied to 230 samples from the Peruvian Amazon (2007-2020), generating baseline surveillance data. We observed a heterogenous P. vivax population with high diversity and gene flow in peri-urban areas of Maynas province (Loreto region) with a temporal drift using all SNPs detected by the assay (nSNP = 2909). In comparison, in an indigenous isolated area, the parasite population was genetically differentiated (FST = 0.07-0.09) with moderate diversity and high relatedness between isolates in the community. In a remote border community, a clonal P. vivax cluster was identified, with distinct haplotypes in drug resistant genes and ama1, more similar to Brazilian isolates, likely representing an introduction of P. vivax from Brazil at that time. To test its applicability for Latin America, we evaluated the SNP Peru barcode in P. vivax genomes from the region and demonstrated the capacity to capture local population clustering at within-country level. Together this data shows that P. vivax transmission is heterogeneous in different settings within the Peruvian Amazon. Genetic analysis is a key component for regional malaria control, offering valuable insights that should be incorporated into routine surveillance.

Sustained behaviour: Encoding of cumulative experience in the anterior cingulate

Time is a ubiquitous dimension of behaviour. A new study demonstrates that low-dimensional temporal drift in rodent anterior cingulate ensembles encodes cumulative experience. These data provide fresh insight into how neurons encode extended periods of time to guide high-level behaviours.

Ab Initio Study of Novel Phase-Change Heterostructures.

Neuromorphic devices constitute a novel approach to computing that takes inspiration from the brain to unify the processing and storage units. Memories based on phase-change materials (PCMs) are potential candidates for such devices due to their non-volatility and excellent scalability, however their use is hindered by their conductance variability and temporal drift in resistance. Recently, it has been shown that the utilization of phase-change heterostructures consisting of nanolayers of the Sb2Te3 PCM interleaved with a transition-metal dichalcogenide, acting as a confinement material, strongly mitigates these problems. In this work, superlattice heterostructures made of TiTe2 and two prototypical PCMs, respectively GeTe and Ge2Sb2Te5 are considered. By performing ab initio molecular dynamics simulations, it is shown that it is possible to switch the PCMs without destroying the superlattice structure and without diffusion of the atoms of the PCM across the TiTe2 nanolayers. In particular, the model containing Ge2Sb2Te5 shows weak coupling between the two materials during the switching process, which, combined with the high stability of the amorphous state of Ge2Sb2Te5, makes it a very promising candidate for neuromorphic computingapplications.

A novel methodology to assess optical profilometer stability to discriminate surface roughness

A new method is proposed to investigate the measurement variation of instruments and their ability to discriminate two TA6V surfaces obtained by grinding with SiC grit papers (80 and 120). Three optical measurement technologies included into a single apparatus, namely Confocal Microscopy (CM), Focus Variation (FV) and Coherence Scanning Interferometry (CSI), were used to measure both ground surfaces according to a specific experimental protocol (MorphoMeca Monitoring) based on measurement iterations and repetitions. From the measurements, four indices were built from the Sa roughness parameter to assess the Quality (ratio of the topographical variability over the measurement noise), the Drift (related to surface fluctuations) and the Stability (estimation of the temporal drift using a second order autoregressive model) of the three measurement technologies (modes), as well as their Relevance (computed from an analysis of variance) to discriminate ground surfaces with close roughness. For these investigated surfaces, it was especially highlighted that the Quality Index is four times higher for the CSI mode than for the FV and CM modes. All the used measurement modes experienced small drifting (Drift Index) during the measurements. The CSI mode was slightly more sensitive to drift than the FV and CM modes. The Stability Index indicated that there was no memory effect between two acquisitions for each measurement mode, i.e., the temporal drift did not depend on the measurement mode. Moreover, the three measurement modes are able to discriminate the two ground surfaces with the same accuracy through the Sa parameter. However, the Sa values may vary depending on the measurement modes, and it was finally shown that the Sa parameter is minimized by 0.03 μm for the FV mode compared with the CSI mode, due to its smoothing effect.

A Method to Correct the Temporal Drift of Single-Photon Detectors Based on Asynchronous Quantum Ghost Imaging.

Single-photon detection and timing has attracted increasing interest in recent years due to their necessity in the field of quantum sensing and the advantages of single-quanta detection in the field of low-level light imaging. While simple bucket detectors are mature enough for commercial applications, more complex imaging detectors are still a field of research comprising mostly prototype-level detectors. A major problem in these detectors is the implementation of in-pixel timing circuitry, especially for two-dimensional imagers. One of the most promising approaches is the use of voltage-controlled ring resonators in every pixel. Each of these runs independently based on a voltage supplied by a global reference. However, this yields the problem that the supply voltage can change across the chip which, in turn, changes the period of the ring resonator. Due to additional parasitic effects, this problem can worsen with increasing measurement time, leading to drift in the timing information. We present here a method to identify and correct such temporal drifts in single-photon detectors based on asynchronous quantum ghost imaging. We also show the effect of this correction on recent quantum ghost imaging (QGI) measurement from our group.

Studying animal locomotion with multiple data loggers: quantifying time drift between tags

Temporal accuracy is a fundamental characteristic of logging technology and is needed to correlate data streams. Single biologgers sensing animal movement (accelerometers, gyroscope, magnetometers, collectively inertial measurement unit; IMU) have been extensively used to study the ecology of animals. To better capture whole body movement and increase the accuracy of behavior classification, there is a need to deploy multiple loggers on a single individual to capture the movement of multiple body parts. Yet due to temporal drift, accurately aligning multiple IMU datasets can be problematic, especially as deployment duration increases. In this paper we quantify temporal drift and errors in commercially available IMU data loggers using a combination of robotic and animal borne experiments. The variance in drift rate within a tag is over an order of magnitude lower (σ = 0.001 s h−1) than the variance between tags (σ = 0.015 s·h−1), showing that recording frequency is a characteristic of each tag and not a random variable. Furthermore, we observed a large offset (0.54 ± 0.016 s·h−1) between two groups of tags that had differing recording frequencies, and we observed three instances of instantaneous temporal jumps within datasets introducing errors into the data streams. Finally, we show that relative drift rates can be estimated even when deployed on animals displaying various behaviors without the tags needing to be simultaneously moved. For the tags used in this study, drift rates can vary significantly between tags, are repeatable, and can be accurately measured in the field. The temporal alignment of multiple tag datasets allows researchers to deploy multiple tags on an individual animal which will greatly increase our knowledge of movement kinematics and expand the range of movement characteristics that can be used for behavioral classification.

A Study of the Drift Phenomena of Gate-Functionalized Biosensors and Dual-Gate-Functionalized Biosensors in Human Serum.

In this paper, we study the drift behavior of organic electrochemical transistor (OECT) biosensors in a phosphate-buffered saline (PBS) buffer solution and human serum. Theoretical and experimental methods are illustrated in this paper to understand the origin of the drift phenomenon and the mechanism of ion diffusion in the sensing layer. The drift phenomenon is explained using a first-order kinetic model of ion adsorption into the gate material and shows very good agreement with experimental data on drift in OECTs. We show that the temporal current drift can be largely mitigated using a dual-gate OECT architecture and that dual-gate-based biosensors can increase the accuracy and sensitivity of immuno-biosensors compared to a standard single-gate design. Specific binding can be detected at a relatively low limit of detection, even in human serum.

Generalize for Future: Slow and Fast Trajectory Learning for CTR Prediction

Deep neural networks (DNNs) have achieved significant advancements in click-through rate (CTR) prediction by demonstrating strong generalization on training data. However, in real-world scenarios, the assumption of independent and identically distributed (i.i.d.) conditions, which is fundamental to this problem, is often violated due to temporal distribution shifts. This violation can lead to suboptimal model performance when optimizing empirical risk without access to future data, resulting in overfitting on the training data and convergence to a single sharp minimum. To address this challenge, we propose a novel model updating framework called Slow and Fast Trajectory Learning (SFTL) network. SFTL aims to mitigate the discrepancy between past and future domains while quickly adapting to recent changes in small temporal drifts. This mechanism entails two interactions among three complementary learners: (i) the Working Learner, which updates model parameters using modern optimizers (e.g., Adam, Adagrad) and serves as the primary learner in the recommendation system, (ii) the Slow Learner, which is updated in each temporal domain by directly assigning the model weights of the working learner, and (iii) the Fast Learner, which is updated in each iteration by assigning exponentially moving average weights of the working learner. Additionally, we propose a novel rank-based trajectory loss to facilitate interaction between the working learner and trajectory learner, aiming to adapt to temporal drift and enhance performance in the current domain compared to the past. We provide theoretical understanding and conduct extensive experiments on real-world CTR prediction datasets to validate the effectiveness and efficiency of SFTL in terms of both convergence speed and model performance. The results demonstrate the superiority of SFTL over existing approaches.

Rarity updated ensemble with oversampling: An ensemble approach to classification of imbalanced data streams

AbstractToday's ever‐increasing generation of streaming data demands novel data mining approaches tailored to mining dynamic data streams. Data streams are non‐static in nature, continuously generated, and endless. They often suffer from class imbalance and undergo temporal drift. To address the classification of consecutive data instances within imbalanced data streams, this research introduces a new ensemble classification algorithm called Rarity Updated Ensemble with Oversampling (RUEO). The RUEO approach is specifically designed to exhibit robustness against class imbalance by incorporating an imbalance‐specific criterion to assess the efficacy of the base classifiers and employing an oversampling technique to reduce the imbalance in the training data. The RUEO algorithm was evaluated on a set of 20 data streams and compared against 14 baseline algorithms. On average, the proposed RUEO algorithm achieves an average‐accuracy of 0.69 on the real‐world data streams, while the chunk‐based algorithms AWE, AUE, and KUE achieve average‐accuracies of 0.48, 0.65, and 0.66, respectively. The statistical analysis, conducted using the Wilcoxon test, reveals a statistically significant improvement in average‐accuracy for the proposed RUEO algorithm when compared to 12 out of the 14 baseline algorithms. The source code and experimental results of this research work will be publicly available at https://github.com/vkiani/RUEO.

Treatment Comparisons in Adaptive Platform Trials Adjusting for Temporal Drift

An adaptive platform trial (APT) is a multi-arm trial in the context of a single disease where treatment arms are allowed to enter or leave the trial based on some decision rule. If a treatment enters the trial later than the control arm, there exist nonconcurrent controls who were not randomized between the two arms under comparison. As APTs typically take long periods of time to conduct, temporal drift may occur, which requires the treatment comparisons to be adjusted for this temporal change. Under the causal inference framework, we propose two approaches for treatment comparisons in APTs that account for temporal drift, both based on propensity score weighting. In particular, to address unmeasured confounders, one approach is doubly robust in the sense that it remains valid so long as either the propensity score model is correctly specified or the time effect model is correctly specified. Simulation study shows that our proposed approaches have desirable operating characteristics with well controlled Type I error rates and high power with or without unmeasured confounders.

Revisiting the mysterious origin of FRB 20121102A with machine-learning classification

Abstract Fast radio bursts (FRBs) are millisecond-duration radio waves from the Universe. Even though more than 50 physical models have been proposed, the origin and physical mechanism of FRB emissions are still unknown. The classification of FRBs is one of the primary approaches to understanding their mechanisms, but previous studies classified conventionally using only a few observational parameters, such as fluence and duration, which might be incomplete. To overcome this problem, we use an unsupervised machine-learning model, the Uniform Manifold Approximation and Projection to handle seven parameters simultaneously, including amplitude, linear temporal drift, time duration, central frequency, bandwidth, scaled energy, and fluence. We test the method for homogeneous 977 sub-bursts of FRB 20121102A detected by the Arecibo telescope. Our machine-learning analysis identified five distinct clusters, suggesting the possible existence of multiple different physical mechanisms responsible for the observed FRBs from the FRB 20121102A source. The geometry of the emission region and the propagation effect of FRB signals could also make such distinct clusters. This research will be a benchmark for future FRB classifications when dedicated radio telescopes such as the square kilometer array or Bustling Universe Radio Survey Telescope in Taiwan discover more FRBs than before.

Evaluating temporal sand drift potential trends in the Sistan region, Southeast Iran.

The Sistan region in Southeastern Iran is one of the world's most sensitive areas when it comes to sandstorms and wind erosion. One of the most influential factors in interpreting sandstorms is sand drift potential (DP), which is directly related to wind speed. Accurately, monitoring this phenomenon is still being determined, considering various temporal scales. Therefore, the main aim of this research is to analyze the trend of DP on monthly and annual scales. Our results showed that monthly variations of DP reached the highest and lowest values in July (609 VU) and January (47 VU), respectively. Blowing sand predominantly moved southeast, and the directional index fluctuated from 0.88 to 0.94. The annual DP was measured equal to 2700 VU, signifying a relatively high value when compared to other arid regions worldwide. The trend analysis results obtained from the Mann-Kendall test revealed both positive trends during the period 1987-2001 and negative ones from 2002 to 2016). However, the positive trend was found statistically insignificant. Furthermore, Sen's slope test results demonstrated that a negative trend could be observed with a steeper slope during July, September, and August, while a positive trend could be observed with a steeper pitch during November, December, and June. We recommend that land managers and stakeholders involved in controlling blowing sand using biological and physical methods should consider these trends in the Sistan region. Implementing nature-based solutions or control strategies should focus on these temporal sequences.

Injection-locked soliton microcomb against temporal drifting

The soliton microcomb offers a unique and compact solution for photonics applications. However, the microcomb is suffering from the perturbations arising from complex higher-order effects such as self-steepening and third-order dispersion, leading to the temporal drift of soliton and the deviation of repetition rate. It is unfavorable to the stability in time and frequency domains. In this work, we numerically and theoretically demonstrate that the injection-locking scheme can effectively eliminate the soliton temporal drift and repetition rate deviation caused by complex higher-order effects. The mechanism of eliminating drift and deviation is explained, and the theoretically predicted stable soliton temporal position agrees well with the simulation. The modulation depth plays a key role in suppressing drift, and an experimental guide for adjusting modulation depth is given. This work enriches soliton dynamics under complex higher-order effects and provides a scheme to improve the stability and controllability of microcombs.

Improved Limits on the Coupling of Ultralight Bosonic Dark Matter to Photons from Optical Atomic Clock Comparisons.

We present improved constraints on the coupling of ultralight bosonic dark matter to photons based on long-term measurements of two optical frequency ratios. In these optical clock comparisons, we relate the frequency of the ^{2}S_{1/2}(F=0)↔^{2}F_{7/2}(F=3) electric-octupole (E3) transition in ^{171}Yb^{+} to that of the ^{2}S_{1/2}(F=0)↔^{2}D_{3/2}(F=2) electric-quadrupole (E2) transition of the same ion, and to that of the ^{1}S_{0}↔^{3}P_{0} transition in ^{87}Sr. Measurements of the first frequency ratio ν_{E3}/ν_{E2} are performed via interleaved interrogation of both transitions in a single ion. The comparison of the single-ion clock based on the E3 transition with a strontium optical lattice clock yields the second frequency ratio ν_{E3}/ν_{Sr}. By constraining oscillations of the fine-structure constant α with these measurement results, we improve existing bounds on the scalar coupling d_{e} of ultralight dark matter to photons for dark matter masses in the range of about (10^{-24}-10^{-17}) eV/c^{2}. These results constitute an improvement by more than an order of magnitude over previous investigations for most of this range. We also use the repeated measurements of ν_{E3}/ν_{E2} to improve existing limits on a linear temporal drift of α and its coupling to gravity.

Temporal and operation-induced instability of apparent soil electrical conductivity measurements

Measuring apparent soil electrical conductivity (ECa), using galvanic contact resistivity (GCR) and electromagnetic induction (EMI) techniques, is frequently conducted to reveal spatial soil heterogeneity. Various studies have demonstrated the possibilities for significant changes in the measured quantities over time with relatively stable spatial structure representations. The objective of this study was to quantify the effects of temporal drift and operational noise for three popular ECa mapping instruments. They were placed in stationary positions approximately 8 m apart in an area with relatively low ECa. Temporal drift was assessed using a series of 4.5-h data logs recorded under different weather conditions (from extremely hot to near freezing temperatures). The two EMI instruments were also used to quantify the effect of minor changes in the height, pitch and roll of the sensor with respect to the ground. These operational noise tests were replicated over several days. Our results reveal the GCR measurements of ECa, along with perpendicular coplanar EMI measurements, have shown relatively strong stability over time. Each operational effect introduced measurement uncertainties comparable to the impact of a change in temperature and soil water content.

Temperature Imposed Sensitivity Issues of Hetero-TFET Based pH Sensor.

An underlapped hetero-structure electrolyte Bio-TFET for potential of hydrogen (pH) sensing has been presented in this article. Intersection charge density (Dit) near the substrate-oxide junction can be employed to represent pH value within the simulation. A feasible fabrication scheme for the proposed model is specified here. A detailed simulation is performed with an ATLAS device simulator to examine the efficiency of the projected sensor. The impact of pH alterations on device features akin to the drain current (IDS), threshold potential (VTH), sensitivity regarding voltage (SV), and current (SI) is examined. The effect of phosphate-buffered saline (PBS) concentrations on the pH buffer are also scrutinized. Moreover, the impact of the reference voltage and current (VRef and IRef), and channel doping concentration (NCH) over SV and SI is analyzed methodically. Here, SV attains ≈ 100 mV/pH, which is superior to the Nernstian limit (59 mV/pH) and SI enhances nearly ten times per pH variation. Benchmarking is included to provide a quantitative assessment of the proposed model with the published literature. The impact of temperature on pH buffer, IDS, temporal drift parameters and sensitivities have been emphasized. Finally, the temperature-immunity aspect of the proposed Bio-HTFET based pH sensor is highlighted by comparing sensitivity parameter among state-of-the-art literature. Hence, the recommended pH sensor can be utilized as an outstanding substitute for the succeeding generation of biosensor applications.

Soliton stabilization in microresonators with high order dispersion via pump phase modulation

We theoretically and numerically investigate the effects of third-order dispersion on microresonator solitons in anomalous dispersion with an extended normalized Lugiato–Lefever equation. The temporal position drift of solitons in the microresonator induced by the third-order dispersion is observed. The soliton temporal location stabilization is realized via pump phase modulation. Moreover, the final stable cavity soliton temporal position is deviated to the maxima of the pump phase modulation field profile which is the stable position of the solitons without high-order dispersion. We also study the effectiveness of the pump phase modulation in different modulation parameters. The research method sheds light on the cavity soliton stabilization, on-chip ultrashort pulse formation and optical information processing.

Dynamics of Dark Pulse Affected by Higher-Order Effects in Microresonators

We theoretically investigate dynamics of dark pulse and Raman-Kerr microcombs generation influenced by higher-order effects, including high-order dispersion (HOD), stimulated Raman scattering (SRS) and self-steepening (SS) effects in silicon microresonators. These three effects cause the delay of dark pulse individually, or interact with each other to alter the drift velocity and direction of pulses. HOD effect can change pulse shift direction and even cause bifurcation. The temporal drift induced by SS or SRS effects could be balanced by the simultaneous third-order dispersion (TOD) engineering. In spectral domain, stable Raman-Kerr frequency comb will be generated due to the competition between strong SRS and Kerr effects. The Raman comb components are suppressed when HOD effect coexists, while SS effect has ignorable effect on the distribution of the Raman comb. Furthermore, the SS effect will increase the total energy of the spectrum by shifting the dispersive wave (DW) generation to the longer wavelength side. Our findings could deepen the understanding of intracavity nonlinear dynamics and provide theoretical guidance to precisely control the stabilization of dark pulse and the generation of broadband mid-infrared (MIR) microcomb.