A physical and concise halo model based on the depletion radius

We develop a self-consistent and accurate halo model by partitioning matter according to the depletion radii of haloes. Unlike conventional models that define haloes with the virial radius while relying on a separate exclusion radius or ad-hoc fixes to account for halo exclusion, our model distributes mass across all scales self-consistently. Using a cosmological simulation, we show that our halo definition leads to very simple and intuitive model components, with the one-halo term given by the Einasto profile with no truncation needed, and the halo-halo correlation function following a universal power-law form down to the halo boundary. The universal halo-halo correlation also allows us to easily model the distribution of unresolved haloes as well as diffuse matter. Convolving the halo profile with the halo-halo correlation function, we obtain a complete description of the halo-matter correlation across all scales, which self-consistently accounts for halo exclusion on the transition scale. Mass conservation is explicitly maintained in our model, and the scale dependence of the classical halo bias is easily reproduced. Our model can successfully reconstruct the halo-matter correlation function with percent level accuracy for halo virial masses in the range of $10^{11.5}h^{-1}{\rm M}_{\odot}<M_{\rm vir}<10^{15.35}h^{-1}{\rm M}_{\odot}$ at $z=0$, and covers the radial range of $0.01h^{-1}{\rm Mpc}<r<20h^{-1}{\rm Mpc}$. We also show that our model profile can accurately predict the characteristic depletion radius at the minimum bias and the splash-back radius at the steepest density slope locations.Comment: 19 pages, 19 figure

Data driven process monitoring based on neural networks and classification trees

Process monitoring in the chemical and other process industries has been of great practical importance. Early detection of faults is critical in avoiding product quality deterioration, equipment damage, and personal injury. The goal of this dissertation is to develop process monitoring schemes that can be applied to complex process systems. Neural networks have been a popular tool for modeling and pattern classification for monitoring of process systems. However, due to the prohibitive computational cost caused by high dimensionality and frequently changing operating conditions in batch processes, their applications have been difficult. The first part of this work tackles this problem by employing a polynomial-based data preprocessing step that greatly reduces the dimensionality of the neural network process model. The process measurements and manipulated variables go through a polynomial regression step and the polynomial coefficients, which are usually of far lower dimensionality than the original data, are used to build a neural network model to produce residuals for fault classification. Case studies show a significant reduction in neural model construction time and sometimes better classification results as well. The second part of this research investigates classification trees as a promising approach to fault detection and classification. It is found that the underlying principles of classification trees often result in complicated trees even for rather simple problems, and construction time can excessive for high dimensional problems. Fisher Discriminant Analysis (FDA), which features an optimal linear discrimination between different faults and projects original data on to perpendicular scores, is used as a dimensionality reduction tool. Classification trees use the scores to separate observations into different fault classes. A procedure identifies the order of FDA scores that results in a minimum tree cost as the optimal order. Comparisons to other popular multivariate statistical analysis based methods indicate that the new scheme exhibits better performance on a benchmarking problem

Chalcogenide-on-Lithium Niobate Resonator Waveguides and their nonlinear applications

Chalcegenide glass material, such as amorphous As2S3, is an ideal candidate material to be integrated onto a much lower refractive index substrate and used as an all-optical active device. The As2S3 glass is with wide infrared transparence from near IR to mid IR and refractive index as high as 2.45 at 1.55 μm. As2S3 glass also shows a good potential as a Kerr medium for ultra-fast all-optical tuning capability because of its high nonlinearity coefficient at infrared wavelength range. In terms of the all-optical nonlinear application, resonant cavity devices are favored for their easily tunable ability as a small change of refractive index in the material would lead to a shift for their resonance. Therefore, it is motivating to combine the As2S3-on-LiNbO3 optical waveguide platform and the resonant cavity structure together for the integrated all-optical circuits. A vertically integrated As2S3 ring resonator side-coupled to a low-index Ti:diffused LiNbO3 straight waveguide was designed and fabricated. At 1.55-μm wavelength, a low 1.2 dB/cm propagation loss and an over 30-dB extinction ratio were demonstrated on the fabricated As2S3-on-LiNbO3 ring resonator waveguide with 400-μm bend radius, which corresponded to an intrinsic Q value as high as 3.5x105. At the same time, an integrated As2S3-on-LiNbO3 optical cavity waveguide based on sidewall grating couplers was designed, fabricated and optically tested. Using the sidewall grating couplers with a coupling strength as high as 14 mm-1, the cavity resonant response with a FSR of 0.5 nm over a 5 nm bandwidth at 1.55 μm was demonstrated with a cavity propagation loss at 2.5 dB/cm. The waveguide nonlinear efficiency γ of the As2S3-on-LiNbO3 ring waveguide was calculated at 3.85 radian/m•W and a pump-signal measurement platform was setup to observe the nonlinear tuning phenomenon of the ring resonator waveguide. Also, the nonlinear tunability of our hybrid As2S3-on-LiNbO3 grating cavity waveguide is numerically analyzed. The optical energy at the resonant wavelength inside the grating cavity waveguide is 7 times as high as the input energy, which would significantly reduce the pump power for the nonlinear tuning applications

Characterization of transient groundwater flow through a high arch dam foundation during reservoir impounding

AbstractEven though a large number of large-scale arch dams with height larger than 200 m have been built in the world, the transient groundwater flow behaviors and the seepage control effects in the dam foundations under difficult geological conditions are rarely reported. This paper presents a case study on the transient groundwater flow behaviors in the rock foundation of Jinping I double-curvature arch dam, the world's highest dam of this type to date that has been completed. Taking into account the geological settings at the site, an inverse modeling technique utilizing the time series measurements of both hydraulic head and discharge was adopted to back-calculate the permeability of the foundation rocks, which effectively improves the uniqueness and reliability of the inverse modeling results. The transient seepage flow in the dam foundation during the reservoir impounding was then modeled with a parabolic variational inequality (PVI) method. The distribution of pore water pressure, the amount of leakage, and the performance of the seepage control system in the dam foundation during the entire impounding process were finally illustrated with the numerical results

Treated amblyopes remain deficient in spatial vision: A contrast sensitivity and external noise study

AbstractTo evaluate residual spatial vision deficits in treated amblyopia, we recruited five clinically treated amblyopes (mean age=10.6 years). Contrast sensitivity functions (CSF) in both the previously amblyopic eyes (pAE; visual acuity=0.944±0.019 MAR) and fellow eyes (pFE; visual acuity=0.936±0.021 MAR) were measured using a standard psychophysical procedure for all the subjects. The results indicated that the treated amblyopes remained deficient in spatial vision, especially at high spatial frequencies, although their Snellen visual acuity had become normal in the pAEs. To identify the mechanisms underlying spatial vision deficits of treated amblyopes, threshold vs external noise contrast (TvC) functions – the signal contrast necessary for the subject to maintain a threshold performance level in varying amounts of external noise (“TV snow”) – were measured in both eyes of four of the subjects in a sine-wave grating detection task at several spatial frequencies. Two mechanisms of amblyopia were identified: increased internal noise at low to medium spatial frequencies, and both increased internal noise and increased impact of external noise at high spatial frequencies. We suggest that, in addition to visual acuity, other tests of spatial vision (e.g., CSF, TvC) should be used to assess treatment outcomes of amblyopia therapies. Training in intermediate and high spatial frequencies may be necessary to fully recover spatial vision in amblyopia in addition to the occlusion therapy

Broad bandwidth of perceptual learning in second-order contrast modulation detection

Comparing characteristics of learning in first- and second-order systems might inform us about different neural plasticity in the two systems. In the current study, we aim to determine the properties of perceptual learning in second-order contrast modulation detection in normal adults. We trained nine observers to detect second-order gratings at an envelope modulation spatial frequency of 8 cycles/8 with their nondominant eyes. We found that, although training generated the largest improvements around the trained frequency, contrast sensitivity over a broad range of spatial frequencies also improved, with a 4.09-octave bandwidth of perceptual learning, exhibiting specificity to the trained spatial frequency as well as a relatively large degree of generalization. The improvements in the modulation sensitivity function (MSF) were not significantly different between the trained and untrained eyes. Furthermore, training did not significantly change subjects&#39; ability in detecting firstorder gratings. Our results suggest that perceptual learning in second-order detection might occur at the postchannel level in binocular neurons, possibly through reducing the internal noise of the visual system

Simple synthetic data reduces sycophancy in large language models

Sycophancy is an undesirable behavior where models tailor their responses to follow a human user's view even when that view is not objectively correct (e.g., adapting liberal views once a user reveals that they are liberal). In this paper, we study the prevalence of sycophancy in language models and propose a simple synthetic-data intervention to reduce this behavior. First, on a set of three sycophancy tasks (Perez et al., 2022) where models are asked for an opinion on statements with no correct answers (e.g., politics), we observe that both model scaling and instruction tuning significantly increase sycophancy for PaLM models up to 540B parameters. Second, we extend sycophancy evaluations to simple addition statements that are objectively incorrect, finding that despite knowing that these statements are wrong, language models will still agree with them if the user does as well. To reduce sycophancy, we present a straightforward synthetic-data intervention that takes public NLP tasks and encourages models to be robust to user opinions on these tasks. Adding these data in a lightweight finetuning step can significantly reduce sycophantic behavior on held-out prompts. Code for generating synthetic data for intervention can be found at https://github.com/google/sycophancy-intervention