Miracle-dz/UniFAR-FTUs huggingface dataset:
Global reliability sensitivity analysis of motion mechanisms

The kinematic failure of a mechanism is commonly caused by the random input errors such as the errors of component dimensions and motion inputs. For identifying the main source of the failure probability, the global reliability sensitivity analysis is introduced. The method is based on decomposing the variance of the failure domain indicator function into partial variances of increasing orders, which enable attributing the failure probability to each of the random input errors and their interactions. Then the analytical solutions of the global reliability sensitivity indices are derived based on the first-order Taylor series expansion of the motion error function. Compared with the traditional local reliability sensitivity indices, the global reliability sensitivity analysis technique has two main merits. First, it is more suitable for ranking the importance of random input errors and identifying the source of failure probability. Second, it provides not only the individual effect of each random input error on the failure probability but also their interaction and total effects. The engineering significance of the global reliability sensitivity indices as well as the effectiveness of the analytical method for computing the global reliability sensitivity indices are demonstrated with a four-bar sine function generator mechanism and a rack-and-pinion steering linkage.
Pavlok-Nudge: A Feedback Mechanism for Atomic Behaviour Modification with Snoring Usecase

This paper proposes a feedback mechanism to 'break bad habits' using the Pavlok device. Pavlok utilises beeps, vibration and shocks as a mode of aversion technique to help individuals with behaviour modification. While the device can be useful in certain periodic daily life situations, like alarms and exercise notifications, the device relies on manual operations that limit its usage. To this end, we design a user interface to generate an automatic feedback mechanism that integrates Pavlok and a deep learning based model to detect certain behaviours via an integrated user interface i.e. mobile or desktop application. Our proposed solution is implemented and verified in the context of snoring, which first detects audio from the environment following a prediction of whether the audio content is a snore or not. Based on the prediction of the deep learning model, we use Pavlok to alert users for preventive measures. We believe that this simple solution can help people to change their atomic habits, which may lead to long-term benefits.
Vārta: A Large-Scale Headline-Generation Dataset for Indic Languages

We present V\=arta, a large-scale multilingual dataset for headline generation in Indic languages. This dataset includes 41.8 million news articles in 14 different Indic languages (and English), which come from a variety of high-quality sources. To the best of our knowledge, this is the largest collection of curated articles for Indic languages currently available. We use the data collected in a series of experiments to answer important questions related to Indic NLP and multilinguality research in general. We show that the dataset is challenging even for state-of-the-art abstractive models and that they perform only slightly better than extractive baselines. Owing to its size, we also show that the dataset can be used to pretrain strong language models that outperform competitive baselines in both NLU and NLG benchmarks.
Scalable and Precise Application-Centered Call Graph Construction for Python

Call graph construction is the foundation of inter-procedural static analysis. PYCG is the state-of-the-art approach for constructing call graphs for Python programs. Unfortunately, PyCG does not scale to large programs when adapted to whole-program analysis where application and dependent libraries are both analyzed. Moreover, PyCG is flow-insensitive and does not fully support Python's features, hindering its accuracy. To overcome these drawbacks, we propose a scalable and precise approach for constructing application-centered call graphs for Python programs, and implement it as a prototype tool JARVIS. JARVIS maintains a type graph (i.e., type relations of program identifiers) for each function in a program to allow type inference. Taking one function as an input, JARVIS generates the call graph on-the-fly, where flow-sensitive intra-procedural analysis and inter-procedural analysis are conducted in turn and strong updates are conducted. Our evaluation on a micro-benchmark of 135 small Python programs and a macro-benchmark of 6 real-world Python applications has demonstrated that JARVIS can significantly improve PYCG by at least 67% faster in time, 84% higher in precision, and at least 20% higher in recall.
Compressing neural network by tensor network with exponentially fewer variational parameters

Neural network (NN) designed for challenging machine learning tasks is in general a highly nonlinear mapping that contains massive variational parameters. High complexity of NN, if unbounded or unconstrained, might unpredictably cause severe issues including over-fitting, loss of generalization power, and unbearable cost of hardware. In this work, we propose a general compression scheme that significantly reduces the variational parameters of NN by encoding them to multi-layer tensor networks (TN's) that contain exponentially-fewer free parameters. Superior compression performance of our scheme is demonstrated on several widely-recognized NN's (FC-2, LeNet-5, and VGG-16) and datasets (MNIST and CIFAR-10), surpassing the state-of-the-art method based on shallow tensor networks. For instance, about 10 million parameters in the three convolutional layers of VGG-16 are compressed in TN's with just $632$ parameters, while the testing accuracy on CIFAR-10 is surprisingly improved from $81.14\%$ by the original NN to $84.36\%$ after compression. Our work suggests TN as an exceptionally efficient mathematical structure for representing the variational parameters of NN's, which superiorly exploits the compressibility than the simple multi-way arrays.
Global Convergence of Deep Galerkin and PINNs Methods for Solving Partial Differential Equations

Numerically solving high-dimensional partial differential equations (PDEs) is a major challenge. Conventional methods, such as finite difference methods, are unable to solve high-dimensional PDEs due to the curse-of-dimensionality. A variety of deep learning methods have been recently developed to try and solve high-dimensional PDEs by approximating the solution using a neural network. In this paper, we prove global convergence for one of the commonly-used deep learning algorithms for solving PDEs, the Deep Galerkin Method (DGM). DGM trains a neural network approximator to solve the PDE using stochastic gradient descent. We prove that, as the number of hidden units in the single-layer network goes to infinity (i.e., in the ``wide network limit"), the trained neural network converges to the solution of an infinite-dimensional linear ordinary differential equation (ODE). The PDE residual of the limiting approximator converges to zero as the training time $\rightarrow \infty$. Under mild assumptions, this convergence also implies that the neural network approximator converges to the solution of the PDE. A closely related class of deep learning methods for PDEs is Physics Informed Neural Networks (PINNs). Using the same mathematical techniques, we can prove a similar global convergence result for the PINN neural network approximators. Both proofs require analyzing a kernel function in the limit ODE governing the evolution of the limit neural network approximator. A key technical challenge is that the kernel function, which is a composition of the PDE operator and the neural tangent kernel (NTK) operator, lacks a spectral gap, therefore requiring a careful analysis of its properties.
Surrogate-based optimisation of process systems to recover resources from wastewater

Wastewater systems are transitioning towards integrative process systems to recover multiple resources whilst simultaneously satisfying regulations on final effluent quality. This work contributes to the literature by bringing a systems-thinking approach to resource recovery from wastewater, harnessing surrogate modelling and mathematical optimisation techniques to highlight holistic process systems. A surrogate-based process synthesis methodology was presented to harness high-fidelity data from black box process simulations, embedding first principles models, within a superstructure optimisation framework. Modelling tools were developed to facilitate tailored derivative-free optimisation solutions widely applicable to black box optimisation problems. The optimisation of a process system to recover energy and nutrients from a brewery wastewater reveals significant scope to reduce the environmental impacts of food and beverage production systems. Additionally, the application demonstrates the capabilities of the modelling methodology to highlight optimal processes to recover carbon, nitrogen, and phosphorous resources whilst also accounting for uncertainties inherent to wastewater systems.
STOCHASTICALLY SCALABLE FLOW CONTROL

Recent advances in the mathematical analysis of flow control have prompted the creation of the Scalable TCP (STCP) and Exponential RED (E-RED) algorithms. These are designed to be scalable under the popular deterministic delay stability modeling framework. In this article, we analyze stochastic models of STCP and STCP combined with E-RED link behavior. We find that under certain plausible network conditions, these probabilistic models also exhibit scalable behavior. In particular, we derive parameter choice schemes for which the equilibrium coefficients of variation of flow rates are bounded, however large, fast, or complex the network. Our model is shown to exhibit behavior similar to the mean field convergence that has recently been observed in TCP.
Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models

A medical provider’s summary of a patient visit serves several critical purposes, including clinical decision-making, facilitating hand-offs between providers, and as a reference for the patient. An effective summary is required to be coherent and accurately capture all the medically relevant information in the dialogue, despite the complexity of patient-generated language. Even minor inaccuracies in visit summaries (for example, summarizing “patient does not have a fever” when a fever is present) can be detrimental to the outcome of care for the patient.This paper tackles the problem of medical conversation summarization by discretizing the task into several smaller dialogue-understanding tasks that are sequentially built upon. First, we identify medical entities and their affirmations within the conversation to serve as building blocks. We study dynamically constructing few-shot prompts for tasks by conditioning on relevant patient information and use GPT-3 as the backbone for our experiments. We also develop GPT-derived summarization metrics to measure performance against reference summaries quantitatively. Both our human evaluation study and metrics for medical correctness show that summaries generated using this approach are clinically accurate and outperform the baseline approach of summarizing the dialog in a zero-shot, single-prompt setting.
Rethinking the Value of Labels for Instance-Dependent Label Noise Learning

Label noise widely exists in large-scale datasets and significantly degenerates the performances of deep learning algorithms. Due to the non-identifiability of the instance-dependent noise transition matrix, most existing algorithms address the problem by assuming the noisy label generation process to be independent of the instance features. Unfortunately, noisy labels in real-world applications often depend on both the true label and the features. In this work, we tackle instance-dependent label noise with a novel deep generative model that avoids explicitly modeling the noise transition matrix. Our algorithm leverages casual representation learning and simultaneously identifies the high-level content and style latent factors from the data. By exploiting the supervision information of noisy labels with structural causal models, our empirical evaluations on a wide range of synthetic and real-world instance-dependent label noise datasets demonstrate that the proposed algorithm significantly outperforms the state-of-the-art counterparts.
Weakly-Supervised ROI Extraction Method Based on Contrastive Learning for Remote Sensing Images

ROI extraction is an active but challenging task in remote sensing because of the complicated landform, the complex boundaries and the requirement of annotations. Weakly supervised learning (WSL) aims at learning a mapping from input image to pixel-wise prediction under image-wise labels, which can dramatically decrease the labor cost. However, due to the imprecision of labels, the accuracy and time consumption of WSL methods are relatively unsatisfactory. In this paper, we propose a two-step ROI extraction based on contractive learning. Firstly, we present to integrate multiscale Grad-CAM to obtain pseudo pixelwise annotations with well boundaries. Then, to reduce the compact of misjudgments in pseudo annotations, we construct a contrastive learning strategy to encourage the features inside ROI as close as possible and separate background features from foreground features. Comprehensive experiments demonstrate the superiority of our proposal. Code is available at https://github.com/HE-Lingfeng/ROI-Extraction
Finding Meaningful Distributions of ML Black-boxes under Forensic Investigation

Given a poorly documented neural network model, we take the perspective of a forensic investigator who wants to find out the model's data domain (e.g. whether on face images or traffic signs). Although existing methods such as membership inference and model inversion can be used to uncover some information about an unknown model, they still require knowledge of the data domain to start with. In this paper, we propose solving this problem by leveraging on comprehensive corpus such as ImageNet to select a meaningful distribution that is close to the original training distribution and leads to high performance in follow-up investigations. The corpus comprises two components, a large dataset of samples and meta information such as hierarchical structure and textual information on the samples. Our goal is to select a set of samples from the corpus for the given model. The core of our method is an objective function that considers two criteria on the selected samples: the model functional properties (derived from the dataset), and semantics (derived from the metadata). We also give an algorithm to efficiently search the large space of all possible subsets w.r. t. the objective function. Experimentation results show that the proposed method is effective. For example, cloning a given model (originally trained with CIFAR-10) by using Caltech 101 can achieve 45.5% accuracy. By using datasets selected by our method, the accuracy is improved to 72.0%.
Growth of highly dense InAs quantum dots with improved crystal quality embedded in an InGaAsSb quantum well

We investigate the structural and optical properties of InAs quantum dots (QDs) embedded in InxGa1−xAs0.96Sb0. 04 layers with different In content. Indium incorporation from 0 to 9% increases QD areal density up to 9.3 × 1010 cm−2 and decreases QD diameter and height by the reduction of the island–island interaction. The elastic strain of the InGaAsSb layers, surrounded by a GaAs matrix, increases with higher In content. Further, the increase of In content from 5 to 9% reduces the density of dislocation loops in the InGaAsSb and GaAs layers almost by half due to improvement of the InAs/InGaAsSb interface quality. The photoluminescence peak from the QDs is redshifted with increasing In content as a result of reduced strain inside the QDs.
Fusion of infrared and visible images based on nonsubsampled Contourlet transform

Aiming at the features of infrared and visible images,a fusion algorithm based on NonSubsampled Contourlet Transform(NSCT) is proposed for infrared and visible image fusion.Firstly,the NSCT is used to perform the multiscale and multidirection decomposition of the source images.Secondly,low frequency subband coefficients and bandpass directional subband coefficients of the fused image can be obtained by the fusion rules based on region energy and area character,respectively.Finally,the fused image is reconstructed by performing the inverse NSCT on the combined coefficients.Simulation experiments show that this algorithm can yield fusion image with better fusion result than that of using Contourlet transform.
Advances in Climate Analysis and Monitoring: Reflections on 40 Years of Climate Diagnostics and Prediction Workshops

AbstractWe examine the progress in the analysis of climate variability through the lens of a 40-year series of annual Climate Diagnostics and Prediction Workshops initiated by the National Oceanic and Atmospheric Administration (NOAA) in 1976. The evolution of climate data and data access, data analysis and display, and our understanding of the physical mechanisms associated with climate variability, as well as the evolution in the character of the workshops, are documented by reference to the series of workshop proceedings. This retrospective essay chronicles the transition from the mid-1970s, when individual investigators or their organizations held much of the climate data suitable for research, to the present day, where many of the key climate datasets are freely accessible on the Internet. In parallel we also chart the evolution in data analysis and display tools from hand-drawn line graphs of single-station data to color animations of regional and global fields based on satellite data, numerical mod.
GENERAL FIXED POINT THEOREMS OF GENERALIZED GREGU_S TYPE IN SYMMETRIC SPACES AND APPLICATIONS

In this paper a general fixed point theorem of Greguˇs type insymmetric space is proved, which generalize Theorems 1 and 3 [19], Theorem1.2 [23], Theorems 3.1 and 4.1 [28] and we obtain new similar results forstrict expansive mappings. In the last part of this paper, as applications, weobtain new results for mappings satisfying contractive (expansive) conditionof integral type
The fate and prospects of stem cell therapy in the treatment of hypoxic–ischemic encephalopathy

Hypoxic–ischemic encephalopathy (HIE) is a leading cause of long‐term neurological disability in neonates and adults. Despite emerging advances in supportive care, like the most effective approach, hypothermia, poor prognosis has still been present in current clinical treatment for HIE. Stem cell therapy has been adopted for treating cerebral ischemia in preclinical and clinical trials, displaying its promising therapeutic value. At present, reported treatments for stroke employed stem cells to replace the lost neurons and integrate them into the existing host circuitry, promoting the release of growth factors to support and stimulate endogenous repair processes and so on. In this review, a meaningful overview to numerous studies published up to now was presented by introducing the preclinical and clinical research status of stem cell therapy for cerebral ischemia and hypoxia, discussing potential therapeutic mechanisms of stem cell transplantation for curing HI‐induced brain injury, summarizing a series of approaches for marking transplanted cells and existing imaging systems for stem cell labelling and in vivo tracking and expounding the endogenous regeneration capability of stem cells in the newborn brain when subjected to an HI insult. Additionally, it is promising to combine stem therapy with neuromodulation through specific regulation of neural circuits. The crucial neural circuits across different brain areas related to functional recovery are of great significance for the application of neuromodulation strategies after the occurrence of neonatal hypoxic–ischemic encephalopathy (NHIE).
Prediction of fetal and neonatal outcomes after preterm manifestations of placental insufficiency: systematic review of prediction models

To identify all prediction models for fetal and neonatal outcomes in pregnancies with preterm manifestations of placental insufficiency (gestational hypertension, pre‐eclampsia, HELLP syndrome or fetal growth restriction with its onset before 37 weeks' gestation) and to assess the quality of the models and their performance on external validation.
Identification of genes showing altered DNA methylation and gene expression in the renal proximal tubular cells of rats treated with ochratoxin A for 13 weeks

Ochratoxin A (OTA) is a mycotoxin that causes renal carcinogenicity following the induction of karyomegaly in proximal tubular cells after repeated administration to rats. Here, we performed gene profiling regarding altered DNA methylation and gene expression in the renal tubules focusing on the mechanism of OTA‐induced carcinogenesis. For this purpose, OTA or 3‐chloro‐1,2‐propanediol (3‐MCPD), a renal carcinogen not inducing karyomegaly, was administered to rats for 13 weeks, and DNA methylation array and RNA sequencing analyses were performed on proximal tubular cells. Genes for which OTA altered the methylation status and gene expression level, after excluding genes showing similar expression changes by 3‐MCPD, were subjected to confirmation analysis of the transcript level by real‐time reverse‐transcription PCR. Gene Ontology (GO)‐based functional annotation analysis of validated genes revealed a cluster of hypermethylated and downregulated genes enriched under the GO term “mitochondrion,” such as those associated with metabolic reprogramming in carcinogenic process (Clpx, Mrpl54, Mrps34, and Slc25a23). GO terms enriched for hypomethylated and upregulated genes included “response to arsenic‐containing substance,” represented by Cdkn1a involved in cell cycle arrest, and “positive regulation of IL‐17 production,” represented by Osm potentiating cell proliferation promotion. Other genes that did not cluster under any GO term included Lrrc14 involved in NF‐κB‐mediated inflammation, Gen1 linked to DNA repair, Has1 related to chromosomal aberration, and Anxa3 involved in tumor development and progression. In conclusion, a variety of genes engaged in carcinogenic processes were obtained by epigenetic gene profiling in rat renal tubular cells specific to OTA treatment for 13 weeks.
Corneal confocal microscopy reveals small nerve fibre loss correlating with motor function in adult spinal muscular atrophy

5q Spinal muscular atrophy (SMA) is a progressive, inherited, and severely disabling – yet treatable – motor neuron disease. Although treatment options have evolved in recent years, biomarkers for treatment monitoring and prognosis prediction remain elusive. Here, we investigated the utility of corneal confocal microscopy (CCM), a non‐invasive imaging technique to quantify small corneal nerve fibres in vivo, as a diagnostic tool in adult SMA.