Miracle-dz/UniFAR-FTUs huggingface dataset:
A 1.6-V 17-µA 5.2-ppm/°C bandgap reference with mutative curvature-compensation

A high-precision high-order curvature-compensated bandgap reference (BGR) compatible with standard BiCMOS process is presented in this article. By utilising the subthreshold and substrate currents generated by only a dynamic-threshold MOSFET (DTMOS), a novel mutative curvature-compensation has been realised to reduce the temperature drift within the full temperature range. The proposed mutative curvature-compensation is achieved by a high-order compensation term with changed temperature characteristics in the temperature range. Verification results of the proposed BGR implemented with 0.25 µm BiCMOS process demonstrate that the proposed BGR can operate down to 1.6 V supply voltage and consume a maximum supply current of 17 µA. A temperature coefficient of 5.2 ppm/°C is realised with temperature ranging from −65°C to 140°C, and a power supply rejection ratio of 57.7 dB is achieved without filtering capacitors.
A label efficient two-sample test

Two-sample tests evaluate whether two samples are realizations of the same distribution (the null hypothesis) or two different distributions (the alternative hypothesis). We consider a new setting for this problem where sample features are easily measured whereas sample labels are unknown and costly to obtain. Accordingly, we devise a three-stage framework in service of performing an effective two-sample test with only a small number of sample label queries: first, a classifier is trained with samples uniformly labeled to model the posterior probabilities of the labels; second, a novel query scheme dubbed \emph{bimodal query} is used to query labels of samples from both classes, and last, the classical Friedman-Rafsky (FR) two-sample test is performed on the queried samples. Theoretical analysis and extensive experiments performed on several datasets demonstrate that the proposed test controls the Type I error and has decreased Type II error relative to uniform querying and certainty-based querying. Source code for our algorithms and experimental results is available at \url{https://github.com/wayne0908/Label-Efficient-Two-Sample}.
A Benchmark for Modeling Violation-of-Expectation in Physical Reasoning Across Event Categories

Recent work in computer vision and cognitive reasoning has given rise to an increasing adoption of the Violation-of-Expectation (VoE) paradigm in synthetic datasets. Inspired by infant psychology, researchers are now evaluating a model's ability to label scenes as either expected or surprising with knowledge of only expected scenes. However, existing VoE-based 3D datasets in physical reasoning provide mainly vision data with little to no heuristics or inductive biases. Cognitive models of physical reasoning reveal infants create high-level abstract representations of objects and interactions. Capitalizing on this knowledge, we established a benchmark to study physical reasoning by curating a novel large-scale synthetic 3D VoE dataset armed with ground-truth heuristic labels of causally relevant features and rules. To validate our dataset in five event categories of physical reasoning, we benchmarked and analyzed human performance. We also proposed the Object File Physical Reasoning Network (OFPR-Net) which exploits the dataset's novel heuristics to outperform our baseline and ablation models. The OFPR-Net is also flexible in learning an alternate physical reality, showcasing its ability to learn universal causal relationships in physical reasoning to create systems with better interpretability.
Bipartite Temporal Graphs and the Parameterized Complexity of Multistage 2-Coloring

We consider the algorithmic complexity of recognizing bipartite temporal graphs. Rather than defining these graphs solely by their underlying graph or individual layers, we define a bipartite temporal graph as one in which every layer can be 2-colored in a way that results in few changes between any two consecutive layers. This approach follows the framework of multistage problems that has received a growing amount of attention in recent years. We investigate the complexity of recognizing these graphs. We show that this problem is NP-hard even if there are only two layers or if only one change is allowed between consecutive layers. We consider the parameterized complexity of the problem with respect to several structural graph parameters, which we transfer from the static to the temporal setting in three different ways. Finally, we consider a version of the problem in which we only restrict the total number of changes throughout the lifetime of the graph. We show that this variant is fixed-parameter tractable with respect to the number of changes.
Kernel-based Diffusion Approximated Markov Decision Processes for Off-Road Autonomous Navigation and Control

We propose a diffusion approximation method to the continuous-state Markov decision processes that can be utilized to address autonomous navigation and control in unstructured off-road environments. In contrast to most decision-theoretic planning frameworks that assume fully known state transition models, we design a method that eliminates such a strong assumption that is often extremely difficult to engineer in reality. We first take the second-order Taylor expansion of the value function. The Bellman optimality equation is then approximated by a partial differential equation, which only relies on the first and second moments of the transition model. By combining the kernel representation of the value function, we design an efficient policy iteration algorithm whose policy evaluation step can be represented as a linear system of equations characterized by a finite set of supporting states. We first validate the proposed method through extensive simulations in 2 D obstacle avoidance and 2.5 D terrain navigation problems. The results show that the proposed approach leads to a much superior performance over several baselines. We then develop a system that integrates our decision-making framework with onboard perception and conduct real-world experiments in both cluttered indoor and unstructured outdoor environments. The results from the physical systems further demonstrate the applicability of our method in challenging real-world environments.
Sentiment Analysis of Microblogging Dataset on Coronavirus Pandemic

Sentiment analysis can largely influence the people to get the update of the current situation. Coronavirus (COVID-19) is a contagious illness caused by the SARS-CoV-2 virus that causes severe respiratory symptoms. The lives of millions have continued to be affected by this pandemic, several countries have resorted to a full lockdown. During this lockdown, people have taken social networks to express their emotions to find a way to calm themselves down. People are spreading their sentiments through microblogging websites as one of the most preventive steps of this disease is the socialization to gain people’s awareness to stay home and keep their distance when they are outside home. Twitter is a popular online social media platform for exchanging ideas. People can post their different sentiments, which can be used to aware people. But, some people want to spread fake news to frighten the people. So, it is necessary to identify the positive, negative, and neutral thoughts so that the positive opinions can be delivered to the mass people for spreading awareness to the people. Moreover, a huge volume of data is floating on Twitter. So, it is also important to identify the context of the dataset. In this paper, we have analyzed the twitter dataset for evaluating the sentiment using several machine learning algorithms, where the random forest algorithm achieved the highest accuracy of 93%. Later, we have found out the context learning of the dataset based on the sentiments.
The Hierarchical Subspace Iteration Method for Laplace–Beltrami Eigenproblems

Sparse eigenproblems are important for various applications in computer graphics. The spectrum and eigenfunctions of the Laplace–Beltrami operator, for example, are fundamental for methods in shape analysis and mesh processing. The Subspace Iteration Method is a robust solver for these problems. In practice, however, Lanczos schemes are often faster. In this article, we introduce the Hierarchical Subspace Iteration Method (HSIM), a novel solver for sparse eigenproblems that operates on a hierarchy of nested vector spaces. The hierarchy is constructed such that on the coarsest space all eigenpairs can be computed with a dense eigensolver. HSIM uses these eigenpairs as initialization and iterates from coarse to fine over the hierarchy. On each level, subspace iterations, initialized with the solution from the previous level, are used to approximate the eigenpairs. This approach substantially reduces the number of iterations needed on the finest grid compared to the non-hierarchical Subspace Iteration Method. Our experiments show that HSIM can solve Laplace–Beltrami eigenproblems on meshes faster than state-of-the-art methods based on Lanczos iterations, preconditioned conjugate gradients, and subspace iterations.
LeMMINGs IV: The X-ray properties of a statistically-complete sample of the nuclei in active and inactive galaxies from the Palomar sample

All 280 of the statistically-complete Palomar sample of nearby (<120Mpc) galaxies δ >20◦ have been observed at 1.5GHz as part of the LeMMINGs e-MERLIN legacy survey. Here, we present Chandra X-ray observations of the nuclei of 213 of these galaxies, including a statistically-complete sub-set of 113 galaxies in the declination range 40◦ < δ < 65◦. We observed galaxies of all optical spectral types, including ‘active’ galaxies (e.g., LINERs and Seyferts) and ‘inactive’ galaxies like H ii galaxies and absorption line galaxies (ALG). The X-ray flux limit of our survey is 1.65×10−14 erg s−1 cm−2 (0.3−10 keV). We detect X-ray emission coincident within 2-arcsec of the nucleus in 150/213 galaxies, including 13/14 Seyferts, 68/77 LINERs, 13/22 ALGs and 56/100 H ii galaxies, but cannot completely rule out contamination from non-AGN processes in sources with nuclear luminosities < ∼ 10 39 erg s−1. We construct an X-ray Luminosity function (XLF) and find that the local galaxy XLF, when including all AGN types, can be represented as a single power-law of slope −0.54 ± 0.06. The Eddington ratio of the Seyferts is usually 2−4 decades higher than that of the LINERs, ALGs and H ii galaxies, which are mostly detected with Eddington ratios < ∼ 10 −3. Using [O iii] line measurements and BH masses from the literature, we show that LINERs, H ii galaxies and ALGs follow similar correlations to low luminosities, suggesting that some ‘inactive’ galaxies may harbour AGN.
The "gain-lever" effect in InGaAsP/InP multiple quantum well lasers

The gain-lever-effect has been assessed in both multiple quantum well (MQW) and bulk active region lasers having a range of lengths and split ratios in the top contact. Compared with a conventional single-contact laser, a 500-/spl mu/m MQW FP device with a top-contact split ratio of 8:1 exhibited >15-dB improvement in AM efficiency and a signal-to-noise ratio improvement of 7.5 dB. With proper impedance matching, these figures may be improved. A reduced noise figure is obtained at the expense of dynamic range with an additional nonlinearity seen experimentally due to thermal and carrier leakage when compared with simulations carried out using a harmonic balance model. Used carefully, these gain-lever lasers are useful components for future analogue fiber-optic systems. >
Priority Index of Production Equipment: A DEA-Based Multi-Criteria Approach to Setting Investment Priorities for Industrial Production Processes

The aim of this paper is to propose and demonstrate the use of an index for ranking investment in production equipment within manufacturing companies. The construction of the index takes into account multiple criteria and relies on a data envelopment analytical model; some of those criteria are the environmental impacts and product quality. An application to a major Cuban heat interchange battery company is included in the paper in order to better illustrate the use of the proposed tool.
Space weather : its impact on Earth and implications for business

Space weather describes disturbances that occur in near-Earth space, which can disrupt modern technologies. It is a natural hazard to which human civilisation has become vulnerable, through our use of advanced technologies. Businesses are exposed to these new risks whenever they adopt new technologies that are vulnerable to space weather. So, it is important to understand and assess these risks and weigh them against the benefits of new technologies. This brochure seeks to brief the business community on these issues – it first outlines the range of different space weather phenomena and discusses their impacts on a range of technologies. It then explores how businesses can respond to these impacts – by improving the resilience of the technologies and operating procedures used by businesses and by seeking opportunities to provide services that help other business deal with the problems caused by space weather.
Voltage amplification in switched-capacitor networks

The letter deals with the voltage amplification property of networks containing only capacitors and switches. It is shown that with any given n capacitors a circuit comprising a finite number of switches may be realised for which a voltage amplification close to 2n is achievable. If there is freedom in choosing the capacitors, then a switching circuit may be designed for which voltage amplification close to 22n−2+2n−1 is achievable. It is conjectured that this number presents the upper bound of voltage amplification achievable by means of an SC circuit with n capacitors.
Combined Noninvasive Respiratory Support Therapies to Treat COVID-19

BACKGROUND: The roles of high-flow nasal cannula (HFNC) and CPAP in coronavirus disease 2019 (COVID-19) are controversial. The objective of the study was to evaluate the impact of the application of a noninvasive respiratory support algorithm on clinical outcomes in subjects with COVID-19 and with acute respiratory failure. METHODS: We performed a single-center prospective observational study of subjects with respiratory failure from COVID-19 managed with HFNC and with CPAP plus HFNC (combined therapy). The main outcome was the intubation rate, which defined failure of therapy. We also analyzed the role of the ROX index ([ SpO2/ FIO2]/breathing frequency) to predict the need for intubation. RESULTS: From June to December 2020, 113 subjects with COVID-19 respiratory failure were admitted to our respiratory intermediate care unit. HFNC was applied in 65 subjects (57.52%) and combined therapy in 48 subjects (42.47%). A total of 83 subjects (73.45%) were successfully treated with noninvasive respiratory support. The intubation rate was 26.54%, and the overall mortality rate was 14.15%. The mortality rate in subjects who were intubated was 55.2%. An ROX index of 6.28 at 12 h predicted noninvasive respiratory support failure, with 97.6% sensitivity and 51.8% specificity. CONCLUSIONS: Data from our cohort managed in a respiratory intermediate care unit showed that combined noninvasive respiratory support was feasible, with favorable outcomes. Further prospective studies are required.
1KT: A Low-Cost 1000-Node Low-Power Wireless IoT Testbed

Testbeds remain indispensable instruments for experimentally evaluating IoT-oriented low-power wireless networking solutions. With the evolution of the field, they are increasingly expected to match envisioned deployment conditions of such solutions, notably in terms of scale. However, large-scale testbeds are scarce, likely because they have been believed to be expensive. This paper argues that this belief need no longer be justified by presenting the architecture and basic properties of 1KT, our new smart-building IoT testbed for solutions utilizing IEEE 802.15. 4 and Bluetooth Low Energy. It comprises 1000 experimental devices deployed directly in human spaces of 168 rooms on all 5 floors of a sizable building. At the same time, its cost is relatively low considering the scale.
Marble: collaborative scheduling of batteryless sensors with meta reinforcement learning

Batteryless energy-harvesting sensing systems are attractive for low maintenance but face challenges in real-world applications due to the low quality of service from sporadic and unpredictable energy availability. To overcome this challenge, recent data-driven energy management techniques optimize energy usage to maximize application performance even in low harvest scenarios by learning energy availability patterns in the environment. These techniques require prior knowledge of the environment in which the sensor nodes are deployed to work correctly. In the absence of historical data, the application performance deteriorates. We present an approach that leverages meta reinforcement learning to increase the application performance of newly deployed batteryless sensor nodes without historical data. Our system, called Marble, exploits information from other sensor node locations to expedite the learning of newly deployed sensor nodes, and improves the application performance in the initial days of deployment. For more details, we refer readers to the full paper of Marble [5].
Optimization algorithm for omic data subspace clustering

Subspace clustering identifies multiple feature subspaces embedded in a dataset together with the underlying sample clusters. When applied to omic data, subspace clustering is a challenging task, as additional problems have to be addressed: the curse of dimensionality, the imperfect data quality and cluster separation, the presence of multiple subspaces representative of divergent views of the dataset, and the lack of consensus on the best clustering method. First, we propose a computational method (discover) to perform subspace clustering on tabular high dimensional data by maximizing the internal clustering score (i.e. cluster compactness) of feature subspaces. Our algorithm can be used in both unsupervised and semi-supervised settings. Secondly, by applying our method to a large set of omic datasets (i.e. microarray, bulk RNA-seq, scRNA-seq), we show that the subspace corresponding to the provided ground truth annotations is rarely the most compact one, as assumed by the methods maximizing the internal quality of clusters. Our results highlight the difficulty of fully validating subspace clusters (justified by the lack of feature annotations). Tested on identifying the ground-truth subspace, our method compared favorably with competing techniques on all datasets. Finally, we propose a suite of techniques to interpret the clustering results biologically in the absence of annotations. We demonstrate that subspace clustering can provide biologically meaningful sample-wise and feature-wise information, typically missed by traditional methods. CCS Concepts: • Computing methodologies → Genetic algorithms; Mixture models; Cluster analysis. ACM Reference Format Madalina Ciortan and Matthieu Defrance. 2021. Optimization algorithm for omic data subspace clustering. 1, 1 (September 2021), 40 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn
CURRENT STATE OF THE ART POS TAGGING FOR INDIAN LANGUAGES - A STUDY

Parts-of-speech (POS) tagging is the basic building block of any Natural Language Processing (NLP) tool. A POS tagger has many applications. Especially for Indian languages, POS tagging adds many more dimensions as most of them are agglutinative, morphologically very rich highly inflected and are sometimes diglossic. Taggers have been developed using linguistic rules, stochastic models or both. This paper is a survey about different POS taggers developed for eight Indian Language, namely Hindi, Bengali, Tamil, Telugu, Gujarati, Malayalam, Manipuri and Assamese in the recent past.
Determining multi-valued input-output characteristics in the circuits containing bipolar transistors

The paper deals with the DC circuits including bipolar transistors represented by the Ebers-Moll model. An important question, how to determine specific multi-valued input-output characteristics, is considered. A general approach for finding the characteristics in nonlinear circuits, based on some known ideas, is developed in this paper. Unfortunately, this approach is not effective in a case of transistor circuits due to particular properties of the functions, which describe the bipolar transistors. The paper shows how to overcome these difficulties and make the approach effective. For this purpose, a transformation is suggested to obtain a circuit representation with suitable nonlinear functions. Next, an improvement is made for solving scalar nonlinear equations arising in the computation process. As a result we obtain an effective and useful tool for determining the characteristics. It is illustrated via a numerical example.
MRI Prognostic Factors in Multiple Sclerosis, Neuromyelitis Optica Spectrum Disorder, and Myelin Oligodendrocyte Antibody Disease

Several MRI measures have been developed in the last couple of decades, providing a number of imaging biomarkers that can capture the complexity of the pathological processes occurring in multiple sclerosis (MS) brains. Such measures have provided more specific information on the heterogeneous pathologic substrate of MS-related tissue damage, being able to detect, and quantify the evolution of structural changes both within and outside focal lesions. In clinical practise, MRI is increasingly used in the MS field to help to assess patients during follow-up, guide treatment decisions and, importantly, predict the disease course. Moreover, the process of identifying new effective therapies for MS patients has been supported by the use of serial MRI examinations in order to sensitively detect the sub-clinical effects of disease-modifying treatments at an earlier stage than is possible using measures based on clinical disease activity. However, despite this has been largely demonstrated in the relapsing forms of MS, a poor understanding of the underlying pathologic mechanisms leading to either progression or tissue repair in MS as well as the lack of sensitive outcome measures for the progressive phases of the disease and repair therapies makes the development of effective treatments a big challenge. Finally, the role of MRI biomarkers in the monitoring of disease activity and the assessment of treatment response in other inflammatory demyelinating diseases of the central nervous system, such as neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte antibody disease (MOGAD) is still marginal, and advanced MRI studies have shown conflicting results. Against this background, this review focused on recently developed MRI measures, which were sensitive to pathological changes, and that could best contribute in the future to provide prognostic information and monitor patients with MS and other inflammatory demyelinating diseases, in particular, NMOSD and MOGAD.