Aerodynamics of forward swept wing.

A forward-swept wing is an aircraft wing configuration in which the quarter-chord line of the wing has a forward sweep. Typically, the leading edge also sweeps forward. Forward swept wing is proposed with the goal of enhancing performance and controllability during high angle of attack perching maneuvers. Data is presented from a series of XFLR5 analysis to qualify the aerodynamic effect of forward swept over a range of angle of attack from -25º to +75º. Various graphs were obtained during this analysis which indicates that the forward swept wing configuration can achieve qualitatively different low cost perching maneuvers

Soft Merging of Experts with Adaptive Routing

Sparsely activated neural networks with conditional computation learn to route their inputs through different "expert" subnetworks, providing a form of modularity that densely activated models lack. Despite their possible benefits, models with learned routing often underperform their parameter-matched densely activated counterparts as well as models that use non-learned heuristic routing strategies. In this paper, we hypothesize that these shortcomings stem from the gradient estimation techniques used to train sparsely activated models that use non-differentiable discrete routing decisions. To address this issue, we introduce Soft Merging of Experts with Adaptive Routing (SMEAR), which avoids discrete routing by using a single "merged" expert constructed via a weighted average of all of the experts' parameters. By routing activations through a single merged expert, SMEAR does not incur a significant increase in computational costs and enables standard gradient-based training. We empirically validate that models using SMEAR outperform models that route based on metadata or learn sparse routing through gradient estimation. Furthermore, we provide qualitative analysis demonstrating that the experts learned via SMEAR exhibit a significant amount of specialization. All of the code used in our experiments is publicly available

Aerodynamics of forward swept wing.

A forward-swept wing is an aircraft wing configuration in which the quarter-chord line of the wing has a forward sweep. Typically, the leading edge also sweeps forward. Forward swept wing is proposed with the goal of enhancing performance and controllability during high angle of attack perching maneuvers. Data is presented from a series of XFLR5 analysis to qualify the aerodynamic effect of forward swept over a range of angle of attack from -25º to +75º. Various graphs were obtained during this analysis which indicates that the forward swept wing configuration can achieve qualitatively different low cost perching maneuvers.</jats:p

Correlation of plasma fibrinogen and lipid profile in normotensive type 2 diabetes and type 2 diabetes with hypertension

Background &amp; Objectives: Diabetes mellitus (DM) is the most common metabolic disorder associated with several abnormalities like hyperglycaemia, high blood pressure, dyslipidaemia, obesity, which often may lead to atherosclerotic cardiovascular disorders. Plasma fibrinogen as an independent cardiovascular risk factor and it is elevated in response to factors like BMI, diabetes, hypertension, serum lipoproteins. The present study has been undertaken to correlate plasma fibrinogen level with cardiovascular risk factors such as BMI, Hypertension, Hyperglycemia and dyslipidaemia, in normotensive as well as hypertensive diabetic patients. Methods: A case control study was done with 90 subjects divided into 3 groups (Group 1: healthy controls, Group 2: Normotensive Type 2 Diabetics and Group 3: Hypertensive Type 2 Diabetics) with inclusion and exclusion criteria. Plasma fibrinogen, Fasting and post prandial blood glucose along with fasting lipid profile were estimated. Blood pressure was recorded and BMI was calculated. Multiple comparisons were made using ANOVA test and correlated with Pearson’s correlation coefficient ‘r’. Results: Plasma fibrinogen, BMI were significantly elevated in Group 2 and highest in group 3. Mean ± SD in group 2 and 3 for lipoproteins were respectively, total cholesterol, (156.43± 50.29, 170.70 ± 47.43) HDL, (30.83±7.905, 25.13 ± 5.316) LDL (80.47 ± 22.14, 90.63 ± 31.60), VLDL (35.33 ± 7.336, 37.70 ± 7.484) TAG (136.63 ± 61.90, 146.26 ± 54.71). Plasma fibrinogen is positively correlated to total cholesterol, LDL and triglycerides which is significant in normotensive diabetics and is positively correlated to BMI, SBP, DBP, TC, LDL, VLDL and TAG in hypertensive diabetic subjects which was statistically significant. Conclusion: Plasma fibrinogen is correlated significantly with lipoproteins levels and BMI in hypertensive diabetic patients so it can be considered as a risk factor for atherosclerotic events in such patients and it can be a potential marker for screening diabetic patients who are at risk of developing cardiovascular complications

Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training examples as part of the input. ICL incurs substantial computational, memory, and storage costs because it involves processing all of the training examples every time a prediction is made. Parameter-efficient fine-tuning (e.g. adapter modules, prompt tuning, sparse update methods, etc.) offers an alternative paradigm where a small set of parameters are trained to enable a model to perform the new task. In this paper, we rigorously compare few-shot ICL and parameter-efficient fine-tuning and demonstrate that the latter offers better accuracy as well as dramatically lower computational costs. Along the way, we introduce a new parameter-efficient fine-tuning method called (IA)$^3$ that scales activations by learned vectors, attaining stronger performance while only introducing a relatively tiny amount of new parameters. We also propose a simple recipe based on the T0 model called T-Few that can be applied to new tasks without task-specific tuning or modifications. We validate the effectiveness of T-Few on completely unseen tasks by applying it to the RAFT benchmark, attaining super-human performance for the first time and outperforming the state-of-the-art by 6% absolute. All of the code used in our experiments is publicly available

Design and Fabrication of Laser Engraving Machine

Laser engraving machine is used to mark various pictures and symbols on different materials. The laser engraving setup is advantageous due to its low operational cost, lightweight, portability and easy-to-learn features. The paper fabricates a low cost rapid prototype laser engraving machine. The proposed setup has been applied to Glass Fiber Reinforced Plastics (GFRP) composites, plastics, wood, cardboard, etc., to yield desired profile, contour, information and various drawings. Moreover, developed laser engraving setup has high precision and processing efficiency. Laser engraving technique involves color change of the surface due to thermal energy emerged by the laser beam. The simulation of this machine is done using Laser GRBL software. A 2.5 W diode laser is used to engrave the various materials. Various advanced software and hardware like Inkscape, MakerBase, GRBL controllers and microcontrollers are assembled together, further leading to the execution of the final engraving. Validity of the machine has been verified by performing dimensional, dependency and co-ordinate tests. Finally, pilot experimentation has been carried out on Cardboard composites.</jats:p

A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning

The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to a particular domain or task. Model MoErging methods aim to recycle expert models to create an aggregate system with improved performance or generalization. A key component of MoErging methods is the creation of a router that decides which expert model(s) to use for a particular input or application. The promise, effectiveness, and large design space of MoErging has spurred the development of many new methods over the past few years. This rapid pace of development has made it challenging to compare different MoErging methods, which are rarely compared to one another and are often validated in different experimental setups. To remedy such gaps, we present a comprehensive survey of MoErging methods that includes a novel taxonomy for cataloging key design choices and clarifying suitable applications for each method. Apart from surveying MoErging research, we inventory software tools and applications that make use of MoErging. We additionally discuss related fields of study such as model merging, multitask learning, and mixture-of-experts models. Taken as a whole, our survey provides a unified overview of existing MoErging methods and creates a solid foundation for future work in this burgeoning field.Comment: 26 page