Interface-dominated plasticity and kink bands in metallic nanolaminates

The theoretical and computational framework of finite deformation mesoscale field dislocation mechanics (MFDM) is used to understand the salient aspects of kink-band formation in Cu-Nb nano-metallic laminates (NMLs). A conceptually minimal, plane-strain idealization of the three-dimensional geometry, including crystalline orientation, of additively manufactured NML is used to model NMLs. Importantly, the natural jump/interface condition of MFDM imposing continuity of (certain components) of plastic strain rates across interfaces allows theory-driven `communication' of plastic flow across the laminate boundaries in our finite element implementation. Kink bands under layer parallel compression of NMLs in accord with experimental observations arise in our numerical simulations. The possible mechanisms for the formation and orientation of kink bands are discussed, within the scope of our idealized framework. We also report results corresponding to various parametric studies that provide preliminary insights and clear questions for future work on understanding the intricate underlying mechanisms for the formation of kink bands.Comment: Keywords: mesoscale plasticity, kink bands, nanometallic laminates, strain gradient plasticit

LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models

Linking information across sources is fundamental to a variety of analyses in social science, business, and government. While large language models (LLMs) offer enormous promise for improving record linkage in noisy datasets, in many domains approximate string matching packages in popular softwares such as R and Stata remain predominant. These packages have clean, simple interfaces and can be easily extended to a diversity of languages. Our open-source package LinkTransformer aims to extend the familiarity and ease-of-use of popular string matching methods to deep learning. It is a general purpose package for record linkage with transformer LLMs that treats record linkage as a text retrieval problem. At its core is an off-the-shelf toolkit for applying transformer models to record linkage with four lines of code. LinkTransformer contains a rich repository of pre-trained transformer semantic similarity models for multiple languages and supports easy integration of any transformer language model from Hugging Face or OpenAI. It supports standard functionality such as blocking and linking on multiple noisy fields. LinkTransformer APIs also perform other common text data processing tasks, e.g., aggregation, noisy de-duplication, and translation-free cross-lingual linkage. Importantly, LinkTransformer also contains comprehensive tools for efficient model tuning, to facilitate different levels of customization when off-the-shelf models do not provide the required accuracy. Finally, to promote reusability, reproducibility, and extensibility, LinkTransformer makes it easy for users to contribute their custom-trained models to its model hub. By combining transformer language models with intuitive APIs that will be familiar to many users of popular string matching packages, LinkTransformer aims to democratize the benefits of LLMs among those who may be less familiar with deep learning frameworks

Understanding Gradient Descent on Edge of Stability in Deep Learning

Deep learning experiments by Cohen et al. [2021] using deterministic Gradient Descent (GD) revealed an Edge of Stability (EoS) phase when learning rate (LR) and sharpness (i.e., the largest eigenvalue of Hessian) no longer behave as in traditional optimization. Sharpness stabilizes around $2/$LR and loss goes up and down across iterations, yet still with an overall downward trend. The current paper mathematically analyzes a new mechanism of implicit regularization in the EoS phase, whereby GD updates due to non-smooth loss landscape turn out to evolve along some deterministic flow on the manifold of minimum loss. This is in contrast to many previous results about implicit bias either relying on infinitesimal updates or noise in gradient. Formally, for any smooth function $L$ with certain regularity condition, this effect is demonstrated for (1) Normalized GD, i.e., GD with a varying LR $\eta_t =\frac{\eta}{\| \nabla L(x(t)) \|}$ and loss $L$; (2) GD with constant LR and loss $\sqrt{L- \min_x L(x)}$. Both provably enter the Edge of Stability, with the associated flow on the manifold minimizing $\lambda_{1}(\nabla^2 L)$. The above theoretical results have been corroborated by an experimental study.Comment: 63 pages. This paper has been accepted for conference proceedings in the 39th International Conference on Machine Learning (ICML), 202

Do Transformers Parse while Predicting the Masked Word?

Pre-trained language models have been shown to encode linguistic structures, e.g. dependency and constituency parse trees, in their embeddings while being trained on unsupervised loss functions like masked language modeling. Some doubts have been raised whether the models actually are doing parsing or only some computation weakly correlated with it. We study questions: (a) Is it possible to explicitly describe transformers with realistic embedding dimension, number of heads, etc. that are capable of doing parsing -- or even approximate parsing? (b) Why do pre-trained models capture parsing structure? This paper takes a step toward answering these questions in the context of generative modeling with PCFGs. We show that masked language models like BERT or RoBERTa of moderate sizes can approximately execute the Inside-Outside algorithm for the English PCFG [Marcus et al, 1993]. We also show that the Inside-Outside algorithm is optimal for masked language modeling loss on the PCFG-generated data. We also give a construction of transformers with $50$ layers, $15$ attention heads, and $1275$ dimensional embeddings in average such that using its embeddings it is possible to do constituency parsing with $>70\%$ F1 score on PTB dataset. We conduct probing experiments on models pre-trained on PCFG-generated data to show that this not only allows recovery of approximate parse tree, but also recovers marginal span probabilities computed by the Inside-Outside algorithm, which suggests an implicit bias of masked language modeling towards this algorithm

To study the association of high sensitivity C-reactive protein with metabolic syndrome

Background: Metabolic Syndrome is a constellation of dyslipidemia (elevated triglycerides, low high-density lipoproteins (HDL)), elevation of arterial blood pressure (BP), dysregulated glucose homeostasis, and increased abdominal obesity.Methods: We studied the association of high sensitivity C-reactive protein with metabolic syndrome by case-control method in our tertiary care hospital in West U.P.Results: The mean age of cases and controls was 52.6 ± 7.7 and 51.4±7.0 years, respectively. There were 25 (50%) male and 25 (50%) female in case groups, and 27 (54%) males and 23 (46%) females in control group. Our analysis revelaed that there was a significant association between hs-CRP and the central obesity when compared in case-control group (3.57 vs 0.96 mg/L) (p value <0.001). There was no significant association between hs-CRP and high triglycerides, hypertension, diabetes, and reduced high density lipoprotein cholesterol.Conclusions: Raised hsCRP level can be considered as a surrogate marker of chronic inflammation in patients with metabolic syndrome

Alcoholism with central pontine demyelination: a case report

Central pontine myelinolysis is a non-inflammatory demyelinating disease characterized by loss of myelin with relative neuron sparing, associated with rapid correction of hyponatremia and sometimes hypernatremia or chronic alcoholism. We are reporting a case of 52 year old male patient who was chronic alcoholic from past 20 years, presented to us with complaints of altered sensorium and dysarthria of 5 days duration .He was investigated and diagnosed as case of central pontine myelinosis associated with chronic alcoholism

Linking Representations with Multimodal Contrastive Learning

Many applications require grouping instances contained in diverse document datasets into classes. Most widely used methods do not employ deep learning and do not exploit the inherently multimodal nature of documents. Notably, record linkage is typically conceptualized as a string-matching problem. This study develops CLIPPINGS, (Contrastively Linking Pooled Pre-trained Embeddings), a multimodal framework for record linkage. CLIPPINGS employs end-to-end training of symmetric vision and language bi-encoders, aligned through contrastive language-image pre-training, to learn a metric space where the pooled image-text representation for a given instance is close to representations in the same class and distant from representations in different classes. At inference time, instances can be linked by retrieving their nearest neighbor from an offline exemplar embedding index or by clustering their representations. The study examines two challenging applications: constructing comprehensive supply chains for mid-20th century Japan through linking firm level financial records - with each firm name represented by its crop in the document image and the corresponding OCR - and detecting which image-caption pairs in a massive corpus of historical U.S. newspapers came from the same underlying photo wire source. CLIPPINGS outperforms widely used string matching methods by a wide margin and also outperforms unimodal methods. Moreover, a purely self-supervised model trained on only image-OCR pairs also outperforms popular string-matching methods without requiring any labels

Self-Reported Obesity Status of School Teachers Teaching In Various Schools of District Panchkula, Haryana, India

INTRODUCTION: Being overweight and obese are a major concern across the globe, as it impact one’s quality of life.AIM: To assess the self-reported Obesity status of private school teachers teaching in various schools of district Panchkula, Haryana, IndiaMATERIALS AND METHOD: A Descriptive, cross sectional study, questionnaire based study was conducted among school teachers of Panchkula District, Haryana, India using self-reported 10 item-questionnaire. The data was duly entered into Microsoft excel wherein descriptive statistics were applied and then, regression analysis to find differences if any, was applied using SPSS version 21.0 (IBM Corp, Armonk NY)RESULTS: Of a 500 questionnaires distributed, 417 were fit for data entry (response rate 83.4%) with a majority of the study subjects being females (220,52.7%), and 97.1% of the teachers taught in private schools. The overall prevalence of obesity 15.1% was reported based on self-reported BMI. A non-significant p value of .006.(Regression analysis) between males and females with respect to their self-reported BMI was observed.CONCLUSION: Efforts are required to be directed to prevent obesity among schoolteachers, who act as role-models for their students

To study the correlation of mean macular thickness using optical coherence tomography with distant and near visual acuity in patients of diabetic maculopathy

Background: to study the correlation of mean macular thickness using optical coherence tomography with distant and near visual acuity in patients of diabetic maculopathy.Methods: A prospective, single centre study was conducted on 50 eyes of diabetic patients, with Diabetic Retinopathy with CSME in which patients macular thickness was measured on Ocular Coherence Tomography using fast macular thickness scan. The unaided and best corrected visual acuity was measured in all patients using Snellens distant vision and Jaggers near vision charts.Results: A linear correlation between the OCT measured macular thickness and both the distance visual acuity and the near visual acuity. That means that for a given level of macular thickness, we can predict visual acuity for it. In our series correlation coefficient was 0.921 for distance visual acuity and 0.899 for near visual acuity. Although the correlation value is high in our study, we did find a range of visual acuities for a given range of macular thickness. For every 100 micron change in mean macular thickness, best corrected visual acuity (BCVA) changed 0.3 LogMAR units, for distance as well as for near.Conclusions: Macular thickness and visual acuities (distance as well as near) are strongly correlated but there can be variations. And a wide range of visual acuities is possible for a given degree of macular edema. Macular thickness though a strong predictor of visual acuity; other factors might also play a role in determining visual acuity for a particular patient

A study of dermatoglyphics in club foot

Background:Development of dermatoglyphics pattern is under genetic control and it is established that aetiology of club foot is partly environmental and partly genetic. So study of dermatoglyphics pattern in club foot patient may become a diagnostic tool to know the development & inheritance of this clinical disorder.  Methods:A total of 42 male child aged b/w 1-8 year were included, for obtaining the palmar and finger tip print standard ink method suggested by Kilgariff was used, and each palmar and finger print were examined for important parameters like loops, whorls, arches, a-t-d angle, a-b ridge count and TFRC count. Then results were tabulated and analysed statistically.Results:Frequency of whorls increase in both hands significantly, frequency of arches and ulnar loops decrease significantly, frequency of radial loops increase in right hand and decrease in left hand but difference was not significant. TFRC count was reduced significantly and no significant difference was found in a-t-d angle and a-b ridge count.Conclusion: Dermatoglyphics is a genetically determined reliable marker for detecting the incidence of club foot. Merely by identifying the dermatoglyphics pattern of couples with family history of club foot may be at risk of having their offspring affected, and they can be diagnosed early and preventive measures can be taken.