julilili42/papers huggingface dataset:
Short-Term Peripheral Contrast Reduction Affects Central Chromatic and Achromatic Contrast Sensitivity
2022-02-22

Peripheral retinal contrast reduction is suggested as a potential myopia control strategy. However, the underlying mechanism is yet unknown. Therefore, this study investigated the influence of peripheral contrast reduction on central chromatic and achromatic contrast sensitivity (CS). A total of 19 participants were included. Peripheral contrast reduction was induced via Bangerter foils of 0.4 and 0.8 density, each with a clear central zone of 8.0 mm diameter. Central achromatic and chromatic (for S-, M-, and L-cone types) CS was measured at 3 and 12 cpd in a 2-IFC psychophysical procedure. CS was tested monocularly at 0, 30, and 90 min of adaptation time, while the fellow eye was covered by an infrared filter. With the filter in place, pupil size was controlled to be smaller than the clear central aperture. Data were analyzed using linear mixed models. Cone-type CS showed significant differences among each other (all p &lt; 0.05), except for the achromatic and L-cone type (p = 0.87). The minimum sensitivity was found with the S-cone type and the maximum with the M-cone type. Central achromatic and chromatic CS were equally affected by diffusion. The level of peripheral diffusion also influenced CS, while the 0.8 Bangerter foil led to a higher reduction in CS compared to the 0.4 Bangerter foil (p = 0.0008) and the control condition (p = 0.05). A significant reduction in CS occurred between 30 and 90 min of adaptation time (p &lt; 0.0001). The current study found that peripheral contrast reduction impacted central achromatic and chromatic CS equally. It further showed that the amplitude of reduction was influenced by the level of diffusion, with the reduction becoming more pronounced over time.
Improved Process Control by Using the Effective Scattering Coefficients to Determine the Fat Content in Homogenized Cow-Based Milk with Multivariate Data Modeling
2022-02-22

Commercially available homogenized cow- and plant-based milks were investigated by optical spectroscopy in the range of 400–1360 nm. Absorbance spectra, the effective scattering coefficient μs′, and the spectral absorption coefficient μa were recorded for 23 milk varieties and analyzed by multivariate data analysis. Cow- and plant-based milks were compared and discriminated using principal component analysis combined with a quadratic discriminant analysis. Furthermore, it was possible to discriminate the origin of plant-based milk by μa and the fat content in cow-based milk by μs′. Partial least squares regression models were developed to determine the fat content in cow-based milk. The model for μs′ proved to be the most efficient for this task with R2 = 0.98 and RMSEP = 0.19 g/100 mL for the external validation. Thus, optical spectroscopy together with multivariate data analysis is suitable for routine laboratory analysis or quality monitoring in the dairy production.
Full history recursive multilevel Picard approximations for ordinary differential equations with expectations
2022-02-22

We consider ordinary differential equations (ODEs) which involve expectations of a random variable. These ODEs are special cases of McKean-Vlasov stochastic differential equations (SDEs). A plain vanilla Monte Carlo approximation method for such ODEs requires a computational cost of order to achieve a root-mean-square error of size . In this work we adapt recently introduced full history recursive multilevel Picard (MLP) algorithms to reduce this computational complexity. Our main result shows for every that the proposed MLP approximation algorithm requires only a computational effort of order to achieve a root-mean-square error of size .
Prediction of progressive lens performance from neural network simulations
2022-02-22

Purpose: The purpose of this study is to present a framework to predict visual acuity (VA) based on a convolutional neural network (CNN) and to further to compare PAL designs. Method: A simple two hidden layer CNN was trained to classify the gap orientations of Landolt Cs by combining the feature extraction abilities of a CNN with psychophysical staircase methods. The simulation was validated regarding its predictability of clinical VA from induced spherical defocus (between +/-1.5 D, step size: 0.5 D) from 39 subjectively measured eyes. Afterwards, a simulation for a presbyopic eye corrected by either a generic hard or a soft PAL design (addition power: 2.5 D) was performed including lower and higher order aberrations. Result: The validation revealed consistent offset of +0.20 logMAR +/-0.035 logMAR from simulated VA. Bland-Altman analysis from offset-corrected results showed limits of agreement (+/-1.96 SD) of -0.08 logMAR and +0.07 logMAR, which is comparable to clinical repeatability of VA assessment. The application of the simulation for PALs confirmed a bigger far zone for generic hard design but did not reveal zone width differences for the intermediate or near zone. Furthermore, a horizontal area of better VA at the mid of the PAL was found, which confirms the importance for realistic performance simulations using object-based aberration and physiological performance measures as VA. Conclusion: The proposed holistic simulation tool was shown to act as an accurate model for subjective visual performance. Further, the simulations application for PALs indicated its potential as an effective method to compare visual performance of different optical designs. Moreover, the simulation provides the basis to incorporate neural aspects of visual perception and thus simulate the VA including neural processing in future.
DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling
2022-02-22

Since 2014 transfer learning has become the key driver for the improvement of spatial saliency prediction; however, with stagnant progress in the last 3-5 years. We conduct a large-scale transfer learning study which tests different ImageNet backbones, always using the same read out architecture and learning protocol adopted from DeepGaze II. By replacing the VGG19 backbone of DeepGaze II with ResNet50 features we improve the performance on saliency prediction from 78% to 85%. However, as we continue to test better ImageNet models as backbones (such as EfficientNetB5) we observe no additional improvement on saliency prediction. By analyzing the backbones further, we find that generalization to other datasets differs substantially, with models being consistently overconfident in their fixation predictions. We show that by combining multiple backbones in a principled manner a good confidence calibration on unseen datasets can be achieved. This new model, "DeepGaze IIE", yields a significant leap in benchmark performance in and out-of-domain with a 15 percent point improvement over DeepGaze II to 93% on MIT1003, marking a new state of the art on the MIT/Tuebingen Saliency Benchmark in all available metrics (AUC: 88.3%, sAUC: 79.4%, CC: 82.4%).
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
2022-02-22

Recently, there has been an increasing number of efforts to introduce models capable of generating natural language explanations (NLEs) for their predictions on vision-language (VL) tasks. Such models are appealing, because they can provide human-friendly and comprehensive explanations. However, there is a lack of comparison between existing methods, which is due to a lack of re-usable evaluation frameworks and a scarcity of datasets. In this work, we introduce e-ViL and e-SNLI-VE. e-ViL is a benchmark for explainable vision-language tasks that establishes a unified evaluation framework and provides the first comprehensive comparison of existing approaches that generate NLEs for VL tasks. It spans four models and three datasets and both automatic metrics and human evaluation are used to assess model-generated explanations. e-SNLI-VE is currently the largest existing VL dataset with NLEs (over 430k instances). We also propose a new model that combines UNITER, which learns joint embeddings of images and text, and GPT-2, a pre-trained language model that is well-suited for text generation. It surpasses the previous state of the art by a large margin across all datasets. Code and data are available here: https://github.com/maximek3/e-ViL.
Learning Zero-Shot Multifaceted Visually Grounded Word Embeddings via Multi-Task Training
2022-02-22

Language grounding aims at linking the symbolic representation of language (e.g., words) into the rich perceptual knowledge of the outside world. The general approach is to embed both textual and visual information into a common space -the grounded space-confined by an explicit relationship between both modalities. We argue that this approach sacrifices the abstract knowledge obtained from linguistic co-occurrence statistics in the process of acquiring perceptual information. The focus of this paper is to solve this issue by implicitly grounding the word embeddings. Rather than learning two mappings into a joint space, our approach integrates modalities by determining a reversible grounded mapping between the textual and the grounded space by means of multi-task learning. Evaluations on intrinsic and extrinsic tasks show that our embeddings are highly beneficial for both abstract and concrete words. They are strongly correlated with human judgments and outperform previous works on a wide range of benchmarks. Our grounded embeddings are publicly available here.
Vigilance, arousal, and acetylcholine: Optimal control of attention in a simple detection task
2022-02-22

Abstract Paying attention to particular aspects of the world or being more vigilant in general can be interpreted as forms of ‘internal’ action. Such arousal-related choices come with the benefit of increasing the quality and situational appropriateness of information acquisition and processing, but incur potentially expensive energetic and opportunity costs. One implementational route for these choices is widespread ascending neuromodulation, including by acetylcholine (ACh). The key computational question that elective attention poses for sensory processing is when it is worthwhile paying these costs, and this includes consideration of whether sufficient information has yet been collected to justify the higher signal-to-noise ratio afforded by greater attention and, particularly if a change in attentional state is more expensive than its maintenance, when states of heightened attention ought to persist. We offer a partially observable Markov decision-process treatment of optional attention in a detection task, and use it to provide a qualitative model of the results of studies using modern techniques to measure and manipulate ACh in rodents performing a similar task.
MRI lung lobe segmentation in pediatric cystic fibrosis patients using a recurrent neural network trained with publicly accessible CT datasets
2022-02-22

Purpose: To introduce a widely applicable workflow for pulmonary lobe segmentation of MR images using a recurrent neural network (RNN) trained with chest computed tomography (CT) datasets. The feasibility is demonstrated for 2D coronal ultra-fast balanced steady-state free precession (ufSSFP) MRI. Methods: Lung lobes of 250 publicly accessible CT datasets of adults were segmented with an open-source CT-specific algorithm. To match 2D ufSSFP MRI data of pediatric patients, both CT data and segmentations were translated into pseudo-MR images, masked to suppress anatomy outside the lung. Network-1 was trained with pseudo-MR images and lobe segmentations, and applied to 1000 masked ufSSFP images to predict lobe segmentations. These outputs were directly used as targets to train Network-2 and Network-3 with non-masked ufSSFP data as inputs, and an additional whole-lung mask as input for Network-2. Network predictions were compared to reference manual lobe segmentations of ufSSFP data in twenty pediatric cystic fibrosis patients. Manual lobe segmentations were performed by splitting available whole-lung segmentations into lobes. Results: Network-1 was able to segment the lobes of ufSSFP images, and Network-2 and Network-3 further increased segmentation accuracy and robustness. The average all-lobe Dice similarity coefficients were 95.0 2.8 (mean pooled SD [%]), 96.4 2.0, 93.0 2.0, and the average median Hausdorff distances were 6.1 0.9 (mean SD [mm]), 5.3 1.1, 7.1 1.3, for Network-1, Network-2, and Network-3, respectively. Conclusions: RNN lung lobe segmentation of 2D ufSSFP imaging is feasible, in good agreement with manual segmentations. The proposed workflow might provide access to automated lobe segmentations for various lung MRI examinations and quantitative analyses.
A note on the modeling of the effects of experimental time in psycholinguistic experiments
2022-02-22

Thul et al. (2020) called attention to problems that arise when chronometric experiments implementing specific factorial designs are analysed with the generalized additive mixed model (GAMM), using factor smooths to capture trial-to-trial dependencies. From a series of simulations incorporating such dependencies, they conclude that GAMMs are inappropriate for between-subject designs. They argue that in addition GAMMs come with too many modeling possibilities, and advise using the linear mixed model (LMM) instead. We address the questions raised by Thul et al. (2020), who clearly demonstrated that problems can indeed arise when using factor smooths in combination with factorial designs. We show that the problem does not arise when using by-smooths. Furthermore, we have traced a bug in the implementation of factor smooths in the mgcv package, which will have been removed from version 1.8-36 onwards. To illustrate that GAMMs now produce correct estimates, we report simulation studies implementing different by-subject longitudinal effects. The maximal LMM emerges as slightly conservative compared to GAMMs, and GAMMs provide estimated coefficients that can be less variable across simulation runs. We also discuss two datasets where time-varying effects interact with numerical predictors in a theoretically informative way. Furthermore, we argue that the wide range of tools that GAMMs make available to researcher across all domains of scientific inquiry do not come with uncontrolled researcher degrees of freedom once confronted with a specific psycholinguistic datasets. We also introduce a distinction between replicable and non-replicable non-linear effects. We conclude that GAMMs are an excellent and reliable tool for understanding experimental data, including chronometric data with time-varying effects.
Origin of englacial stratigraphy at three deep ice core sites of the Greenland Ice Sheet by synthetic radar modelling
2022-02-23

Abstract During the past 20 years, multi-channel radar emerged as a key tool for deciphering an ice sheet's internal architecture. To assign ages to radar reflections and connect them over large areas in the ice sheet, the layer genesis has to be understood on a microphysical scale. Synthetic radar trace modelling based on the dielectric profile of ice cores allows for the assignation of observed physical properties’ variations on the decimetre scale to radar reflectors extending from the coring site to a regional or even whole-ice-sheet scale. In this paper we rely on the available dielectric profiling data of the northern Greenland deep ice cores: NGRIP, NEEM and EGRIP. The three records are well suited for assigning an age model to the stratigraphic radar-mapped layers, and linking up the reflector properties to observations in the cores. Our modelling results show that the internal reflections are mainly due to conductivity changes. Furthermore, we deduce fabric characteristics at the EGRIP drill site from two-way-travel-time differences of along and across-flow polarized radarwave reflections of selected horizons (below 980 m). These indicate in deeper parts of the ice column an across-flow concentrated c -axis fabric.
Motor skill learning decreases movement variability and increases planning horizon
2022-02-23

We show that when learning a motor skill humans are using information about the environment from an increasingly longer amount of the movement path ahead to improve performance. Crucial features of the behavioral performance can be captured by modeling the behavioral data with a receding horizon optimal control model.
7 tricks for 7 T CEST: Improving the reproducibility of multipool evaluation provides insights into the effects of age and the early stages of Parkinson's disease
2022-02-23

The objective of the current study was to optimize the postprocessing pipeline of 7 T chemical exchange saturation transfer (CEST) imaging for reproducibility and to prove this optimization for the detection of age differences and differences between patients with Parkinson's disease versus normal subjects. The following 7 T CEST MRI experiments were analyzed: repeated measurements of a healthy subject, subjects of two age cohorts (14 older, seven younger subjects), and measurements of 12 patients with Parkinson's disease. A slab‐selective, ‐homogeneous parallel transmit protocol was used. The postprocessing, consisting of motion correction, smoothing, ‐correction, normalization, denoising, ‐correction and Lorentzian fitting, was optimized regarding the intrasubject and intersubject coefficient of variation (CoV) of the amplitudes of the amide pool and the aliphatic relayed nuclear Overhauser effect (rNOE) pool within the brain. Seven “tricks” for postprocessing accomplished an improvement of the mean voxel CoV of the amide pool and the aliphatic rNOE pool amplitudes of less than 5% and 3%, respectively. These postprocessing steps are: motion correction with interpolation of the motion of low‐signal offsets (1) using the amide pool frequency offset image as reference (2), normalization of the Z‐spectrum using the outermost saturated measurements (3), correction of the Z‐spectrum with moderate spline smoothing (4), denoising using principal component analysis preserving the 11 highest intensity components (5), correction using a linear fit (6) and Lorentzian fitting using the five‐pool fit model (7). With the optimized postprocessing pipeline, a significant age effect in the amide pool can be detected. Additionally, for the first time, an aliphatic rNOE contrast between subjects with Parkinson's disease and age‐matched healthy controls in the substantia nigra is detected. We propose an optimized postprocessing pipeline for CEST multipool evaluation. It is shown that by the use of these seven “tricks”, the reproducibility and, thus, the statistical power of a CEST measurement, can be greatly improved and subtle changes can be detected.
STAT1 and STAT3 Exhibit a Crosstalk and Are Associated with Increased Inflammation in Hepatocellular Carcinoma
2022-02-23

Liver cancers, which are mostly hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), are very aggressive tumors with poor prognosis. Therapeutic options with curative intent are largely limited to surgery and available systemic therapies show limited benefit. Signal transducer and activator of transcription 1 (STAT1) and 3 (STAT3) are key transcription factors activated by pro-inflammatory cytokines such as interferon-γ (IFN-γ) and interleukin-6 (IL-6). In this study, we combined in vitro cell culture experiments and immunohistochemical analyses of human HCC (N = 124) and CCA (N = 138) specimens. We observed that in the absence of STAT3, IL-6 induced the activation of STAT1 and its target genes suggesting that IL-6 derived from the tumor microenvironment may activate both STAT1 and STAT3 target genes in HCC tumor cells. In addition, STAT1 and STAT3 were highly activated in a subset of HCC, which exhibited a high degree of infiltrating CD8- and FOXP3-positive immune cells and PD-L1 expression. Our results demonstrate that STAT1 and STAT3 are expressed and activated in HCC and tumor infiltrating immune cells. In addition, HCC cases with high STAT1 and STAT3 expression also exhibited a high degree of immune cell infiltration, suggesting increased immunological tolerance.
Secondary malignant neoplasms after bone and soft tissue sarcomas in children, adolescents, and young adults
2022-02-23

Background Increased survival in young sarcoma patients comes along with a higher incidence of second malignant neoplasms (SMNs). The incidence, latency, histiotype, and outcome of these patients were analyzed because this information is essential to design evidence‐based long‐term follow‐up care programs for young sarcoma survivors. Methods Patients entered on clinical trials or registered in registries with a primary sarcoma in 1 of the cooperative sarcoma study groups in the framework of the Society for Pediatric Oncology and Hematology (GPOH) were screened for SMNs. Descriptive analysis, the Kaplan‐Meier method, the Gray model, the Fine‐Gray model, and the Cox regression model were used for the statistical analyses. Results A total of 159 out of 7079 (2.2%) patients were registered with a SMN. Among them, 104 solid SMNs (65%) and 56 hematologic SMNs (35%) occurred. Median latency from first diagnosis of sarcoma to the diagnosis of SMN was 6.8 years (range, 0‐26.7 years). Cumulative incidence of SMN was 8.8% after 30 years. Five‐year‐survival was 67.1% (95% confidence interval [CI], 66.0‐68.2) for the 7079 patients and it was 45.1% (95% CI, 36.2‐53.6) after the diagnosis of a SMN (subcohort of n = 159 patients). Conclusions There is a remarkable high cumulative incidence of SMNs after bone and soft tissue sarcomas in children, adolescents, and young adults. Therefore, effective transition as well as risk adapted long‐term follow‐up care programs should be developed and offered to young sarcoma survivors. Lay Summary Bone sarcomas and soft tissue tumors are rare tumors in children, adolescents, and young adults. The treatment varies, but may comprise chemotherapy, surgery, and/or radiotherapy. Developing a subsequent malignant tumor is a long‐term risk for the patients. To better characterize this risk, we analyzed the data of 7079 patients (up to 21 years old) with bone sarcomas or soft tissue tumors. Our findings provide a basis to counsel young sarcoma survivors on their individual risk of subsequent malignant tumors. Moreover, these data can help to establish recommendations for aftercare in young sarcoma survivors.