A Curious Case of Remarkable Resilience to Gradient Attacks via Fully Convolutional and Differentiable Front End with a Skip Connection

We tested front-end enhanced neural models where a frozen classifier was prepended by a differentiable and fully convolutional model with a skip connection. By training them using a small learning rate for about one epoch, we obtained models that retained the accuracy of the backbone classifier while being unusually resistant to gradient attacks including APGD and FAB-T attacks from the AutoAttack package, which we attributed to gradient masking. The gradient masking phenomenon is not new, but the degree of masking was quite remarkable for fully differentiable models that did not have gradient-shattering components such as JPEG compression or components that are expected to cause diminishing gradients. Though black box attacks can be partially effective against gradient masking, they are easily defeated by combining models into randomized ensembles. We estimate that such ensembles achieve near-SOTA AutoAttack accuracy on CIFAR10, CIFAR100, and ImageNet despite having virtually zero accuracy under adaptive attacks. Adversarial training of the backbone classifier can further increase resistance of the front-end enhanced model to gradient attacks. On CIFAR10, the respective randomized ensemble achieved 90.8$\pm 2.5$% (99% CI) accuracy under AutoAttack while having only 18.2$\pm 3.6$% accuracy under the adaptive attack. We do not establish SOTA in adversarial robustness. Instead, we make methodological contributions and further supports the thesis that adaptive attacks designed with the complete knowledge of model architecture are crucial in demonstrating model robustness and that even the so-called white-box gradient attacks can have limited applicability. Although gradient attacks can be complemented with black-box attack such as the SQUARE attack or the zero-order PGD, black-box attacks can be weak against randomized ensembles, e.g., when ensemble models mask gradients

Osteocrinology: Insights from the Great Indian Epics

Indian epics are a storehouse of knowledge and information, which offer an insight into various aspects of health and disease. In this paper, we surmise some of the legendary figures in the great Indian epics, who possibly could have disorders related to osteocrinology. Based on the detailed description provided in Vedic texts, these exemplars from Indian history provide an interesting framework for the study of osteocrinology. These may spark an interest in students and researchers to explore and understand this subject in greater depth

Meaningful Clinical Conversation: Guidance from the Gita

Chronic disease care is a challenging vocation. One of the reasons for this is the need to inform and share decision making with patients. Communication and conversational skills are the pillars of chronic care delivery. In this editorial, we take guidance from the Srimad Bhagavad Gita in order to improve the quality of clinical conversations, and make them meaningful. The Gita encourages us to be ‘saatvik’ or balanced in thought, words and deeds, to perform ‘penance’ of mind, speech and body, and to accept equanimity. This introspective opinion piece should help us polish our communication skills, and improve interaction with our patients

Distributionally Robust Classification on a Data Budget

Real world uses of deep learning require predictable model behavior under distribution shifts. Models such as CLIP show emergent natural distributional robustness comparable to humans, but may require hundreds of millions of training samples. Can we train robust learners in a domain where data is limited? To rigorously address this question, we introduce JANuS (Joint Annotations and Names Set), a collection of four new training datasets with images, labels, and corresponding captions, and perform a series of carefully controlled investigations of factors contributing to robustness in image classification, then compare those results to findings derived from a large-scale meta-analysis. Using this approach, we show that standard ResNet-50 trained with the cross-entropy loss on 2.4 million image samples can attain comparable robustness to a CLIP ResNet-50 trained on 400 million samples. To our knowledge, this is the first result showing (near) state-of-the-art distributional robustness on limited data budgets. Our dataset is available at \url{https://huggingface.co/datasets/penfever/JANuS_dataset}, and the code used to reproduce our experiments can be found at \url{https://github.com/penfever/vlhub/}.Comment: TMLR 2023; openreview link: https://openreview.net/forum?id=D5Z2E8CNs

The Spirit and Science: Is the Endocrine System the Essence of Existence?

In this reflective opinion piece, the authors offer a unique insight into the connection between spirituality and science. A reading of the Shrimad Bhagvad Gita, through the eyes of an endocrinologist, uncovers unexpected corollaries and correlations between spirituality or religion on one hand, and science or rationale, on the other

A review of current and future powertrain technologies and trends in 2020

[EN] Fossil fuels are currently the most convenient on-board energy sources for vehicles in terms of energy density and refueling time. However, the increase in global temperature together with the increase in transported people and goods in recent years has forced regulatory authorities around the world to establish strict regulations on pollutant and CO2 emissions. These scenarios are challenging for vehicle manufacturers, but they also create opportunities for the development of new technologies and concepts. For example, automotive companies and researchers are currently exploring hybrid powertrains with either advanced internal combustion engine technologies and low levels of electrification, or with high levels of electrification combined with simpler internal combustion engines. While these hybridization approaches can provide significant improvements in efficiency and emissions. There is also a global movement at the, consumer, manufacturing and government level to accelerate the adoption of zero tailpipe emitting vehicles (e.g., battery electric vehicles and fuel cell electric vehicles). This paper reviews the current state of powertrain technologies, analyzing first the evolution of emissions regulations in major markets and emphasizing the future tighter measures that will be adopted in Europe and the US. After that, an analysis of current global vehicle sales considering the COVID situation is performed, followed by a forecast of future powertrain technology market share trends. Finally, reviews of internal combustion engine, hybrid, and battery electric vehicle technologies announced in 2020 are carried out.Conway, G.; Joshi, A.; Leach, F.; García Martínez, A.; Senecal, PK. (2021). A review of current and future powertrain technologies and trends in 2020. Transportation Engineering. 5:1-15. https://doi.org/10.1016/j.treng.2021.100080115

Prevalence of diabetes distress and its psychosocial determinants among Indian population with type II diabetes

Background: Diabetes distress (DD) refers to the negative emotional or affective experience resulting from the challenge of living with the demands of diabetes, regardless of the type of diabetes. In addition to the chronic treatment of diabetes, patients with type 2 diabetes mellitus (T2DM) often experience psychosocial difficulties which can go unnoticed. Hence, it is necessary to identify DD at an early stage to prevent its effect on the patients’ long-term self-care and management plan. This study was conducted to assess the prevalence of DD and its psychosocial determinants among T2DM at a tertiary care centre. Methods: This was a cross sectional, observational study which included patients of either gender, who were between 18-65 years of age with T2DM for more than 3 months to 12 years. DD was assessed using the diabetes distress scale (DDS17) scale. In addition, association between the level of DD with the sociodemographic and clinical characteristics of the patients was assessed. Results: The prevalence of DD in type II diabetic patients in suburban population was found to be 17.69%. The psychosocial determinants which influence DD were found to be age, treatment modality, hypothyroidism, hypertension, and smoking. Conclusions: This study signifies the importance of identifying DD by the primary care physician which often remain unrecognized in clinical practice and to implement the interventions at early stages to improve the quality of life of diabetic patients