Miracle-dz/UniFAR-FTUs huggingface dataset:
SUPPNet: Neural network for stellar spectrum normalisation

Context. Precise continuum normalisation of merged échelle spectra is a demanding task that is necessary for various detailed spec- troscopic analyses. Automatic methods have limited e ﬀ ectiveness due to the variety of features present in the spectra of stars. This complexity often leads to the necessity for manual normalisation which is highly time-consuming. Aims. The aim of this work is to develop a fully automated normalisation tool that works with order-merged spectra and o ﬀ ers ﬂexible manual ﬁne-tuning, if necessary. Methods. The core of the proposed method uses the novel, fully convolutional deep neural network (SUPP Network) that was trained to predict a pseudo-continuum. The post-processing step uses smoothing splines that give access to regressed knots, which are useful for optional manual corrections. The active learning technique was applied to deal with possible biases that may arise from training with synthetic spectra and to extend the applicability of the proposed method to features absent in this kind of spectra. Results. The developed normalisation method was tested with high-resolution spectra of stars with spectral types from O to G, and gives a root mean squared (RMS) error over the set of test stars equal to 0.0128 in the spectral range from 3900Å to 7000Å and 0.0081 in the range from 4200Å to 7000Å. Experiments with synthetic spectra give a RMS of the order of 0.0050. Conclusions. The proposed method leads to results that are comparable to careful manual normalisation. Additionally, this approach is general and can be used in other ﬁelds of astronomy where background modelling or trend removal is a part of data processing.
Integrable and superintegrable extensions of the rational Calogero–Moser model in three dimensions

We consider a class of Hamiltonian systems in 3 degrees of freedom, with a particular type of quadratic integral and which includes the rational Calogero–Moser system as a particular case. For the general class, we introduce separation coordinates to find the general separable (and therefore Liouville integrable) system, with two quadratic integrals. This gives a coupling of the Calogero–Moser system with a large class of potentials, generalising the series of potentials which are separable in parabolic coordinates. Particular cases are superintegrable, including Kepler and a resonant oscillator. The initial calculations of the paper are concerned with the flat (Cartesian type) kinetic energy, but in section 5, we introduce a conformal factor φ to H and extend the two quadratic integrals to this case. All the previous results are generalised to this case. We then introduce some two and three dimensional symmetry algebras of the Kinetic energy (Killing vectors), which restrict the conformal factor. This enables us to reduce our systems from 3 to 2 degrees of freedom, giving rise to many interesting systems, including both Kepler type and Hénon–Heiles type potentials on a Darboux–Koenigs D 2 background.
Chloride film separation in garbage

This system already has Japanese Patent Application 2000-159699 using the blue light absorption, JPA2001-393262 using the thermal impedance and JPA 2003-42467 using the stress-strain diagram for the chloride film detection in the garbage. We, human beings have the three big ecological problems. The first is the nuclear waste in the northern part of the Japan Sea, the second is the irregular weather that is less oxygen caused by carbon fuels and the third is dioxins caused by the incineration of the chloride film and garbage. The investment and stopping cars solve the first and second problems. The third problem is required for the development of an automatic chloride film detector of the treatment for waste garbage plastics that is Sanuki RDF system against dioxins. The first method is detecting the plasticizer in the chloride films of garbage and is not suitable for heated films by the microwave cooker. The plasticizer goes into the cooked food. The second thermal impedance method detects PVDC, PVC, heated PVDC, heated PVC and non-chloride films.
KARL-Trans-NER: Knowledge Aware Representation Learning for Named Entity Recognition using Transformers

The inception of modeling contextual information using models such as BERT, ELMo, and Flair has significantly improved representation learning for words. It has also given SOTA results in almost every NLP task - Machine Translation, Text Summarization and Named Entity Recognition, to name a few. In this work, in addition to using these dominant context-aware representations, we propose a Knowledge Aware Representation Learning (KARL) Network for Named Entity Recognition (NER). We discuss the challenges of using existing methods in incorporating world knowledge for NER and show how our proposed methods could be leveraged to overcome those challenges. KARL is based on a Transformer Encoder that utilizes large knowledge bases represented as fact triplets, converts them to a graph context, and extracts essential entity information residing inside to generate contextualized triplet representation for feature augmentation. Experimental results show that the augmentation done using KARL can considerably boost the performance of our NER system and achieve significantly better results than existing approaches in the literature on three publicly available NER datasets, namely CoNLL 2003, CoNLL++, and OntoNotes v5. We also observe better generalization and application to a real-world setting from KARL on unseen entities.
Efficient and robust high-dimensional sparse logistic regression via nonlinear primal-dual hybrid gradient algorithms

Logistic regression is a widely used statistical model to describe the relationship between a binary response variable and predictor variables in data sets. It is often used in machine learning to identify important predictor variables. This task, variable selection, typically amounts to fitting a logistic regression model regularized by a convex combination of l1 and l 2 2 penalties. Since modern big data sets can contain hundreds of thousands to billions of predictor variables, variable selection methods depend on efficient and robust optimization algorithms to perform well. State-of-the-art algorithms for variable selection, however, were not traditionally designed to handle big data sets; they either scale poorly in size or are prone to produce unreliable numerical results. It therefore remains challenging to perform variable selection on big data sets without access to adequate and costly computational resources. In this paper, we propose a nonlinear primal-dual algorithm that addresses these shortcomings. Specifically, we propose an iterative algorithm that provably computes a solution to a logistic regression problem regularized by an elastic net penalty inO(T (m,n) log(1/ǫ)) operations, where ǫ ∈ (0, 1) denotes the tolerance and T (m,n) denotes the number of arithmetic operations required to perform matrix-vector multiplication on a data set with m samples each comprising n features. This result improves on the known complexity bound of O(min(mn,mn) log(1/ǫ)) for first-order optimization methods such as the classic primal-dual hybrid gradient or forward-backward splitting methods. Significance statement Logistic regression is a widely used statistical model to describe the relationship between a binary response variable and predictor variables in data sets. With the trends in big data, logistic regression is now commonly applied to data sets whose predictor variables range from hundreds of thousands to billions. State-of-the-art algorithms for fitting logistic regression models, however, were not traditionally designed to handle big data sets; they either scale poorly in size or are prone to produce unreliable numerical results. This paper proposes a nonlinear primal-dual algorithm that provably computes a solution to a logistic regression problem regularized by an elastic net penalty in O(T (m,n) log(1/ǫ)) operations, where ǫ ∈ (0, 1) denotes the tolerance and T (m,n) denotes the number of arithmetic operations required to perform matrix-vector multiplication on a data set with m samples each comprising n features. This result improves on the known complexity bound of O(min(m2n,mn2) log(1/ǫ)) for first-order optimization methods such as the classic primal-dual hybrid gradient or forward-backward splitting methods.
Dissipative Superradiant Spin Amplifier for Enhanced Quantum Sensing

Quantum metrology protocols exploiting ensembles of $N$ two-level systems and Ramsey-style measurements are ubiquitous. However, in many cases excess readout noise severely degrades the measurement sensitivity; in particular in sensors based on ensembles of solid-state defect spins. We present a dissipative"spin amplification"protocol that allows one to dramatically improve the sensitivity of such schemes, even in the presence of realistic intrinsic dissipation and noise. Our method is based on exploiting collective (i.e., superradiant) spin decay, an effect that is usually seen as a nuisance because it limits spin-squeezing protocols. We show that our approach can allow a system with a highly imperfect spin readout to approach SQL-like scaling in $N$ within a factor of two, without needing to change the actual readout mechanism. Our ideas are compatible with several state-of-the-art experimental platforms where an ensemble of solid-state spins (NV centers, SiV centers) is coupled to a common microwave or mechanical mode.
Roadmap on wavefront shaping and deep imaging in complex media

The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow us to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working in this field, which has revolutionized the prospect of diffraction-limited imaging at depth in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead.
Engineering strong beamsplitter interaction between bosonic modes via quantum optimal control theory

In continuous-variable quantum computing with qubits encoded in the inﬁnite-dimensional Hilbert space of bosonic modes, it is a diﬃcult task to realize strong and on-demand interactions between the qubits. One option is to engineer a beamsplitter interaction for photons in two superconducting cavities by driving an intermediate superconducting circuit with two continuous-wave drives, as demonstrated in a recent experiment [Gao et al. , Phys. Rev. X 8 , 021073 (2018)]. Here, we show how quantum optimal control theory (OCT) can be used in a systematic way to improve the beamsplitter interaction between the two cavities. We ﬁnd that replacing the two-tone protocol by a three-tone protocol accelerates the eﬀective beamsplitter rate between the two cavities. The third tone’s amplitude and frequency are determined by gradient-free optimization and make use of cavity-transmon sideband couplings. We show how to further improve the three-tone protocol via gradient-based optimization while keeping the optimized drives experimentally feasible. Our work exempliﬁes how to use OCT to systematically improve practical protocols in quantum information applications.
BuildFormer: Automatic building extraction with vision transformer

: Building extraction from fine-resolution remote sensing images plays a vital role in numerous geospatial applications, such as urban planning, population statistic, economic assessment and disaster management. With the advancement of deep learning technology, deep convolutional neural networks (DCNNs) have dominated the automatic building extraction task for many years. However, the local property of DCNNs limits the extraction of global information, weakening the ability of the network for recognizing the building instance. Recently, the Transformer comprises a hot topic in the computer vision domain and achieves state-of-the-art performance in fundamental vision tasks, such as image classification, semantic segmentation and object detection. Inspired by this, in this paper, we propose a novel transformer-based network for extracting buildings from fine-resolution remote sensing images, namely BuildFormer. In Comparision with the ResNet, the proposed method achieves an improvement of 2% in mIoU on the WHU building dataset.
Some New Perspectives for Introducing Human-Systems Integration into the System Development Process

As further background for this Special Issue, this paper introduces a sampling of the findings of a National Research Council Committee on Human-Systems Design Support for Changing Technology. The paper introduces the Incremental Commitment Model of system development that was designed to accommodate the needs of the human-system integration (HSI) community. The committee viewed effective integration of human-system issues as requiring (a) stakeholder satisficing—solutions that meet acceptability criteria of all stakeholders; (b) incremental growth of system definition and stakeholder commitment; (c) iterative and concurrent system definition and development; (d) HSI risk analysis and risk management in concert with that of the other engineering disciplines involved in a project; and (e) HSI outcomes or deliverables designed specifically to be shared and understood by the other stakeholders. As the committee members looked to the future, they envisioned a point when it would be possible to develop a fully integrated HSI development methodology. They also foresaw a time when HSI might become an independently recognized discipline and more HSI specialists would be qualified to lead project teams, placing the discipline in a more commanding role in the system development process.
Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding

Multi-view projection methods have demonstrated promising performance on 3D understanding tasks like 3D classification and segmentation. However, it remains unclear how to combine such multi-view methods with the widely available 3D point clouds. Previous methods use unlearned heuristics to combine features at the point level. To this end, we introduce the concept of the multi-view point cloud (Voint cloud), representing each 3D point as a set of features extracted from several view-points. This novel 3D Voint cloud representation combines the compactness of 3D point cloud representation with the natural view-awareness of multi-view representation. Naturally, we can equip this new representation with convolutional and pooling operations. We deploy a Voint neural network (VointNet) to learn representations in the Voint space. Our novel representation achieves \sota performance on 3D classification, shape retrieval, and robust 3D part segmentation on standard benchmarks ( ScanObjectNN, ShapeNet Core55, and ShapeNet Parts).
ATS: Adaptive Token Sampling For Efficient Vision Transformers

While state-of-the-art vision transformer models achieve promising results for image classiﬁcation, they are computationally very expensive and require many GFLOPs. Although the GFLOPs of a vision transformer can be decreased by reducing the number of tokens in the network, there is no setting that is optimal for all input images. In this work, we therefore introduce a differentiable parameter-free Adaptive Token Sampling (ATS) module, which can be plugged into any existing vision transformer architecture. ATS empowers vision transformers by scoring and adaptively sampling signiﬁcant tokens. As a result, the number of tokens is not anymore static but it varies for each input image. By integrating ATS as an additional layer within current transformer blocks, we can convert them into much more efﬁcient vision transformers with an adaptive number of tokens. Since ATS is a parameter-free module, it can be added to off-the-shelf pretrained vision transformers as a plug and play module, thus reducing their GFLOPs without any additional training. However, due to its differentiable design, one can also train a vision transformer equipped with ATS. We evaluate our module on the ImageNet dataset by adding it to multiple state-of-the-art vision transformers. Our evaluations show that the proposed module improves the state-of-the-art by reducing the computational cost (GFLOPs) by 37% while preserving the accuracy.
EDM 2011: 4th international conference on educational data mining : Eindhoven, July 6-8, 2011 : proceedings

Assessment on collaborative student behavior is a longstanding issue in user modeling. Nowadays thanks to the proliferation of online learning and the vast amount of data on students’ interactions this modeling issue features some alternatives. The purpose is not to depend on teachers or students assessments, which either requires management effort difficult to assume (due to some students-per-teacher ratios) or depends on individual motivations (i.e. student willingness on providing explicit feedback related to collaboration). In our research we have shown that based on frequent and regular analysis of those interactions it is feasible to obtain collaborative assessments that concurs with expert valorizations. This approach relies on data mining and machine learning techniques, which are applied to infer collaborative significant student’s characteristics such as regularity, in terms of activity and initiative, and student acknowledgment of fellow-students. The advantages of the approach are to obtain domain-independent assessments, applicable in different learning management systems and exploitable over different courses and learning settings without the teacher involvement in the analysis process. The method has been developed from a collaborative experience involving hundreds of students over 3 consecutive year-courses. Here we focus on discussing the improvements on measurements provided during a new collaboration learning experience of this academic year.
Analysis of an E-plane waveguide T-junction with a quarter-wave transformer using the overlapping T-block method and genetic algorithm

The scattering characteristics of an E-plane waveguide T-junction with a quarter-wave transformer are systematically analyzed using the overlapping T-block method. The structure of an E-plane waveguide T-junction with a quarter-wave transformer is divided into two overlapping T-blocks. The geometry of a quarter-wave transformer is optimised with a binary genetic algorithm. Numerical computations are performed to illustrate the rate of convergence and accuracy of a series solution.
EQC: ensembled quantum computing for variational quantum algorithms

Variational quantum algorithm (VQA), which is comprised of a classical optimizer and a parameterized quantum circuit, emerges as one of the most promising approaches for harvesting the power of quantum computers in the noisy intermediate scale quantum (NISQ) era. However, the deployment of VQAs on contemporary NISQ devices often faces considerable system and time-dependant noise and prohibitively slow training speeds. On the other hand, the expensive supporting resources and infrastructure make quantum computers extremely keen on high utilization. In this paper, we propose a virtualized way of building up a quantum backend for variational quantum algorithms: rather than relying on a single physical device which tends to introduce ever-changing device-specific noise with less reliable performance as time-since-calibration grows, we propose to constitute a quantum ensemble, which dynamically distributes quantum tasks asynchronously across a set of physical devices, and adjusts the ensemble configuration with respect to machine status. In addition to reduced machine-dependant noise, the ensemble can provide significant speedups for VQA training. With this idea, we build a novel VQA training framework called EQC - a distributed gradient-based processor-performance-aware optimization system - that comprises: (i) a system architecture for asynchronous parallel VQA cooperative training; (ii) an analytical model for assessing the quality of a circuit output concerning its architecture, transpilation, and runtime conditions; (iii) a weighting mechanism to adjust the quantum ensemble's computational contribution according to the systems' current performance. Evaluations comprising 500K times' circuit evaluations across 10 IBMQ NISQ devices using a VQE and a QAOA applications demonstrate that EQC can attain error rates very close to the most performant device of the ensemble, while boosting the training speed by 10.5X on average (up to 86X and at least 5.2x). EQC is available at https://github.com/pnnl/eqc.
Verifying Transactional Consistency of MongoDB

MongoDB is a popular general-purpose, document-oriented, distributed NoSQL database. It supports transactions in three different deployments: single-document transactions utilizing the WiredTiger storage engine in a standalone node, multi-document transactions in a replica set which consists of a primary node and several secondary nodes, and distributed transactions in a sharded cluster which is a group of multiple replica sets, among which data is sharded. A natural and fundamental question about MongoDB transactions is: What transactional consistency guarantee do MongoDB Transactions in each deployment provide? However, it lacks both concise pseudocode of MongoDB transactions in each deployment and formal specification of the consistency guarantees which MongoDB claimed to provide. In this work, we formally specify and verify the transactional consistency protocols of MongoDB. Specifically, we provide a concise pseudocode for the transactional consistency protocols in each MongoDB deployment, namely WIREDTIGER, REPLICASET, and SHARDEDCLUSTER, based on the official documents and source code. We then prove that WIREDTIGER, REPLICASET, and SHARDEDCLUSTER satisfy different variants of snapshot isolation, namely Strong-SI, Realtime-SI, and Session-SI, respectively. We also propose and evaluate efficient white-box checking algorithms for MongoDB transaction protocols against their consistency guarantees, effectively circumventing the NP-hard obstacle in theory.
PCA-based Category Encoder for Categorical to Numerical Variable Conversion

— Increasing the cardinality of categorical variables might decrease the overall performance of ML algorithms. This paper presents a novel computational preprocessing method to convert categorical to numerical variables for machine learning (ML) algorithms. In this method, We select and convert three categorical features to numerical features. First, we choose the threshold parameter based on the distribution of categories in variables. Then, we use conditional probabilities to convert each categorical variable into two new numerical variables, resulting in six new numerical variables in total. After that, we feed these six numerical variables to the Principal Component Analysis (PCA) algorithm. Next, we select the whole or partial numbers of Principal Components (PCs). Finally, by applying binary classification with ten different classifiers, We measured the performance of the new encoder and compared it with the other 17 well-known category encoders. The proposed technique achieved the highest performance related to accuracy and Area under thecurve ( AUC ) on high cardinality categorical variables using the well-known cybersecurity NSLKDD dataset. Also, we defined harmonic average metrics to find the best trade-off between train and test performance and prevent underfitting and overfitting. Ultimately, the number of newly created numerical variables is minimal. Consequently, this data reduction improves computational processing time which might reduce processing data in 5G future telecommunication networks