Length Uncertainty Research Articles

PULMO: Precise utterance-level modeling for speech anti-spoofing

In recent years, most state-of-the-art approaches for spoofed speech detection have been based on convolutional neural networks (CNNs). Most neural networks, including CNNs, are trained in minibatch units, where all input data in each minibatch must have the same shape. Therefore, for minibatch training, each utterance is first either padded or truncated because utterances are variable-length sequences and thus cannot be directly fed into networks in minibatch units. However, modeling either a padded or truncated utterance, rather than the original one, makes it unfeasible to capture the entire context as is: padding could propagate even unwanted information, like artifacts, in the original utterance, and truncation inevitably loses some information. With these information distortions, model could get stuck in a suboptimal solution. To fill this gap, we proposeÚ a method for precise utterance-level modeling that enables minibatch-wise utterance-level modeling of variable-length utterances while minimizing the information distortions. The proposed method comprises sequence segmentation followed by segment aggregation. Sequence segmentation feeds variable-length utterances in the minibatch unit by decomposing each of them into fixed-length segments, which enables parallel processing of variable-length utterances without the uncertainty in input length. Segment aggregation plays a role in aggregating the segment embeddings by utterance to encode the entire information of each utterance. The experimental results of the evaluation trials of ASVspoof 2019 and 2021 indicate that the proposed method shows up to 84.9 % and 97.6 % relative equal error rate reductions on logical and physical access scenarios, respectively. Furthermore, the proposed method reduced the FLOPs for an epoch by 6 %.

Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval.

Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for climate research. Here, we propose a novel pretrained deep learning framework that can extract information underlying each satellite pixel and use it to create new latent features that can be employed for improving retrieval accuracy in regions without in situ data. With the proposed model, we developed a new global fAOD (at 0.5 μm) data from 2001 to 2020, resulting in a 10% improvement in the overall correlation coefficient (R) during site-based independent validation and a 15% enhancement in non-AERONET site areas validation. Over the past two decades, there has been a noticeable downward trend in global fAOD (-1.39 × 10-3/year). Compared to the general deep-learning model, our method reduces the global trend's previously overestimated magnitude by 7% per year. China has experienced the most significant decline (-5.07 × 10-3/year), which is 3 times greater than the global trend. Conversely, India has shown a significant increase (7.86 × 10-4/year). This study bridges the gap between sparse in situ observations and abundant satellite measurements, thereby improving predictive models for global patterns of fAOD and other climate factors.

Algebraic solution and thermodynamic properties for the one- and two-dimensional Dirac oscillator with minimal length uncertainty relations

We study the quantum characteristics of the Dirac oscillator within the framework of Heisenberg's generalized uncertainty principle. This principle leads to the appearance of a minimal length of the order of the Planck length. Hidden symmetries are identified to solve the model algebraically. The presence of the minimal length leads to a quadratic dependence of the energy spectrum on the quantum number n, implying the hard confinement property of the system. Thermodynamic properties are calculated using the canonical partition function. The latter is well determined by the method based on Epstein's zeta function. The results reveal that the minimal length has a significant effect on the thermodynamic properties.

Inversion model for extracting chemically resolved depth profiles across liquid interfaces of various configurations from XPS data: PROPHESY.

PROPHESY, a technique for the reconstruction of surface-depth profiles from X-ray photoelectron spectroscopy data, is introduced. The inversion methodology is based on a Bayesian framework and primal-dual convex optimization. The acquisition model is developed for several geometries representing different sample types: plane (bulk sample), cylinder (liquid microjet) and sphere (droplet). The methodology is tested and characterized with respect to simulated data as a proof of concept. Possible limitations of the method due to uncertainty in the attenuation length of the photo-emitted electron are illustrated.

Born reciprocity and relativistic generalized uncertainty principle in Finsler structure: Fundamental tensor in discretized curved spacetime

The main findings of the generalized uncertainty principle (GUP), the phenomenological approach, for instance, the emergence of a minimal measurable length uncertainty, are obtained in various versions from theories of quantum gravity, such as string theory, loop quantum gravity, doubly special relativity and black hole physics. GUP counts for impacts of relativistic energies and finite gravitational fields on the fundamental theories of quantum mechanics (QM), the noncommutation and measurement uncertainty. Utilizing GUP in reconciling principles of general relativity (GR) and QM, thereby enables to draw convincing conclusions about quantum gravity. To resolve the shortcuts reported with the nonrelativistic three-dimensional GUP, namely, violation of Lorentz covariance, dependence on frame of reference, and violation of the linear additional law of momenta, we introduce relativistic four-dimensional generalized uncertainty principle (RGUP) to curved spacetime. To unify GR and QM, we apply the Born reciprocity principle (BRP), distance-momentum duality symmetry and RGUP to estimate the fundamental tensor in discretized curved spacetime. To this end, we generalize Riemann geometry. The Finsler geometry, which is characterized by manifold and Finsler structure, allows to directly apply RGUP to the Finsler structure of a free particle so that [Formula: see text] can be expressed as [Formula: see text], from which the metric tensor in discretized Riemann spacetime could be deduced. We conclude that [Formula: see text] is homogeneous with degree [Formula: see text] in [Formula: see text], while [Formula: see text] is [Formula: see text]-homogeneous resulting in [Formula: see text]. Despite, the astonishing similarity with the conformal transformation, know as Weyl tensor, this study suggests that principles of QMs could be unambiguously imposed on the resulting fundamental tensor. Also, we conclude that the features of Finsler geometry assumed in this study are likely the ones of the duel Hamilton geometry.

Path integral in position-deformed Heisenberg algebra with maximal length uncertainty

In this work, we study the path integral in a position-deformed Heisenberg algebra with quadratic deformation which implements both minimal momentum and maximal length uncertainties. We construct propagators of path integrals within this deformed algebra using the position space representation on the one hand and the Fourier transform and its inverse representations on the other. The result is remarkably similar to the one obtained by Pramanik (2022) from the Perivolaropoulos’s deformed algebra (Perivolaropoulos, 2017). Then, the propagators and the corresponding actions of a free particle and a simple harmonic oscillator are discussed as examples. We also show that the actions which describe the classical trajectories of both systems are bounded by the ordinary ones of classical mechanics due to the existence of this maximal length. Consequently, particles of these systems travel faster from one point to another with low kinetic and mechanical energies.

Short-term water demand forecasting using data-centric machine learning approaches

Abstract Accurate water demand forecasting is the key to urban water management and can alleviate system pressure brought by urbanisation, water scarcity and climate change. However, existing research on water demand forecasting using machine learning is focused on model-centric approaches, where various forecasting models are tested to improve accuracy. The study undertakes a data-centric machine learning approach by analysing the impact of training data length, temporal resolution and data uncertainty on forecasting model results. The models evaluated are Autoregressive (AR) Integrated Moving Average (ARIMA), Neural Network (NN), Random Forest (RF) and Prophet. The first two are commonly used forecasting models. RF has shown similar forecast accuracy to NN but has received less attention. Prophet is a new model that has not been applied to short-term water demand forecasting, though it has had successful applications in various fields. The results obtained from four case studies show that (1) data-centric machine learning approaches offer promise for improving forecast accuracy of short-term water demands; (2) accurate forecasts are possible with short training data; (3) RF and NN models are superior at forecasting high-temporal resolution data; and (4) data quality improvement can achieve a level of accuracy increase comparable to model-centric machine learning approaches.

Design of a mathematical model and a simulation-optimisation approach for master surgical scheduling considering uncertainty in length of stay, demands and duration of surgery

Design of a mathematical model and a simulation-optimisation approach for master surgical scheduling considering uncertainty in length of stay, demands and duration of surgery

High-accuracy transmission and fluorescence XAFS of zinc at 10 K, 50 K, 100 K and 150 K using the hybrid technique.

The most accurate measurements of the mass attenuation coefficient for metals at low temperature for the zinc K-edge from 9.5 keV to 11.5 keV at temperatures of 10 K, 50 K, 100 K and 150 K using the hybrid technique are reported. This is the first time transition metal X-ray absorption fine structure (XAFS) has been studied using the hybrid technique and at low temperatures. This is also the first hybrid-like experiment at the Australian Synchrotron. The measured transmission and fluorescence XAFS spectra are compared and benchmarked against each other with detailed systematic analyses. A recent method for modelling self-absorption in fluorescence has been adapted and applied to a solid sample. The XAFS spectra are analysed using eFEFFIT to provide a robust measurement of the evolution of nanostructure, including such properties as net thermal expansion and mean-square relative displacement. This work investigates crystal dynamics, nanostructural evolution and the results of using the Debye and Einstein models to determine atomic positions. Accuracies achieved, when compared with the literature, exceed those achieved by both relative and differential XAFS, and represent a state-of-the-art for future structural investigations. Bond length uncertainties are of the order of 20-40 fm.

Modified Brans–Dicke cosmology with minimum length uncertainty

We consider a modification of the Brans-Dicke gravitational Action Integral inspired by the existence of a minimum length uncertainty for the scalar field. In particular, the kinetic part of the Brans-Dicke scalar field is modified such that the equation of motion for the scalar field be modified according to the quadratic Generalized Uncertainty Principle (GUP). For the background geometry, we assume the homogeneous and isotropic Friedmann--Lema\^{\i}tre--Robertson--Walker metric. We investigate the dynamics and the cosmological evolution for the dynamical variables of the theory and we compare the results with the unmodified Brans-Dicke theory. It follows that in consideration because of the additional degrees of freedom in the energy-momentum tensor the dynamical variables describe various aspects of the cosmological history. This is one of the first studies on the effects of GUP in a Machian gravitational theory.

The reduction in laminar burning velocity and Markstein length extrapolation uncertainty for TRF-air mixtures

The laminar burning velocity and Markstein length of TRF (n-heptane, isooctane, and toluene)-air mixtures were investigated at the initial temperature of 400 K and 453 K, initial pressure of 1 bar, and an equivalence ratio range of 0.8–1.5. The effects of extrapolation radius (Rf) and Markstein length (Lb) were discussed on the linear and nonlinear extrapolation models. The results showed that the linear model leads to higher extrapolation result for all equivalence ratios compared with the nonlinear model due to the differences in the third term of Taylor expansion. Finally, the laminar burning velocity and Markstein length uncertainty caused by the extrapolation model can be minimized by adjusting LbRf1 (Rf1 refers to the flame initial extrapolated radius). When the value of LbRf1 was below 0.012, the laminar burning velocity and Markstein length uncertainty can be controlled at < 0.05 and < 0.5, respectively.

Stride frequency derived from GPS speed fluctuations in galloping horses

Changes in gallop stride parameters prior to injury have been documented previously in Thoroughbred racehorses. Validating solutions for quantification of fundamental stride parameters is important for large scale studies investigating injury related factors. This study describes a fast Fourier transformation-based method for extracting stride frequency (SF) values from speed fluctuations recorded with a standalone GPS-logger suitable for galloping horses. Limits of agreement with SF values derived from inertial measurement unit (IMU) pitch data are presented. Twelve Thoroughbred horses were instrumented with a GPS-logger (Vbox sport, Racelogic, 10 Hz samplerate) and a IMU-logger (Xsens DOT, Xsens, 120 Hz samplerate), both attached to the saddlecloth in the midline caudal to the saddle and time synchronized by minimizing root mean square error between differentiated GPS and IMU heading. Each horse performed three gallop trials with a target speed of 36miles per hour (16.1 ms−1) on a dirt racetrack. Average speed was 16.48 ms−1 ranging from 16.1 to 17.4 ms−1 between horses. Limits of agreement between GPS- and IMU-derived SF had a bias of 0.0032 Hz and a sample-by-sample precision of +/−0.027 Hz calculated over N = 2196 values. The stride length uncertainty related to the trial-by-trial SF precision of 0.0091 Hz achieved across 100 m gallop sections is smaller than the 10 cm decrease in stride length that has been associated with an increased risk of musculoskeletal injury. This suggests that the described method is suitable for calculating fundamental stride parameters in the context of injury prevention in galloping horses.

A Possible Solution of the Cosmological Constant Problem Based on GW170817 and Planck Observations with Minimal Length Uncertainty

We propose generalized uncertainty principle (GUP) with an additional term of quadratic momentum motivated by string theory and black hole physics and providing a quantum mechanical framework for the minimal length uncertainty, at the Planck scale. We demonstrate that the GUP parameter, β 0 , could be best constrained by the gravitational wave observations, GW170817 event. To determine the difference between the group velocity of graviton and that of the light, we suggest another proposal based on the modified dispersion relations (MDRs). We conclude that the upper bound of β 0 reads ≃1060. Utilizing features of the UV/IR correspondence and the apparent similarities between GUP (including nongravitating and gravitating impacts on Heisenberg uncertainty principle) and the discrepancy between the theoretical and the observed cosmological constant Λ (obviously manifesting gravitational influences on the vacuum energy density), known as catastrophe of nongravitating vacuum, we suggest a possible solution for this long-standing physical problem, Λ ≃ 1 0 − 47 GeV4/ℏ3c3.

A consistent approach to the path integral formalism of quantum mechanics based on the maximum length uncertainty

We have developed a proper path integral formalism consistent with the deformed version of the quantum mechanics that contains a maximum observable length scale at the order of the cosmological particle horizon, existing in cosmology. We have started by modifying the classical mechanics which shows non-minimal effects on the equation of motion of a particle. Next, we have provided representation of the deformed quantum mechanical algebra. With this algebra in hand, we have calculated the general form of the path integral propagator in this deformed background. Thereafter, as a most simple case, we have built up the explicit form of the free particle propagator. The modifications to the free particle propagator show some non-trivial effects in this case. Our formalism can be applied to analyze the quantum properties and observations emerging in the context of cosmology.

Service at risk in delivery operations

AbstractThis article examines disruption risks at fulfillment centers and develops risk mitigation strategies based on inventory stocking and delivery decisions. It considers a Fortune 150 firm whose delivery operations are designed to fulfill the orders from contracted business customers within the next day. The firm promises its customers that the probability of late deliveries exceeding a certain threshold will be limited. We coin this requirement as the service‐at‐risk (SaR) constraint. The firm proactively determines the inventory amount to be kept in each fulfillment center. If a disruption occurs, the firm determines the best way to deliver orders from its operational fulfillment centers and vendors under disruption length and demand uncertainty to minimize additional costs and satisfy the SaR constraint. This article makes four main contributions. First, we find a surprising result that total inventory commitment can decrease with risk aversion when there exists a disruption possibility that impacts two nearby facilities together. Using actual data from the motivating firm, the numerical analysis demonstrates that this phenomenon exists in practice. Second, we define a new metric: The Risk Dispersion Index (RDI), which measures the dispersion in risk exposure across fulfillment centers. It leads to a lower and more balanced risk exposure in the firm's delivery operations. Third, we find that a facility may elect to abandon its own customers to serve the customers of a disrupted facility; this behavior becomes more prominent under risk aversion. Fourth, the introduction of demand uncertainty leads to a smaller inventory commitment for a risk‐neutral retailer.

Higher-order GUP black hole based on COW experiment and Einstein–Bohr’s photon box

In this work, we have explored the effects of the higher-order generalized uncertainty principle (GUP), inspired by the quantum gravity COW experiment and Einstein–Bohr’s photon box thought experiment, on the properties of a black hole. Two different GUP models, namely GUP to all orders in the Planck length model and GUP with minimal length uncertainty and maximal momentum model, are considered for our study. For each model, we have investigated the modified de Broglie formula, modification in gravitational phase shift, Einstein–Bohr’s photon box, and the modified Hawking temperature and the tidal force of the GUP modified black hole.

Observational signatures of quantum gravity in interferometers

We consider the uncertainty in the arm length of an interferometer due to metric fluctuations from the quantum nature of gravity, proposing a concrete microscopic model of energy fluctuations in holographic degrees of freedom on the surface bounding a causally connected region of spacetime. In our model, fluctuations longitudinal to the beam direction accumulate in the infrared and feature strong long distance correlation in the transverse direction. This leads to a signal that could be observed in a gravitational wave interferometer. We connect the positional uncertainty principle arising from our calculations to the 't Hooft gravitational S-matrix.

Thermodynamic properties and algebraic solution of the N-dimensional harmonic oscillator with minimal length uncertainty relations

The algebraic solution of the deformed N-dimensional harmonic oscillator in the presence of a minimal length is determined. To reach this goal, we identify a hidden symmetry for the N free deformed harmonic oscillators. This allows us to algebraically determine the spectrum of the system. After determining the deformed partition function, we give some thermodynamics properties such as the mean energy, the mean free energy, the entropy, and the specific heat. The effect of the minimal length on all thermodynamic properties is very clear and considerable. In particular, from the curve of the entropy function no abrupt change has been identified for different values of the deformation parameter β. This means that the curvature observed in the specific heat curve does not present or indicate the existence of a phase transition. In the limit β = 0, the specific heat of a crystalline body is independent of the temperature and of the body considered for large values of the temperature.

Estimating pile length uncertainty with Kriging

Piles are used to transfer loads from the structure into the bearing soil layer. The accurate estimation of the pile lengths is a complex statistical problem that includes several spatial and measurement-based uncertainties. Commonly, bearing subsurface is modeled with linear triangular networks, and geometry related uncertainty is considered with overall safety factor. Currently, it is common practice is to solve the unknowns in this problem with simplistic borehole analysis and engineering judgment. Our paper presents a statistical pile length model based on the borehole uncertainty analysis. Modern geotechnical designing contains multiple steps including ground investigations, computer modeling, and expert decision making. All the included steps add uncertainty to the design process. The problem with these traditional methods is that the sources and the magnitudes of the uncertainty are not visible to the designer, which can lead to non-optimal decisions. With the help of the state-of-the-art probabilistic models, a design pipeline can be constructed that propagates the uncertainty from process to process and is transparent about its sources and magnitudes. In our paper, we show how the probabilistic pipeline approach enables us to make more informed decisions. This is due to uncertainty propagation from ground investigations to the 3D volumetric model. Given the uncertainty, we can estimate the mean and variance of the future costs and the impact that different design decisions have. The paper presents a practical example where probabilistic models are utilized to improve decision making.

Battery sizing for plug-in hybrid electric buses considering variable route lengths

For plug-in hybrid electric buses, optimizing the battery size is beneficial for lowering the capital cost and improving the efficiency of energy split between multiple power sources. In addition to the energy management strategy, the optimization of battery size is highly dependent on expected trip mileage. For a fleet of urban buses, the travel distance varies between different routes or even within the same route due to random events. The strategy proposed in this paper addresses uncertainty in route length by a method that coordinates the energy management strategy and battery aging to determine optimal battery size. The route length is assumed to follow a normal distribution, and the overall speed profile along the route is composed of basic driving cycles established using a Markov chain model. The co-optimization of battery size, energy management, and battery aging is formulated as a convex programming problem. The optimal battery size for a given expectation value of route length is identified by minimizing the total weighted cost of energy consumption and battery aging over the whole route length distribution. The results, when assuming a variable route length and calculating a weighted cost, are significantly different compared to the case when a fixed route length is used. When optimal depth-of-discharge is not considered, the weighted cost versus route length has a shallow parabolic shape, and an optimal battery size corresponding to minimum weighted cost can be identified. If the optimal depth-of-discharge is taken into account, the optimal battery size is roughly constant versus route length.