Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergent constraints on climate sensitivities

Despite major advances in climate science over the last 30 years, persistent uncertainties in projections of future climate change remain. Climate projections are produced with increasingly complex models which attempt to represent key processes in the Earth system, including atmospheric and oceanic circulations, convection, clouds, snow, sea-ice, vegetation and interactions with the carbon cycle. Uncertainties in the representation of these processes feed through into a range of projections from the many state-of-the-art climate models now being developed and used worldwide. For example, despite major improvements in climate models, the range of equilibrium global warming due to doubling carbon dioxide still spans a range of more than three. Here we review a promising way to make use of the ensemble of climate models to reduce the uncertainties in the sensitivities of the real climate system. The emergent constraint approach uses the model ensemble to identify a relationship between an uncertain aspect of the future climate and an observable variation or trend in the contemporary climate. This review summarises previous published work on emergent constraints, and discusses the huge promise and potential dangers of the approach. Most importantly, it argues that emergent constraints should be based on well-founded physical principles such as the fluctuation-dissipation theorem. It is hoped that this review will stimulate physicists to contribute to the rapidly developing fi? eld of emergent constraints on climate projections, bringing to it much needed rigour and physical insights.
Intrusion Detection and identification System Design and Performance Evaluation for Industrial SCADA Networks

Industrial SCADA networks are subject to cyber-attacks that have the potential to cause significant disruption, damage, and havoc. In this paper, we present a study that proposes a three-stage classifier model which employs a machine learning algorithm to develop an intrusion detection and identification system for tens of different types of attacks against industrial SCADA networks. The machine learning classifier is trained and tested on the data generated using the laboratory prototype of a gas pipeline SCADA network. The dataset consists of three attack groups and seven different attack classes or categories. The same dataset further provides signatures of 35 different types of attacks which are related to those seven attack classes. The study entailed the design of three-stage machine learning classifier as a misuse intrusion detection system to detect and identify specifically each of the 35 attack types. The first stage of the classifier decides if a record is associated with normal operation or an attack signature. If the record is found to belong to an attack signature, then in the second stage, it is classified into one of seven attack classes. Based on the identified attack class as determined by the output from the second stage classifier, the attack record is provided for a third stage attack type classification, where seven different classifiers are employed. The output from the third stage classifier identifies the attack type to which the record belongs. Simulation results indicate that designs exploring specialization to domains or executing the classification in multiple stages versus single-stage designs are promising for problems where there are tens of classes. Comparison with studies in the literature also indicated that the multi-stage classifier performed markedly better.
On a Paley graph-type graph on $\mathbb{Z}_n$

Let $q$ be a prime power such that $q\equiv 1\pmod{4}$. The Paley graph of order $q$ is the graph with vertex set as the finite field $\mathbb{F}_q$ and edges defined as, $ab$ is an edge if and only if $a-b$ is a non-zero square in $\mathbb{F}_q$. We attempt to construct a similar graph of order $n$, where $n$ is a positive integer. For suitable $n$, we construct the graph where the vertex set is the finite commutative ring $\mathbb{Z}_n$ and edges defined as, $ab$ is an edge if and only if $a-b\equiv x^2\pmod{n}$ for some unit $x$ of $\mathbb{Z}_n$. We look at some properties of this graph. For primes $p\equiv 1\pmod{4}$, Evans, Pulham and Sheehan computed the number of complete subgraphs of order $4$ in the Paley graph of order $p$. Very recently, Dawsey and McCarthy find the number of complete subgraphs of order 4 in the generalized Paley graph of order $q$. In this article, we find the number of complete subgraphs of order 3 and 4 in our graph defined over $\mathbb{Z}_n$.
Model-free readout-error mitigation for quantum expectation values

Measurements on current quantum-hardware are subject to hardware imperfections that lead to readout-errors. These errors manifest themselves as a bias in quantum expectation values. Here, we propose a method to remove this bias from the expectation values of Pauli observables. No specific form of the noise is assumed, other than requiring that it is `weak'. We apply a method that forces the bias in the expectation value to appear as a multiplicative factor irrespective of the actual noise process. This factor can be measured directly and removed, at the cost of an increase in the sampling complexity for the observable. A bound relating the error in the expectation value to the sample complexity is derived.
Balanced Product Quantum Codes

This work provides the first explicit and non-random family of <inline-formula> <tex-math notation="LaTeX">$[[N,K,D]]$ </tex-math></inline-formula> LDPC quantum codes which encode <inline-formula> <tex-math notation="LaTeX">$K \in \Theta \left({N^{\frac {4}{5}}}\right)$ </tex-math></inline-formula> logical qubits with distance <inline-formula> <tex-math notation="LaTeX">$D \in \Omega \left({N^{\frac {3}{5}}}\right)$ </tex-math></inline-formula>. The family is constructed by amalgamating classical codes and Ramanujan graphs via an operation called <italic>balanced product</italic>. Recently, Hastings–Haah–O’Donnell and Panteleev–Kalachev were the first to show that there exist families of LDPC quantum codes which break the <inline-formula> <tex-math notation="LaTeX">$\mathrm {polylog}(N)\sqrt {N}$ </tex-math></inline-formula> distance barrier. However, their constructions are based on probabilistic arguments which only guarantee the code parameters with high probability whereas our bounds hold unconditionally. Further, balanced products allow for non-abelian twisting of the check matrices, leading to a construction of LDPC quantum codes that can be shown to have <inline-formula> <tex-math notation="LaTeX">$K\in \Theta (N)$ </tex-math></inline-formula> and that we conjecture to have linear distance <inline-formula> <tex-math notation="LaTeX">$D\in \Theta (N)$ </tex-math></inline-formula>.
Parity Check Codes for Second Order Diversity

Block codes are typically not used for fading channels as soft decision decoding is computationally intensive and hard decision decoding results in performance loss. In this paper we propose a diversity preserving hard decision decoding scheme for parity check codes (PCC) over Rayleigh fading channels. The proposed flip decoding scheme has linear complexity in the block length. Theoretical analysis and simulation results verify the correctness of the proposed detection scheme.
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting

Time series forecasting is an important yet challenging task. Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to improve the interpretability of time series models. Previous interpretation methods, including the methods for general neural networks and attention-based methods, mainly consider the interpretation in the feature dimension while ignoring the crucial temporal dimension. In this paper, we present the series saliency framework for temporal interpretation for multivariate time series forecasting, which considers the forecasting interpretation in both feature and temporal dimensions. By extracting the "series images" from the sliding windows of the time series, we apply the saliency map segmentation following the smallest destroying region principle. The series saliency framework can be employed to any well-defined deep learning models and works as a data augmentation to get more accurate forecasts. Experimental results on several real datasets demonstrate that our framework generates temporal interpretations for the time series forecasting task while produces accurate time series forecast.
A framework to semantify BPMN models using DEMO business transaction pattern

BPMN is a specification language widely used by industry and researchers for business process modeling and execution. It defines clearly how to articulate its concepts, but do not provide mechanism to represent the semantics of the produced models. This paper addresses the problem of how to improve the expressiveness of BPMN models, proposing a definition for the semantics of a business process within a BPMN model, and improving the completeness of the models in a systematic manner, so that models can describe far more situations with few extra managed complexity. We conceive a framework based on the business transaction patterns available in the enterprise ontology body of knowledge to prescribe the foundations of semantic BPMN models. A tool has been developed to automate the framework. Then, two industrial proof-of-concepts are used to measure its coverage, both positive and negative, and to argue about our proposal's usefulness. After that, the proposal is compared with others using a systematic literature review. A full BPMN pattern is proposed encompassing the happy flow, the declinations, the rejections and the revocations, without adding any new element to the BPMN specification. A software tool has been developed, and made publicly available, to support the automatic generation of the BPMN models from the proposed patterns. Our semantified BPMN pattern allowed the identification of a large amount of implicit, and other not implemented, situations in both proof-of-concepts. It is concluded that the usage of a semantic solution, grounded on a sound pattern, allows the systematic enrichment of the BPMN models with a bounded effort. Moreover, to simplify the BPMN executable models' implementation, its elements could be classified as implicit, explicit, or not implemented yet. Finally, related work indicates that this work is demanded, but no full solutions are available.
Procaine as a Sensing Material for Determination of Dysprosium(III) Ions in Presence of Other Rare‐earth Elements in Biological and Environmental Samples

Abstract The biological properties of the lanthanides, primarily based on their similarity to calcium, have been the basis for research into potential therapeutic applications of lanthanides since the early part of the twentieth century. In this research, procaine was used as a sensing material in construction of a new Dy(III) potentiometric membrane sensor. The sensor exhibits a Nernstian response over a concentration range of 1.0×10−6 mol l−1−1.0×10−1 mol l−1, with a detection limit of 7.9×10−7 mol L−1. The best performance was achieved with a membrane composition, consisting of 30% PVC, 63% dibutylphthalate (DBP), 5% procaine and 2% sodium tetraphenylborate (NaTPB). It was found that at the pH range of 4.0–8.0, the potential response of the sensor was not affected by the pH. Furthermore, the electrode presents satisfactory reproducibility, very fast response time (<10 s) and relatively good discriminating ability for Dy(III) ions with respect to many common cations and other lanthanide ions. The sensor has been applied to the determination of Dy(III) in human serum and in soil and sediment samples. Validation with Certified Reference Material (CRM) was also done.
Combating Mode Collapse in GAN training: An Empirical Analysis using Hessian Eigenvalues

Generative adversarial networks (GANs) provide state-of-the-art results in image generation. However, despite being so powerful, they still remain very challenging to train. This is in particular caused by their highly non-convex optimization space leading to a number of instabilities. Among them, mode collapse stands out as one of the most daunting ones. This undesirable event occurs when the model can only fit a few modes of the data distribution, while ignoring the majority of them. In this work, we combat mode collapse using second-order gradient information. To do so, we analyse the loss surface through its Hessian eigenvalues, and show that mode collapse is related to the convergence towards sharp minima. In particular, we observe how the eigenvalues of the $G$ are directly correlated with the occurrence of mode collapse. Finally, motivated by these findings, we design a new optimization algorithm called nudged-Adam (NuGAN) that uses spectral information to overcome mode collapse, leading to empirically more stable convergence properties.
Hardness of Learning Halfspaces with Massart Noise

We study the complexity of PAC learning halfspaces in the presence of Massart (bounded) noise. Specifically, given labeled examples $(x, y)$ from a distribution $D$ on $\mathbb{R}^{n} \times \{ \pm 1\}$ such that the marginal distribution on $x$ is arbitrary and the labels are generated by an unknown halfspace corrupted with Massart noise at rate $\eta<1/2$, we want to compute a hypothesis with small misclassification error. Characterizing the efficient learnability of halfspaces in the Massart model has remained a longstanding open problem in learning theory. Recent work gave a polynomial-time learning algorithm for this problem with error $\eta+\epsilon$. This error upper bound can be far from the information-theoretically optimal bound of $\mathrm{OPT}+\epsilon$. More recent work showed that {\em exact learning}, i.e., achieving error $\mathrm{OPT}+\epsilon$, is hard in the Statistical Query (SQ) model. In this work, we show that there is an exponential gap between the information-theoretically optimal error and the best error that can be achieved by a polynomial-time SQ algorithm. In particular, our lower bound implies that no efficient SQ algorithm can approximate the optimal error within any polynomial factor.
KwARG: parsimonious reconstruction of ancestral recombination graphs with recurrent mutation

The reconstruction of possible histories given a sample of genetic data in the presence of recombination and recurrent mutation is a challenging problem, but can provide key insights into the evolution of a population. We present KwARG, which implements a parsimony-based greedy heuristic algorithm for finding plausible genealogical histories (ancestral recombination graphs) that are minimal or near-minimal in the number of posited recombination and mutation events. Given an input dataset of aligned sequences, KwARG outputs a list of possible candidate solutions, each comprising a list of mutation and recombination events that could have generated the dataset; the relative proportion of recombinations and recurrent mutations in a solution can be controlled via specifying a set of ‘cost’ parameters. We demonstrate that the algorithm performs well when compared against existing methods. The software is made available on GitHub.
Checking Fact Worthiness using Sentence Embeddings

Checking and confirming factual information in texts and speeches is vital to determine the veracity and correctness of the factual statements. This work was previously done by journalists and other manual means but it is a time-consuming task. With the advancements in Information Retrieval and NLP, research in the area of Fact-checking is getting attention for automating it. CLEF-2018 and 2019 organised tasks related to Fact-checking and invited participants. This project focuses on CLEF-2019 Task-1 Check-Worthiness and experiments using the latest Sentence-BERT pre-trained embeddings, topic Modeling and sentiment score are performed. Evaluation metrics such as MAP, Mean Reciprocal Rank, Mean R-Precision and Mean Precision@N present the improvement in the results using the techniques.
A deep radio view of the evolution of the cosmic star-formation rate density from a stellar-mass selected sample in VLA-COSMOS.

We present the 1.4GHz radio luminosity functions (RLFs) of galaxies in the COSMOS field, measured above and below the $5\sigma$ detection threshold, using a Bayesian model-fitting technique. The radio flux-densities from VLA-COSMOS 3-GHz data, are extracted at the position of stellar mass-limited near-infrared (NIR) galaxies. We fit a local RLF model, which is a combination of active galactic nuclei (AGN) and star-forming galaxy (SFG), in 10 redshift bins with a pure luminosity evolution (PLE) model. We show that the evolution strength is similar to literature values up to $z\sim 1.6$. Beyond $z\sim 2$, we find that the SFG RLF exhibits a negative evolution ($L^*$ moves to lower luminosities) due to the decrease in low stellar-mass sources in our stellar mass-limited sample at high redshifts. From the RLF for SFGs, we determine the evolution in the cosmic star-formation-rate density (SFRD), which we find to be consistent with the established behaviour up to $z\sim 1$. Beyond $z\sim 1$ cosmic SFRD declines if one assumes an evolving infrared--radio correlation (IRRC), whereas it stays relatively higher if one adopts a constant IRRC. We find that the form of the relation between radio luminosity and SFR is therefore crucial in measuring the cosmic SFRD from radio data. We investigate the effects of stellar mass on the total RLF by splitting our sample into low ($10^{8.5} \leq M/\mathrm{M}_{\odot} \leq 10^{10}$) and high ($M>10^{10}\,\mathrm{M}_{\odot}$) stellar-mass subsets. We find that the SFRD is dominated by sources in the high stellar masses bin, at all redshifts.
Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning

Achieving efficiency gains in Chinese district heating networks, thereby reducing their carbon footprint, requires new optimal control methods going beyond current industry tools. Focusing on the secondary network, we propose a data-driven deep reinforcement learning (DRL) approach to address this task. We build a recurrent neural network, trained on simulated data, to predict the indoor temperatures. This model is then used to train two DRL agents, with or without expert guidance, for the optimal control of the supply water temperature. Our tests in a multi-apartment setting show that both agents can ensure a higher thermal comfort and at the same time a smaller energy cost, compared to an optimized baseline strategy.
Instantaneous vacuum for Dirac fields with a Yukawa interaction in cosmological space-times

We propose a suitable vacuum state for a quantized Dirac field interacting with a classical scalar field in a Friedman-Robertson-Walker-Lemaitre spacetime. This state is constructed from an extended version of the adiabatic expansion of the associated Fourier modes of the field. We compute the full renormalized stress-energy tensor. For any given initial time, this instantaneous vacuum can always be chosen to have vanishing Fourier modes for the renormalized stress-energy tensor. We check the consistency of the solution for several inflationary models.
Extending the Range of Drone-based Delivery Services by Exploration

Drones have a fairly short range due to their limited battery life. We propose an adaptive exploration techniques to extend the range of drones by taking advantage of physical structures such as tall buildings and trees in urban environments. Our goal is to extend the coverage of a drone delivery service by generating paths for a drone to reach its destination while learning about the energy consumption on each edge on its path in order to optimize its range for future missions. We evaluated the performance of our exploration strategy in finding the set of all reachable destinations in a city, and found that exploring locations near the boundary of the reachable sets according to the current energy model can speed up the learning process.
Solving large permutation flow-shop scheduling problems on GPU-accelerated supercomputers

Makespan minimization in permutation flow-shop scheduling is a well-known hard combinatorial optimization problem. Among the 120 standard benchmark instances proposed by E. Taillard in 1993, 23 have remained unsolved for almost three decades. In this paper, we present our attempts to solve these instances to optimality using parallel Branch-and-Bound tree search on the GPU-accelerated Jean Zay supercomputer. We report the exact solution of 11 previously unsolved problem instances and improved upper bounds for 8 instances. The solution of these problems requires both algorithmic improvements and leveraging the computing power of peta-scale high-performance computing platforms. The challenge consists in efficiently performing parallel depth-first traversal of a highly irregular, fine-grained search tree on distributed systems composed of hundreds of massively parallel accelerator devices and multi-core processors. We present and discuss the design and implementation of our permutation-based B&B and experimentally evaluate its parallel performance on up to 384 V100 GPUs (2 million CUDA cores) and 3840 CPU cores. The optimality proof for the largest solved instance requires about 64 CPU-years of computation -- using 256 GPUs and over 4 million parallel search agents, the traversal of the search tree is completed in 13 hours, exploring 339$\times 10^{12}$ nodes.
Elliptic modular graph forms I: Identities and generating series

Elliptic modular graph functions and forms (eMGFs) are defined for arbitrary graphs as natural generalizations of modular graph functions and forms obtained by including the character of an Abelian group in their Kronecker--Eisenstein series. The simplest examples of eMGFs are given by the Green function for a massless scalar field on the torus and the Zagier single-valued elliptic polylogarithms. More complicated eMGFs are produced by the non-separating degeneration of a higher genus surface to a genus one surface with punctures. eMGFs may equivalently be represented by multiple integrals over the torus of combinations of coefficients of the Kronecker--Eisenstein series, and may be assembled into generating series. These relations are exploited to derive holomorphic subgraph reduction formulas, as well as algebraic and differential identities between eMGFs and their generating series.
Online Algorithms with Advice

In online scenarios requests arrive over time, and each request must be serviced in an irrevocable manner before the next request arrives. Online algorithms with advice is an area of research where one attempts to measure how much knowledge of future requests is necessary to achieve a given performance level, as defined by the competitive ratio. When this knowledge, the advice, is obtainable, this leads to practical algorithms, called semi-online algorithms. On the other hand, each negative result gives robust results about the limitations of a broad range of semi-online algorithms. This survey explains the models for online algorithms with advice, motivates the study in general, presents some examples of the work that has been carried out, and includes an extensive set of references, organized by problem studied.