Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud Broker: A Systematic Mapping Study

— In a cloud environment, a cloud broker is an important entity that works as an independent middleware between cloud customers and providers to address issues and conduct negotiations related to satisfying both customer preferences and service provider profits. In recent years, researchers have published many articles which directly or indirectly address this research area. A systematic method is vital for extracting all search spaces (journals, conferences, and workshops) and primary studies (articles) conducted in the cloud broker field and then selecting some of the highest quality studies. An important part of a systematic review is its provision of an appropriate research method that can extract large volumes of related studies. The proposed systematic review includes a comprehensive three-tier search strategy (manual search, backward snowballing, and database search). The accuracy of the search methodology has been analyzed in terms of extracting related studies and collecting comprehensive and complete information in a supplementary file and also, the detailed explanation of the reviewing process is inserted in Appendix A. In the search methodology, qualitative criteria have been defined to select studies with the highest quality and the most relevance among all search spaces. Furthermore, to find studies related to the cloud broker field, some queries have been created that employ relevant keywords in that field. In the present study, out of 1,928 extracted search spaces, 171 search spaces have been selected based on the defined quality criteria. Then, 1,298 articles have been extracted from these 171 selected search spaces. As a result, 496 high-quality papers have been selected among the mentioned papers. The chosen papers were published in prestigious journals, conferences, and workshops from 2009 through 2019. In the current Systematic Mapping Study (SMS), eight research questions have been designed for the purpose of identifying information that is significant to the cloud broker field, such as the most critical and debated topics, existing trends and issues, active researchers and countries, commonly used techniques in building cloud brokers, evaluation methods, the amount of research conducted by year and the place of publication, and the most important active search spaces. This information has been extracted from 496 selected papers (their references are in Appendix B) and can provide a useful guide for research teams and developers interested in this field .
Deep Equal Risk Pricing of Financial Derivatives with Multiple Hedging Instruments

This paper studies the equal risk pricing (ERP) framework for the valuation of European financial derivatives. This option pricing approach is consistent with global trading strategies by setting the premium as the value such that the residual hedging risk of the long and short positions in the option are equal under optimal hedging. The ERP setup of Marzban et al. (2020) is considered where residual hedging risk is quantified with convex risk measures. The main objective of this paper is to assess through extensive numerical experiments the impact of including options as hedging instruments within the ERP framework. The reinforcement learning procedure developed in Carbonneau and Godin (2020), which relies on the deep hedging algorithm of Buehler et al. (2019b), is applied to numerically solve the global hedging problems by representing trading policies with neural networks. Among other findings, numerical results indicate that in the presence of jump risk, hedging long-term puts with shorter-term options entails a significant decrease of both equal risk prices and market incompleteness as compared to trading only the stock. Monte Carlo experiments demonstrate the potential of ERP as a fair valuation approach providing prices consistent with observable market prices. Analyses exhibit the ability of ERP to span a large interval of prices through the choice of convex risk measures which is close to encompass the variance-optimal premium.
Heterogeneous Interactions of ClONO2 and HCl with Sulfuric Acid Tetrahydrate: Implications for the Stratosphere

The reaction probabilities for ClONO2+H2O- HOCl + HNO3 and ClONO2+ HCl Cl2 +HNO3 have been investigated on sulfuric acid tetrahydrate (SAT, H2SO4-4H2O)surfaces at temperatures between 190 and 230 K and at reactant concentrations that are typical in the lower stratosphere, using a fast-flow reactor coupled to a quadrupole mass spectrometer. The results indicate that the reaction probabilities as well as HCl uptake depend strongly on the thermodynamic state of SAT surface: they decrease significantly with decreasing H2O partial pressure at a given temperature, and decrease with increasing temperature at a given H2O partial pressure, as the SAT changes from the H2O-rich form to the H2SO4-rich form. For H2O-rich SAT at 195 K gamma(sub 1) approx. = -0.01 and gamma(sub 2) greater or equal to 0.1, whereas the values for H2SO4-rich SAT decrease by more than 2 orders of magnitude. At low concentrations of HCl, close to those found in the stratosphere, the amount of HCl taken up by H2O-rich SAT films corresponds to a coverage of the order of a tenth of a monolayer (approx. = 10(exp 14) molecules/sq cm); H2SO4-rich SAT films take up 2 orders of magnitude less HCl (les than 10(exp 12) molecules/sq cm). Substantial HCl uptake at high HCl concentrations is also observed, as a result of surface melting. The data reveal that frozen stratospheric sulfate aerosols may play an important role in chlorine activation in the winter polar stratosphere via processes similar to those occurring on the surfaces of polar stratospheric cloud particles.
A Wells type exact sequence for non-degenerate unitary solutions of the Yang–Baxter equation

Cycle sets are known to give non-degenerate unitary solutions of the Yang--Baxter equation and linear cycle sets are enriched versions of these algebraic systems. The paper explores the recently developed cohomology and extension theory for linear cycle sets. We derive a four term exact sequence relating 1-cocycles, second cohomology and certain groups of automorphisms arising from central extensions of linear cycle sets. This is an analogue of a similar exact sequence for group extensions known due to Wells. We also compare the exact sequence for linear cycle sets with that for their underlying abelian groups via the forgetful functor and discuss generalities on dynamical 2-cocycles.
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning

We study reinforcement learning (RL) with no-reward demonstrations, a setting in which an RL agent has access to additional data from the interaction of other agents with the same environment. However, it has no access to the rewards or goals of these agents, and their objectives and levels of expertise may vary widely. These assumptions are common in multi-agent settings, such as autonomous driving. To effectively use this data, we turn to the framework of successor features. This allows us to disentangle shared features and dynamics of the environment from agent-specific rewards and policies. We propose a multi-task inverse reinforcement learning (IRL) algorithm, called \emph{inverse temporal difference learning} (ITD), that learns shared state features, alongside per-agent successor features and preference vectors, purely from demonstrations without reward labels. We further show how to seamlessly integrate ITD with learning from online environment interactions, arriving at a novel algorithm for reinforcement learning with demonstrations, called $\Psi \Phi$-learning (pronounced `Sci-Fi'). We provide empirical evidence for the effectiveness of $\Psi \Phi$-learning as a method for improving RL, IRL, imitation, and few-shot transfer, and derive worst-case bounds for its performance in zero-shot transfer to new tasks.
FM2: Field-matrixed Factorization Machines for Recommender Systems

Click-through rate (CTR) prediction plays a critical role in recommender systems and online advertising. The data used in these applications are multi-field categorical data, where each feature belongs to one field. Field information is proved to be important and there are several works considering fields in their models. In this paper, we proposed a novel approach to model the field information effectively and efficiently. The proposed approach is a direct improvement of FwFM, and is named Field-matrixed Factorization Machines (FmFM, or FM2). We also proposed a new explanation of FM and FwFM within the FmFM framework, and compared it with the FFM. Besides pruning the cross terms, our model supports field-specific variable dimensions of embedding vectors, which acts as a soft pruning. We also proposed an efficient way to minimize the dimension while keeping the model performance. The FmFM model can also be optimized further by caching the intermediate vectors, and it only takes thousands of floating-point operations (FLOPs) to make a prediction. Our experiment results show that it can out-perform the FFM, which is more complex. The FmFM model’s performance is also comparable to DNN models which require much more FLOPs in runtime.
Self-Tuning for Data-Efficient Deep Learning

Deep learning has made revolutionary advances to diverse applications in the presence of large-scale labeled datasets. However, it is prohibitively time-costly and labor-expensive to collect sufficient labeled data in most realistic scenarios. To mitigate the requirement for labeled data, semi-supervised learning (SSL) focuses on simultaneously exploring both labeled and unlabeled data, while transfer learning (TL) popularizes a favorable practice of fine-tuning a pre-trained model to the target data. A dilemma is thus encountered: Without a decent pre-trained model to provide an implicit regularization, SSL through self-training from scratch will be easily misled by inaccurate pseudo-labels, especially in large-sized label space; Without exploring the intrinsic structure of unlabeled data, TL through fine-tuning from limited labeled data is at risk of under-transfer caused by model shift. To escape from this dilemma, we present Self-Tuning to enable data-efficient deep learning by unifying the exploration of labeled and unlabeled data and the transfer of a pre-trained model, as well as a Pseudo Group Contrast (PGC) mechanism to mitigate the reliance on pseudo-labels and boost the tolerance to false labels. Self-Tuning outperforms its SSL and TL counterparts on five tasks by sharp margins, e.g. it doubles the accuracy of fine-tuning on Cars with 15% labels.
ConCrete MAP: Learning a Probabilistic Relaxation of Discrete Variables for Soft Estimation with Low Complexity

Following the great success of Machine Learning (ML), especially Deep Neural Networks (DNNs), in many research domains in 2010s, several learning-based approaches were proposed for detection in large inverse linear problems, e.g., massive MIMO systems. The main motivation behind is that the complexity of Maximum A-Posteriori (MAP) detection grows exponentially with system dimensions. Instead of using DNNs, essentially being a black-box in its most basic form, we take a slightly different approach and introduce a probabilistic Continuous relaxation of disCrete variables to MAP detection. Enabling close approximation and continuous optimization, we derive an iterative detection algorithm: ConCrete MAP Detection (CMD). Furthermore, by extending CMD to the idea of deep unfolding, we allow for (online) optimization of a small number of parameters to different working points while limiting complexity. In contrast to recent DNN-based approaches, we select the optimization criterion and output of CMD based on information theory and are thus able to learn approximate probabilities of the individual optimal detector. This is crucial for soft decoding in today's communication systems. Numerical simulation results in MIMO systems reveal CMD to feature a promising performance complexity trade-off compared to SotA. Notably, we demonstrate CMD's soft outputs to be reliable for decoders.
Symmetric distinguishability as a quantum resource

We develop a resource theory of symmetric distinguishability, the fundamental objects of which are elementary quantum information sources, i.e. sources that emit one of two possible quantum states with given prior probabilities. Such a source can be represented by a classical-quantum state of a composite system XA, corresponding to an ensemble of two quantum states, with X being classical and A being quantum. We study the resource theory for two different classes of free operations: (i) CPTP A , which consists of quantum channels acting only on A, and (ii) conditional doubly stochastic maps acting on XA. We introduce the notion of symmetric distinguishability of an elementary source and prove that it is a monotone under both these classes of free operations. We study the tasks of distillation and dilution of symmetric distinguishability, both in the one-shot and asymptotic regimes. We prove that in the asymptotic regime, the optimal rate of converting one elementary source to another is equal to the ratio of their quantum Chernoff divergences, under both these classes of free operations. This imparts a new operational interpretation to the quantum Chernoff divergence. We also obtain interesting operational interpretations of the Thompson metric, in the context of the dilution of symmetric distinguishability.
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

With the broader and highly successful usage of machine learning (ML) in industry and the sciences, there has been a growing demand for explainable artificial intelligence (XAI). Interpretability and explanation methods for gaining a better understanding of the problem-solving abilities and strategies of nonlinear ML, in particular, deep neural networks, are, therefore, receiving increased attention. In this work, we aim to: 1) provide a timely overview of this active emerging field, with a focus on “post hoc” explanations, and explain its theoretical foundations; 2) put interpretability algorithms to a test both from a theory and comparative evaluation perspective using extensive simulations; 3) outline best practice aspects, i.e., how to best include interpretation methods into the standard usage of ML; and 4) demonstrate successful usage of XAI in a representative selection of application scenarios. Finally, we discuss challenges and possible future directions of this exciting foundational field of ML.
Sample Elicitation

It is important to collect credible training samples ( x, y ) for building data-intensive learning systems (e.g., a deep learning system). Asking people to report complex distribution p ( x ) , though theoretically viable, is challenging in practice. This is primarily due to the cognitive loads required for human agents to form the report of this highly complicated information. While classical elicitation mechanisms apply to eliciting a complex and generative (and continuous) distribution p ( x ) , we are interested in eliciting samples x i ∼ p ( x ) from agents directly. We coin the above problem sample elicitation . This paper introduces a deep learning aided method to incentivize credible sample contributions from self-interested and rational agents. We show that with an accurate estimation of a certain f -divergence function we can achieve approximate incentive compatibility in eliciting truthful samples. We then present an eﬃcient estimator with theoretical guarantees via studying the variational forms of the f -divergence function. We also show a connection be-tween this sample elicitation problem and f - GAN, and how this connection can help reconstruct an estimator of the distribution based on collected samples. Experiments on synthetic data, MNIST, and CIFAR-10 datasets demonstrate that our mechanism elicits truth-ful samples. Our implementation is available at https://github.com/weijiaheng/
Molecular features and gene expression signature of metastatic colorectal cancer

Uncontrollable metastatic outgrowth process is the leading cause of mortality worldwide, even in the case of colorectal cancer. Colorectal cancer (CRC) accounts for approximately 10% of all annually diagnosed cancers and 50% of CRC patients will develop metastases in the course of disease. Most patients with metastatic CRC have incurable disease. Even if patients undergo resection of liver metastases, the 5-year survival rate ranges from 25 to 58%. Next-generation sequencing of tumour specimens from large colorectal cancer patient cohorts has led to major advances in elucidating the genomic landscape of these tumours and paired metastases. The expression profiles of primary CRC and their metastatic lesions at both the gene and pathway levels were compared and led to the selection of early driver genes responsible for carcinogenesis and metastasis-specific genes that increased the metastatic process. The genetic, transcriptional and epigenetic alteration encoded by these genes and their combination influence many pivotal signalling pathways, enabling the dissemination and outgrowth in distant organs. Therapeutic regimens affecting several different active pathways may have important implications for therapeutic efficacy.
The complete mitochondrial genome of Labeo catla (Hamilton, 1822) using long read sequencing

Abstract Labeo catla is a widely cultured species in monoculture and polyculture systems of the Indian subcontinent. In this study, the complete mitochondrial genome sequence of catla was reconstructed from Oxford Nanopore sequence data. The mitochondrial genome is 16,600 bp in length (accession no. is MN830943) which is larger than the previously reported catla mitogenomes. Like other vertebrate mitochondrial genomes, it has 13 protein-coding genes, 22 tRNAs, 2 rRNAs and a putative control region. Most of the mitogenes are encoded on H-strand. Phylogenetic analysis showed that Labeo catla is more closely related to Labeo rohita than other labeo species. The catla mtgenome reported here will facilitate population genetics, phylogenetics and molecular taxonomy of Indian major carps.
Clinical Efficacy of Intravenous Immunoglobulins in Management of Toxic Shock Syndrome: An Updated Literature Review

Toxic shock syndrome (TSS) is an uncommon complication of infection caused by streptococci and staphylococci. It is associated with a high mortality rate. When evaluating patients with shock symptoms from skin or soft tissue sources, a high index of suspicion for TSS must be maintained. Prompt diagnosis and integrative management with surgical intervention, antibiotics, hemodynamic stabilization, and adjuvants like intravenous immunoglobulins improve survival.
The complete chloroplast genome of Lemmaphyllum intermedium, a valuable medicinal fern

Abstract Lemmaphyllum intermedium is a valuable medicinal fern in China. We have determined its complete chloroplast genome using Illumina paired-end sequencing. The genome is 157,795 bp in length with 132 genes, including 88 protein-coding genes, eight ribosomal RNA genes, 35 tRNA genes, and one pseudogene. It has a quadripartite structure consisting of a small single-copy (SSC) of 21,764 bp, a large single-copy (LSC) of 81,189 bp, and two inverted repeats (IRs) of 27,421 bp each. A maximum-likelihood phylogenetic tree reconstructed by using complete chloroplast genome sequences reveals that L. intermedium is closely related to Lemmaphyllum carnosum var. microphyllum with strong support.
Enhanced Comprehensive Learning Particle Swarm Optimization with Dimensional Independent and Adaptive Parameters

Comprehensive learning particle swarm optimization (CLPSO) and enhanced CLPSO (ECLPSO) are two literature metaheuristics for global optimization. ECLPSO significantly improves the exploitation and convergence performance of CLPSO by perturbation-based exploitation and adaptive learning probabilities. However, ECLPSO still cannot locate the global optimum or find a near-optimum solution for a number of problems. In this paper, we study further bettering the exploration performance of ECLPSO. We propose to assign an independent inertia weight and an independent acceleration coefficient corresponding to each dimension of the search space, as well as an independent learning probability for each particle on each dimension. Like ECLPSO, a normative interval bounded by the minimum and maximum personal best positions is determined with respect to each dimension in each generation. The dimensional independent maximum velocities, inertia weights, acceleration coefficients, and learning probabilities are proposed to be adaptively updated based on the dimensional normative intervals in order to facilitate exploration, exploitation, and convergence, particularly exploration. Our proposed metaheuristic, called adaptive CLPSO (ACLPSO), is evaluated on various benchmark functions. Experimental results demonstrate that the dimensional independent and adaptive maximum velocities, inertia weights, acceleration coefficients, and learning probabilities help to significantly mend ECLPSO's exploration performance, and ACLPSO is able to derive the global optimum or a near-optimum solution on all the benchmark functions for all the runs with parameters appropriately set.
Steady state and time-resolved IR spectroscopy of the native and unfolded states of bovine ubiquitin: protein stability and temperature-jump kinetic measurements of protein folding at low pH

Infrared spectroscopy has been used to characterise the folded and unfolded states of bovine ubiquitin (a small protein of 76 residues) under acidic conditions (pH ∼1); fast time-resolved measurements of protein unfolding, initiated by a laser-induced temperature-jump of ∼8 °C, shows rapid refolding and β-sheet secondary structure formation on a timescale of a few milliseconds.
The complete chloroplast genome sequence of Neopallasia pectinata (Asteraceae)

Abstract The complete chloroplast (cp) genome of Neopallasia pectinata was sequenced and analyzed in this study. It was 150,766 bp in length and has a typical circular structure, including a large single copy (LSC) with 82,605 bp, two inverted repeats (IRs) with 24,944 bp, and a small single copy (SSC) with 18,273 bp. The phylogenetic analysis of N. pectinata and its related taxa was conducted depended on the complete cp-genome sequences. The maximum likelihood tree indicates a close relationship between Chrysanthemum and Neopallasia. The cp-genome of N. pectinata is useful for future phylogenetic studies of Asteraceae.
Dental Management of a Patient with Special Health Care Needs

Patients with special health care needs (PSHCN) may have an increased risk of oral disease throughout the course of their life and require particular delivery of dental care due to their medical condition or limitations. The purpose is to report the dental management of a patient with Cornelia de Lange Syndrome (CdLS), which was classified as PSHCN due to physical, behavioural, cognitive, and emotional impairment. A 14-year-old female with a clinical diagnosis of CdLS and its common craniofacial features such as microcephaly, short neck, synophrys, arched eyebrows, downturned angle of the mouth, high arched palate, micrognathia, and microdontia was referred to the hospital where the dental treatment was performed under general anesthesia. Multiple tooth extraction, fillings, and coronary polishing were performed. During the follow-up, we observed that dental restorations were clinically satisfactory and there was an improvement in the patient's behaviour during dental treatment. Managing and shaping behaviour of such patients are crucial to delivering quality dental care, as they require specialized care due to their behavioural and clinical conditions.