Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial success in closing the gap between human and machine vision

A few years ago, the first CNN surpassed human performance on ImageNet. However, it soon became clear that machines lack robustness on more challenging test cases, a major obstacle towards deploying machines"in the wild"and towards obtaining better computational models of human visual perception. Here we ask: Are we making progress in closing the gap between human and machine vision? To answer this question, we tested human observers on a broad range of out-of-distribution (OOD) datasets, recording 85,120 psychophysical trials across 90 participants. We then investigated a range of promising machine learning developments that crucially deviate from standard supervised CNNs along three axes: objective function (self-supervised, adversarially trained, CLIP language-image training), architecture (e.g. vision transformers), and dataset size (ranging from 1M to 1B). Our findings are threefold. (1.) The longstanding distortion robustness gap between humans and CNNs is closing, with the best models now exceeding human feedforward performance on most of the investigated OOD datasets. (2.) There is still a substantial image-level consistency gap, meaning that humans make different errors than models. In contrast, most models systematically agree in their categorisation errors, even substantially different ones like contrastive self-supervised vs. standard supervised models. (3.) In many cases, human-to-model consistency improves when training dataset size is increased by one to three orders of magnitude. Our results give reason for cautious optimism: While there is still much room for improvement, the behavioural difference between human and machine vision is narrowing. In order to measure future progress, 17 OOD datasets with image-level human behavioural data and evaluation code are provided as a toolbox and benchmark at: https://github.com/bethgelab/model-vs-human/
What can linearized neural networks actually say about generalization?

For certain infinitely-wide neural networks, the neural tangent kernel (NTK) theory fully characterizes generalization, but for the networks used in practice, the empirical NTK only provides a rough first-order approximation. Still, a growing body of work keeps leveraging this approximation to successfully analyze important deep learning phenomena and design algorithms for new applications. In our work, we provide strong empirical evidence to determine the practical validity of such approximation by conducting a systematic comparison of the behavior of different neural networks and their linear approximations on different tasks. We show that the linear approximations can indeed rank the learning complexity of certain tasks for neural networks, even when they achieve very different performances. However, in contrast to what was previously reported, we discover that neural networks do not always perform better than their kernel approximations, and reveal that the performance gap heavily depends on architecture, dataset size and training task. We discover that networks overfit to these tasks mostly due to the evolution of their kernel during training, thus, revealing a new type of implicit bias.
Biomedical Entity Linking with Contrastive Context Matching

We introduce BioCoM, a contrastive learning framework for biomedical entity linking that uses only two resources: a small-sized dictionary and a large number of raw biomedical articles. Specifically, we build the training instances from raw PubMed articles by dictionary matching and use them to train a context-aware entity linking model with contrastive learning. We predict the normalized biomedical entity at inference time through a nearest-neighbor search. Results found that BioCoM substantially outperforms state-of-the-art models, especially in low-resource settings, by effectively using the context of the entities.
On a class of divergence form linear parabolic equations with degenerate coefficients

We study a class of linear parabolic equations in divergence form with degenerate coefficients on the upper half space. Specifically, the equations are considered in (−∞, T ) × R+, where R d + = {x ∈ R d : xd > 0} and T ∈ (−∞,∞] is given, and the diffusion matrices are the product of xd and bounded uniformly elliptic matrices, which are degenerate at {xd = 0}. As such, our class of equations resembles well the corresponding class of degenerate viscous Hamilton-Jacobi equations. We obtain wellposedness results and regularity type estimates in some appropriate weighted Sobolev spaces for the solutions.
On the Representation of Solutions to Elliptic PDEs in Barron Spaces

Numerical solutions to high-dimensional partial differential equations (PDEs) based on neural networks have seen exciting developments. This paper derives complexity estimates of the solutions of d-dimensional second-order elliptic PDEs in the Barron space, that is a set of functions admitting the integral of certain parametric ridge function against a probability measure on the parameters. We prove under some appropriate assumptions that if the coefficients and the source term of the elliptic PDE lie in Barron spaces, then the solution of the PDE is -close with respect to the H norm to a Barron function. Moreover, we prove dimensionexplicit bounds for the Barron norm of this approximate solution, depending at most polynomially on the dimension d of the PDE. As a direct consequence of the complexity estimates, the solution of the PDE can be approximated on any bounded domain by a two-layer neural network with respect to the H norm with a dimension-explicit convergence rate.
Non Gaussian Denoising Diffusion Models

Generative diffusion processes are an emerging and effective tool for image and speech generation. In the existing methods, the underline noise distribution of the diffusion process is Gaussian noise. However, fitting distributions with more degrees of freedom, could help the performance of such generative models. In this work, we investigate other types of noise distribution for the diffusion process. Specifically, we show that noise from Gamma distribution provides improved results for image and speech generation. Moreover, we show that using a mixture of Gaussian noise variables in the diffusion process improves the performance over a diffusion process that is based on a single distribution. Our approach preserves the ability to efficiently sample state in the training diffusion process while using Gamma noise and a mixture of noise.
Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective

In this paper we analyze the graph-based approach to semi-supervised learning under a manifold assumption. We adopt a Bayesian perspective and demonstrate that, for a suitable choice of prior constructed with sufficiently many unlabeled data, the posterior contracts around the truth at a rate that is minimax optimal up to a logarithmic factor. Our theory covers both regression and classification.
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving

Feedforward computation, such as evaluating a neural network or sampling from an autoregressive model, is ubiquitous in machine learning. The sequential nature of feedforward computation, however, requires a strict order of execution and cannot be easily accelerated with parallel computing. To enable parallelization, we frame the task of feedforward computation as solving a system of nonlinear equations. We then propose to find the solution using a Jacobi or Gauss-Seidel fixed-point iteration method, as well as hybrid methods of both. Crucially, Jacobi updates operate independently on each equation and can be executed in parallel. Our method is guaranteed to give exactly the same values as the original feedforward computation with a reduced (or equal) number of parallelizable iterations, and hence reduced time given sufficient parallel computing power. Experimentally, we demonstrate the effectiveness of our approach in accelerating (i) backpropagation of RNNs, (ii) evaluation of DenseNets, and (iii) autoregressive sampling of MADE and PixelCNN++, with speedup factors between 2.1 and 26 under various settings.
Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack

Automated Lane Centering (ALC) systems are convenient and widely deployed today, but also highly security and safety critical. In this work, we are the first to systematically study the security of state-of-the-art deep learning based ALC systems in their designed operational domains under physical-world adversarial attacks. We formulate the problem with a safety-critical attack goal, and a novel and domain-specific attack vector: dirty road patches. To systematically generate the attack, we adopt an optimization-based approach and overcome domain-specific design challenges such as camera frame inter-dependencies due to attack-influenced vehicle control, and the lack of objective function design for lane detection models. We evaluate our attack on a production ALC using 80 scenarios from real-world driving traces. The results show that our attack is highly effective with over 97.5% success rates and less than 0.903 sec average success time, which is substantially lower than the average driver reaction time. This attack is also found (1) robust to various real-world factors such as lighting conditions and view angles, (2) general to different model designs, and (3) stealthy from the driver's view. To understand the safety impacts, we conduct experiments using software-in-the-loop simulation and attack trace injection in a real vehicle. The results show that our attack can cause a 100% collision rate in different scenarios, including when tested with common safety features such as automatic emergency braking. We also evaluate and discuss defenses.
MAPKs Are Highly Abundant but Do Not Contribute to α1-Adrenergic Contraction of Rat Saphenous Arteries in the Early Postnatal Period

Previously, the abundance of p42/44 and p38 MAPK proteins had been shown to be higher in arteries of 1- to 2-week-old compared to 2- to 3-month-old rats. However, the role of MAPKs in vascular tone regulation in early ontogenesis remains largely unexplored. We tested the hypothesis that the contribution of p42/44 and p38 MAPKs to the contraction of peripheral arteries is higher in the early postnatal period compared to adulthood. Saphenous arteries of 1- to 2-week-old and 2- to 3-month-old rats were studied using wire myography and western blotting. The α1-adrenoceptor agonist methoxamine did not increase the phosphorylation level of p38 MAPK in either 1- to 2-week-old or 2- to 3-month-old rats. Accordingly, inhibition of p38 MAPK did not affect arterial contraction to methoxamine in either age group. Methoxamine increased the phosphorylation level of p42/44 MAPKs in arteries of 2- to 3-month-old and of p44 MAPK in 1- to 2-week-old rats. Inhibition of p42/44 MAPKs reduced methoxamine-induced contractions in arteries of 2- to 3-month-old, but not 1- to 2-week-old rats. Thus, despite a high abundance in arterial tissue, p38 and p42/44 MAPKs do not regulate contraction of the saphenous artery in the early postnatal period. However, p42/44 MAPK activity contributes to arterial contractions in adult rats.
A Velostat-Based Pressure-Sensitive Mat for Center-of-Pressure Measurements: A Preliminary Study

Center-of-pressure (CoP) displacements play a key role in studies assessing postural stability. The accepted instrument to measure CoP trajectories is the force platform, but pressure-sensitive mats (PSMs) are an alternative composed of a matrix of sensitive cells. A typical cell comprises two electrodes with piezoresistive material in between, while a force platform has a force sensor at each of its corners. In this paper, we compare a homemade Velostat-based PSM and an affordable commercial mat with a commercial force platform in a test series with 42 healthy volunteers in single-legged trials (29 males, 13 females; height 1.74 (0.09) m, weight 74.3 (16.34) kg, age 31.21 (12.66) years). The aim of the research was to perform a preliminary study of the performance of our prototype to measure CoP, and more specifically, the standard deviation of the CoP path on both axes, the medial–lateral and anterior–posterior. We could thus discover several improvements for future clinical applications. The intraclass correlation coefficient (ICC) for agreement in the base experiment showed a moderate value for the prototype (0.38 to 0.63) and lower values for the commercial mat (0.11 to 0.12). However, we identified several factors that were relevant to improve ICC and reduce error by considering several processing options: (i) the known crosstalk problem between cells that appears in this kind of mats must be eliminated; (ii) the response time of the sensor has to be taken into account; and (iii) increasing the mat resolution also improves agreement. Therefore, as future work, we plan to test the improved version of the prototype in a clinical environment.
ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection †

Diabetic retinopathy, an eye disease commonly afflicting diabetic patients, can result in loss of vision if prompt detection and treatment are not done in the early stages. Once the symptoms are identified, the severity level of the disease needs to be classified for prescribing the right medicine. This study proposes a deep learning-based approach, for the classification and grading of diabetic retinopathy images. The proposed approach uses the feature map of ResNet-50 and passes it to Random Forest for classification. The proposed approach is compared with five state-of-the-art approaches using two category Messidor-2 and five category EyePACS datasets. These two categories on the Messidor-2 dataset include ’No Referable Diabetic Macular Edema Grade (DME)’ and ’Referable DME’ while five categories consist of ‘Proliferative diabetic retinopathy’, ‘Severe’, ‘Moderate’, ‘Mild’, and ‘No diabetic retinopathy’. The results show that the proposed approach outperforms compared approaches and achieves an accuracy of 96% and 75.09% for these datasets, respectively. The proposed approach outperforms six existing state-of-the-art architectures, namely ResNet-50, VGG-19, Inception-v3, MobileNet, Xception, and VGG16.
“It Was Definitely an Eye-Opener to Me”—People with Disabilities’ and Health Professionals’ Perceptions on Combining Traditional Indoor Rehabilitation Practice with an Urban Green Rehabilitation Context

Research points to the health benefits of rehabilitation in urban green spaces. Nevertheless, more studies indicate complexity of utilising urban green spaces in an established health system context. An understanding of challenges related to rehabilitation in urban green spaces remains unaddressed. Therefore, the aim was to describe and analyse people with disabilities’ and health professionals’ perceptions on combining traditional indoor rehabilitation practice with an urban green rehabilitation context. The interpretive description methodology was applied supplemented by Edgar Schein’s Model of Organisational Culture. Three online focus group interviews were conducted with people with disabilities (n = 4) and health professionals (n = 10). Three interrelated themes formed an understanding of rehabilitation practice in an urban green rehabilitation context: “ambivalence due to contextual change”, “negotiating rehabilitation assumptions” and “expanding the frame of rehabilitation”. Expanding the frame of rehabilitation to an urban green context may provide a basis for enhancing compatibility to everyday life for people with disabilities and still accommodate structural quality standards of professional rehabilitation practice.
Glycemic Control for Colorectal Cancer Survivors Compared to Those without Cancer in the Dutch Primary Care for Type 2 Diabetes: A Prospective Cohort Study

Simple Summary A growing number of colorectal cancer survivors live with type 2 diabetes, as a result of improved cancer diagnosis and treatment. These patients might have worse glycemic control after their cancer diagnosis, which may increase the risk of cardiovascular diseases. This prospective cohort study evaluated the quality of glycemic control for colorectal cancer survivors, as compared to those without cancer in Dutch primary care for diabetes. During a 10-year follow-up for 57,330 patients, there were 705 patients diagnosed with colorectal cancer. No clinically relevant difference on the probability of reaching the target HbA1c was observed between colorectal cancer survivors and patients with no history of cancer. These results showed a robust diabetes care system, implying that the glycemic control for colorectal cancer survivors can be delegated to the primary care professionals. Abstract Cancer survivors with diabetes tend to have worse glycemic control after their cancer diagnosis, which may increase the risk of cardiovascular diseases. We aimed to investigate whether glycemic control differs between colorectal cancer (CRC) survivors and those without cancer, among patients with type 2 diabetes being treated in the Dutch primary care. The Zwolle Outpatient Diabetes project Integrating Available Care database was linked with the Dutch Cancer Registry (n = 71,648, 1998–2014). The cases were those with stage 0–III CRC, and the controls were those without cancer history. The primary and secondary outcomes were the probability of reaching the glycated hemoglobin (HbA1c) target and the mean of HbA1c during follow-up, respectively. Mixed linear modeling was applied, where the status of CRC was a time-varying variable. Among the 57,330 patients included, 705 developed CRC during follow-up. The mean probability of reaching the HbA1c target during follow-up was 73% versus 74% (p = 0.157) for CRC survivors versus those without cancer, respectively. The mean HbA1c was 51.1 versus 50.8 mmol/mol (p = 0.045) among CRC survivors versus those without cancer, respectively. We observed a clinically comparable glycemic control among the CRC survivors without cancer, indicating that glycemic control for CRC survivors can be delegated to primary care professionals.
Virtual Reality vs. Tablet Video as an Experiential Education Platform for Pediatric Patients Undergoing Chest Radiography: A Randomized Clinical Trial

Virtual reality (VR), which offers an immersive experience, has been implemented into the education of pediatric patients to reduce peri-procedural anxiety. This randomized clinical trial evaluated the effect of VR, compared with standard video, on reducing anxiety and distress in pediatric patients undergoing chest radiography. A total of 120 children aged 4 to 8 years with scheduled chest radiography appointments were randomized into either the tablet or the VR group. Children in the tablet group experienced chest radiography indirectly with a 3 min tablet video, whereas those in the VR group received the same content via a VR experience. The distress of children was measured using the Observational Scale of Behavioral Distress (OSBD) scale. Parental presence and procedural outcomes were also recorded. The number of less distressed children (OSBD score < 5) was significantly higher in the VR group than in the tablet group (49 [81.7%]) vs. 32 [53.3%]) (p = 0.001). The OSBD scores, the need for parental presence, the procedure time, and the number of repeated procedures were all lower in the VR group. The immersive VR experience appears to decrease the degree of anxiety in children and increase the efficiency of the procedures compared with the tablet video with the same content.
Laying the Foundation for Pregnancy Physical Activity Profiling: A Framework for Providing Tailored Physical Activity Advice and Guidance to Pregnant Women

The aim of this study was to examine the predictive utility of the theory of planned behaviour (TPB) in explaining pregnant women’s physical activity (PA) intentions and behaviour and to scrutinise the role of past behaviour within this context. Pregnant women (n = 89) completed the pregnancy physical activity questionnaire (PPAQ) and newly developed TPB questionnaire on two separate occasions during their pregnancy. Analyses were carried out in relation to three scenarios. Firstly, when considering the original TPB, intention emerged as the strongest determinant of pregnant women’s PA behaviour. Secondly, controlling for past behaviour attenuated the influence of intention and perceived behavioural control on behaviour, with neither of the original variables providing a unique influence. Finally, the addition of past behaviour added significantly to the prediction of intention with the model as a whole, explaining 85% of the variance in pregnant women’s PA intention, and with past behaviour uniquely contributing 44.8% of the variance. Pregnancy physical activity profiling based on intention and behaviour status is subsequently introduced as a novel and practical framework. This provides healthcare professionals with the opportunity and structure to provide tailored advice and guidance to pregnant women, thereby facilitating engagement with PA throughout motherhood.
Peptides Derived from Growth Factors to Treat Alzheimer’s Disease

Alzheimer’s disease (AD) is a devastating neurodegenerative disease characterized by progressive neuron losses in memory-related brain structures. The classical features of AD are a dysregulation of the cholinergic system, the accumulation of amyloid plaques, and neurofibrillary tangles. Unfortunately, current treatments are unable to cure or even delay the progression of the disease. Therefore, new therapeutic strategies have emerged, such as the exogenous administration of neurotrophic factors (e.g., NGF and BDNF) that are deficient or dysregulated in AD. However, their low capacity to cross the blood–brain barrier and their exorbitant cost currently limit their use. To overcome these limitations, short peptides mimicking the binding receptor sites of these growth factors have been developed. Such peptides can target selective signaling pathways involved in neuron survival, differentiation, and/or maintenance. This review focuses on growth factors and their derived peptides as potential treatment for AD. It describes (1) the physiological functions of growth factors in the brain, their neuronal signaling pathways, and alteration in AD; (2) the strategies to develop peptides derived from growth factor and their capacity to mimic the role of native proteins; and (3) new advancements and potential in using these molecules as therapeutic treatments for AD, as well as their limitations.