Miracle-dz/UniFAR-FTUs huggingface dataset:
Grounded Adaptation for Zero-shot Executable Semantic Parsing

We propose Grounded Adaptation for Zero-shot Executable Semantic Parsing (GAZP) to adapt an existing semantic parser to new environments (e.g. new database schemas). GAZP combines a forward semantic parser with a backward utterance generator to synthesize data (e.g. utterances and SQL queries) in the new environment, then selects cycle-consistent examples to adapt the parser. Unlike data-augmentation, which typically synthesizes unverified examples in the training environment, GAZP synthesizes examples in the new environment whose input-output consistency are verified. On the Spider, Sparc, and CoSQL zero-shot semantic parsing tasks, GAZP improves logical form and execution accuracy of the baseline parser. Our analyses show that GAZP outperforms data-augmentation in the training environment, performance increases with the amount of GAZP-synthesized data, and cycle-consistency is central to successful adaptation.
TreeGAN: Incorporating Class Hierarchy into Image Generation

Conditional image generation (CIG) is a widely studied problem in computer vision and machine learning. Given a class, CIG takes the name of this class as input and generates a set of images that belong to this class. In existing CIG works, for different classes, their corresponding images are generated independently, without considering the relationship among classes. In real-world applications, the classes are organized into a hierarchy and their hierarchical relationships are informative for generating high-fidelity images. In this paper, we aim to leverage the class hierarchy for conditional image generation. We propose two ways of incorporating class hierarchy: prior control and post constraint. In prior control, we first encode the class hierarchy, then feed it as a prior into the conditional generator to generate images. In post constraint, after the images are generated, we measure their consistency with the class hierarchy and use the consistency score to guide the training of the generator. Based on these two ideas, we propose a TreeGAN model which consists of three modules: (1) a class hierarchy encoder (CHE) which takes the hierarchical structure of classes and their textual names as inputs and learns an embedding for each class; the embedding captures the hierarchical relationship among classes; (2) a conditional image generator (CIG) which takes the CHE-generated embedding of a class as input and generates a set of images belonging to this class; (3) a consistency checker which performs hierarchical classification on the generated images and checks whether the generated images are compatible with the class hierarchy; the consistency score is used to guide the CIG to generate hierarchy-compatible images. Experiments on various datasets demonstrate the effectiveness of our method.
An Integer Arithmetic-Based Sparse Linear Solver Using a GMRES Method and Iterative Refinement

In this paper, we develop a (preconditioned) GM-RES solver based on integer arithmetic, and introduce an iterative refinement framework for the solver. We describe the data format for the coefficient matrix and vectors for the solver that is based on integer or fixed-point numbers. To avoid overflow in calculations, we introduce initial scaling and logical shifts (adjustments) of operands in arithmetic operations. We present the approach for operand shifts, considering the characteristics of the GMRES algorithm. Numerical tests demonstrate that the integer arithmetic-based solver with iterative refinement has comparable solver performance in terms of convergence to the standard solver based on floating-point arithmetic. Moreover, we show that preconditioning is important, not only for improving convergence but also reducing the risk of overflow.
The MAVERIC Survey: New Compact Binaries Revealed by Deep Radio Continuum Observations of the Galactic Globular Cluster Terzan 5

Owing to its massive, dense core, Terzan 5 has the richest population of millisecond pulsars (MSPs) known among Galactic globular clusters. Here we report new deep 2–8 GHz radio continuum observations of Terzan 5 obtained with the Karl G. Jansky Very Large Array. We have identified a total of 24 sources within the cluster half-light radius, including 17 within the core radius. Nineteen are associated with previously studied MSPs and X-ray binaries. Three of the new radio sources have steep radio spectra and are located within the cluster core, as expected for MSPs. These three sources have hard X-ray photon indices (Γ = 1.3–1.5) and highly variable X-ray emission, suggesting they are binary MSPs belonging to the spider class. For the most X-ray luminous of these sources, the redback spider classification is confirmed by its X-ray light curve, which shows an orbital period of 12.32 hr and double-peaked structure around X-ray maximum. The likely discovery of bright binary MSPs in a well-studied cluster like Terzan 5 highlights how deep radio continuum imaging can complement pulsar search and timing observations in finding probable eclipsing systems. The other new radio source in the core has a flat radio spectrum and is X-ray faint ( erg s−1) with a photon index Γ = 2.1 ± 0.5, consistent with the properties expected for a quiescent stellar-mass black hole X-ray binary.
Continuous measurements for control of superconducting quantum circuits

ABSTRACT Developments over the last two decades have opened the path towards quantum technologies in many quantum systems, such as cold atoms, trapped ions, cavity-quantum electrodynamics (QED), and circuit-QED. However, the fragility of quantum states to the effects of measurement and decoherence still poses one of the greatest challenges in quantum technology. An imperative capability in this path is quantum feedback, as it enhances the control possibilities and allows for prolonging coherence times through quantum error correction. While changing parameters from shot to shot of an experiment or procedure can be considered feedback, quantum mechanics also allows for the intriguing possibility of performing feedback operations during the measurement process itself. This broader approach to measurements leads to the concepts of weak measurement, quantum trajectories, and numerous types of feedback with no classical analogs. These types of processes are the primary focus of this review. We introduce the concept of quantum feedback in the context of circuit-QED, an experimental platform with significant potential in quantum feedback and technology. We then discuss several experiments and see how they elucidate the concepts of continuous measurements and feedback. We conclude with an overview of coherent feedback, with application to fault-tolerant error correction. Graphical abstract
Ultra Buck DC/DC Converter for Electric Vehicles

A critical challenge in power conversion in electric vehicles is the efficient use of DC-DC buck converters that need to provide 12-V supply for load systems from 400/800-V batteries. This paper presents a literature review on the development of DC-DC buck converters. Moreover, one novel four-phase interleaved step-down topology is selected for simulation and hardware experiments. Based on the four-phase interleaved structure, an extended-phase topology is proposed, which has a higher voltage conversion ratio. Control techniques are also applied to it. Theoretical analyses and simulation results are provided to verify the improved converter. A 400V-to-12V and 150W output power hardware prototype is implemented to verify its performance
General DeepLCP model for disease prediction : Case of Lung Cancer

According to GHO (Global Health Observatory (GHO), the high prevalence of a large variety of diseases such as Ischaemic heart disease, stroke, lung cancer disease and lower respiratory infections have remained the top killers during the past decade. The growth in the number of mortalities caused by these disease is due to the very delayed symptoms'detection. Since in the early stages, the symptoms are insignificant and similar to those of benign diseases (e.g. the flu ), and we can only detect the disease at an advanced stage. In addition, The high frequency of improper practices that are harmful to health, the hereditary factors, and the stressful living conditions can increase the death rates. Many researches dealt with these fatal disease, and most of them applied advantage machine learning models to deal with image diagnosis. However the drawback is that imagery permit only to detect disease at a very delayed stage and then patient can hardly be saved. In this Paper we present our new approach "DeepLCP" to predict fatal diseases that threaten people's lives. It's mainly based on raw and heterogeneous data of the concerned (or under-tested) person. "DeepLCP" results of a combination combination of the Natural Language Processing (NLP) and the deep learning paradigm.The experimental results of the proposed model in the case of Lung cancer prediction have approved high accuracy and a low loss data rate during the validation of the disease prediction.
Pendulum-driven passive self-tuning energy harvester for rotating applications

This paper presents a pendulum-driven passive self-tuning energy harvester for rotating applications. The compact harvester rotates in the vertical plane and is comprised of a steel ball on a cable (akin to a pendulum) and a relatively rigid piezoelectric generating beam. The ball impacts the beam periodically under the influence of gravity, driving generation in the beam. The centrifugal force from the rotation changes the resonant frequency of the cable with tip-mounted ball and of the harvester. With optimized design, the resonant frequency of the harvester can be passively tuned to match the frequency of the rotation. With a resonant frequency of 15 Hz, a prototype of the passive self-tuning energy harvester achieved more than 11 Hz bandwidth, which is much larger than the predicted 0.8 Hz bandwidth of an untuned harvester.
Synthesis Framework for Executing Neural Networks on Heterogeneous Platforms

Artificial neural networks (ANNs) have been a topic of research for a few decades. Nowadays, owing to the advancement in computation power and research in learning methods for neural networks (NNs), they are prevalent in almost every facet of daily life. These include search engine algorithms, smart phone personal assistants, autonomous vehicles and so on. Current frameworks focus more towards learning algorithms and architectures of neural networks. Not much attention has been paid to the process involved in executing neural networks on different devices. Popular machine learning (ML) frameworks provide platform support using third party basic linear algebra subprogram libraries (BLAS). These frameworks support specific NNs, and target devices either limited to general purpose computers or only application specific hardware. Thus, a framework is still needed that can support the synthesis of different NNs for heterogeneous devices, including general purpose and application specific hardware. Based on that, this work proposes a framework for synthesizing specific NNs for heterogeneous devices. The heterogeneous devices targeted by this framework are CPUs, GPUs and FPGA-SoCs. OpenCL programming framework has been selected as the standard hardware abstraction for heterogeneous devices. Concretely, this framework only focuses on the execution of trained NNs. A model of computation (MoC) based approach is selected for modeling ANNs. This model is then transformed to OpenCL model to address platform differences, and is complimented with a scheduler that allows the execution of desired target data set.
Release and activity of ACE inhibitory peptides from pea and whey protein

Angiotensin I converting enzyme (ACE) plays a major role in the regulation of blood pressure. A diagnostic assay to measure ACE activity was transformed into an enzyme inhibition assay and optimised, which led to a more sensitive and less expensive assay. By this spectrophotometric method, ACE inhibition is measured using the substrate furanacryloyl-Phe-Gly-Gly and rabbit lung acetone extract as ACE source. The optimised as well as the original ACE inhibition assays were used to verify the ACE inhibitory activity of captopril. The ACE inhibition assay was further validated by enalapril, its active derivative enalaprilat and the ACE-inhibitory peptide Ala-Leu-Pro-Met-His-Ile-Arg, corresponding to a tryptic fragment of bovine β-lactoglobulin. Sigmoid curves could be fit adequately to the data points representing ACE inhibition in function of inhibitor concentration. IC50 values for these compounds corresponded well with literature data. Futhermore, pea and whey protein hydrolysates obtained by digestion with trypsin showed ACE inhibitory activity in the ACE inhibition assay. Hence, this optimised assay is suited for the screening of ACE inhibitory peptides derived from food proteins with a possible antihypertensive effect in vivo. Later on, a more delicate and standardised assay was obtained by applying pure ACE from porcine kidney. This assay was also validated by captopril and Ala-Leu-Pro-Met-His-Ile-Arg. For most experiments done in this work, ACE inhibitory activity was measured by the latter assay.
Journal of Economic Development, Environment and People

Sustainability is an internationally discussed topic in the development of products and services. Frugal innovations aim to conserve resources in the manufacture and use of such products and services. It can, therefore, be assumed that the areas of sustainability and frugal innovation overlap. This work is intended to show the connection between sustainability, frugal innovations, and household appliances in the form of washing machines in industrialized countries. For the analysis, the characteristics of frugal innovations are compared with the requirements of washing machines in industrial nations, and then the parallels to the social development goals (SDG) are determined as characteristics for sustainability. The existing literature will be reviewed for evaluation. The work aims to show the connection and give an indication of the sustainable development of an everyday product as a frugal innovation in the industrialized countries.
Fuzzy Expert System for Recognition of Power Quality Disturbances

This paper presents a new approach to detect and classify power quality disturbances in the power system using Fuzzy C-means clustering, Fuzzy logic (FL) and Radial basis Function Neural Networks (RBFNN). Feature extracted through wavelet is used for training, after training, the obtained weight is used to classify the power quality problems in RBFNN, but it suffers from extensive computation and low convergence speed. Then to detect and classify the events, FL is proposed, the extracted characters are used to find out membership functions and fuzzy rules being determined from the power quality inherence. For the classification,5 types of disturbance are taken in to account. The classification performance of FL is compared with RBFNN.The clustering analysis is used to group the data in to clusters to identifying the class of the data with Fuzzy C-means algorithm. The classification accuracy of FL and Fuzzy C-means clustering is improved with the help of cognitive as well as the social behavior of particles along with fitness value using Particle swarm optimization (PSO),just by determining the ranges of the feature of the membership funtion for each rules to identify each disturbance specifically.The simulation result using Fuzzy C-means clustering possess significant improvements and gives classification results in less than a cycle when compared over other considered approach.
Lunar Rock Classification Using Machine Learning

Lunar landings by esteemed space stations around the world have yielded an abundance of new scientific data on the Moon which has helped scientists to study our closest neighbour and hence have provided evidence for understanding Earth’s past and future. This paper is about solving the challenge on HackerEarth about classifying the lunar rock into small or large rock. These tasks have historically been conducted by visual image inspection, thereby reducing the scope, reliability and accuracy of the retrieval. The competition was to build a machine learning model to reduce human effort of doing a monotonous task. We built a Support Vector Machine model, used widely in classification problems, feeding features extracted from images in the dataset using OpenCV, only to obtain an accuracy of 99.41%. Our source code solving the challenge and the dataset are given in the github repository https://github.com/ArshitaKalra/Lunar-Rock-classification.
Protoplast generation in nematode trapping fungi

A protocol for generating viable protoplasts from isolate of the nematophagous fungus Arthobotrys oviformis is developed. The lytic enzyme Novozyme 234 is used in the study. Maximum protoplasts are released with 24 hours old A. oviformis mycelia after 35 minutes of treatment with the lytic enzyme Novozyme 234 at a concentration of 10 mg ml. The nematophagous fungus Dactylaria parvispora however is found to be refractive to the process and protoplasts could not be generated under the range of conditions used in this study. The yield of protoplasts and the frequency of regeneration of A. oviformis is found to be highest when 0.6 molar Sodium Chloride is used as the osmotic stabilizer. This is the first report of an optimized method for generating viable protoplast from young mycelia of the nematophagous fungus A. oviformis.
Analysis of Decision Tree Pruning Using Windowing in Medical Datasets with Di(cid:27)erent Class Distributions

Windowing searches for a sample of the training set that would provide better and smaller models than those obtained from the entire set. Originally, the classi(cid:28)ers are pruned only at the end of each trial, and the technique is considered to be interesting in unbalanced datasets. We have performed experiments to verify if pruning the inter-mediary trees within a trial is bene(cid:28)cial; if windowing is better than the tree inducer alone; and the behavior of windowing in medical datasets with di(cid:27)erent class distributions. Our main conclusions are: windowing is not better than the tree inducer alone and not bene(cid:28)cial when comparing balanced and unbalanced domains. However when the emphasis is on human understanding of the induced knowledge, as well as on extensibility, windowing tends to build smaller trees, which are more likely to be interpreted by humans, without signi(cid:28)cant di(cid:27)erences in AUC.
Discrete event simulation for problem solving in the context of an emergency department

Emergency departments (ED) are responsible for the immediate care and stabilization of patients in critical health conditions. Several factors have caused overcrowding in the emergency care system, but the variability of patient arrival and the triage process requires special attention. The criticality of these components and their configuration directly impact the waiting times, length of stay and quality of service, being the subject of several studies. So, this paper aims to understand by means of Discrete Event Simulation how ED works with the variation of patient arrival and how this variation highlights the bottlenecks of the triage process. Varying the patient arriving interval between 0.1 and 7.6 in a 4-hour scenario, the system saturation point was established in β = 1.1. Besides, with the variation in the number of triages points, a considerable decrease in the total length of stay spent and the waiting times were noticed, mainly when there was two triage points operating simultaneously.
Cytological Basis and Molecular Mechanism of Variation in Number of Seeds Per Pod in Brassica napus

The number of seeds per pod in Brassica napus shows great variation among different cultivars, lines and germplasm resources. Studies on this trait are important in increasing rapeseed yield by utilizing advantageous variations. In this study, we revealed the cytological basis and molecular mechanism of the formation of variation in number of seeds per pod in Brassica napus through analyzing a series of doubled haploid lines and cultivars. The results showed that a part of ovule primordiums could not develop into mature seeds because they had encountered abortion during the development process. The percentage of normally developed ovules was an important determinant of variation in number of seeds per pod. Cytological analyses concluded that the abortion of ovule occurred between the stages of megaspore degradation and uninucleate gametophyte. Our results also showed that the variation in the rate of ovule abortion is related to the differences in the regulation level of BRS-BRI1 signaling pathway in Brasica napus .
The Effects of Hungarian Vetch (Vicia pannonica Crantz.) and Triticale (x Triticosecale sp. Wittmack) Sown in Different Seed Rates on Feed Value and Ruminal Degradability Characteristics of Nutrients

Bu calismanin amaci, tritikale (x Triticosecale sp. Wittmack) ile Macar figinin (Vicia pannonica Crantz.) farkli tohum karisim oranlarinin (HV, 75:25 HVT, 50:50HVT, 25:75 HVT ve T). ) tritikalenin ciceklenme donemindeki ot verimi ve kalitesi ile rumende parcalanabilirlik ozelliklerine etkisini belirlemekti. Arastirma, 2015-2016 vejatasyon doneminde Gaziosmanpasa Universitesi Tarimsal Uygulama ve Arastirma Merkezi deneme sahalarinda gerceklestirilmistir. Deneme tesaduf bloklari deneme deseninde 3 tekrarlamali olarak yurutulmustur. Ayrica, saf ve karisim kuru otlarin rumende kuru madde parcalanabilirlik ozelliklerini belirlemek amaciyla rumenlerine kanul takilmis uc bas Karayaka kocu (50 kg canli agirlik ve ortalama 3 yasinda) kullanilmistir. Yesil ot ve kuru madde veriminin gruplar arasinda farkli olmadigi, en yuksek ham protein konsantrasyonu (% 13.12) ise saf ekili Macar figinde oldugu belirlenmistir. (P<0.01). Ayrica, maksimum ham protein verimi 75:25HVT karisim grubundan elde edilmistir. Etkin kuru madde sindirilebilirligi (ED2, ED5 ve ED8) acisindan, en yuksek kuru madde parcalanabilirligine 50: 50HVT karisim grubunda (720.82, 706.1 ve 692.64 g/kg) bulunmustur. Sonuc olarak, Macar fig ve tritikale karisik ekim durumunda, saf ekime gore daha fazla sindirilebilir kuru madde verimi ve ham protein veriminin elde edilebilecegi sonucuna varilmistir.
Intelligently Tuned μ-PID for Aircraft Lateral Control

Aircraft control is often exposed to disturbances and uncertainties. Accomplishing safe and secure flight requires robust controls. The well-known robust methods usually suggest large in size controllers. In this paper, a PID sized controller (μ-PID) is designed where both the robust frequency domain merits, observed by the robust techniques, and the time domain desires contribute. The performance index weights are appropriately set to match the intended goals. The optimization process is conducted using particle swarm optimization (PSO) technique. The H∞, μ synthesis and the μ-PID designs are used for a multi-input multi-output (MIMO) flight lateral control. The controlled variables are the roll angle φ and the sideslip angle β. The numerical simulation demonstrates that the proposed method with lower sized controller can yield performances better than what the robust methods such as the H∞, μ synthesis designs succeed.