Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of granulation and supergranulation on Earth-mass planet detectability in the habitable zone around F6-K4 stars

Context. The detectability of exoplanets and the determination of their projected mass in radial velocity are affected by stellar magnetic activity and photospheric dynamics. Among those processes, the effect of granulation, and even more so of supergranulation, has been shown to be significant in the solar case. The impact for other spectral types has not yet been characterised. Aims. Our study is aimed at quantifying the impact of these flows for other stars and estimating how such contributions affect their performance. Methods. We analysed a broad array of extended synthetic time series that model these processes to characterise the impact of these flows on exoplanet detection for main sequence stars with spectral types from F6 to K4. We focussed on Earth-mass planets orbiting within the habitable zone around those stars. We estimated the expected detection rates and detection limits, tested the tools that are typically applied to such observations, and performed blind tests. Results. We find that both granulation and supergranulation on these stars significantly affect planet mass characterisation in radial velocity when performing a follow-up of a transit detection: the uncertainties on these masses are sometimes below 20% for a 1 MEarth (for granulation alone or for low-mass stars), but they are much larger in other configurations (supergranulation, high-mass stars). For granulation and low levels of supergranulation, the detection rates are good for K and late G stars (if the number of points is large enough), but poor for more massive stars. The highest level of supergranulation leads to a very poor performance, even for K stars; this is both due to low detection rates and to high levels of false positives, even for a very dense temporal sampling over 10 yr. False positive levels estimated from standard false alarm probabilities sometimes significantly overestimate or underestimate the true level, depending on the number of points: it is, therefore, crucial to take this effect into account when analysing observations. Conclusions. We conclude that granulation and supergranulation significantly affect the performance of exoplanet detectability. Future works will focus on improving the following three aspects: decreasing the number of false positives, increasing detection rates, and improving the false alarm probability estimations from observations.
Luminous blue variable candidates in M31

We study five Luminous Blue Variable (LBV) candidates in the Andromeda galaxy and one more (MN112) in the Milky Way. We obtain the same-epoch near-infrared (NIR) and optical spectra on the 3.5-meter telescope at the Apache Point Observatory and on the 6-meter telescope of the SAO RAS. The candidates show typical LBV features in their spectra: broad and strong hydrogen lines, HeI, FeII, and [FeII] lines. We estimate the temperatures, reddening, radii and luminosities of the stars using their spectral energy distributions. Bolometric luminosities of the candidates are similar to those of known LBV stars in the Andromeda galaxy. One candidate, J004341.84+411112.0, demonstrates photometric variability (about 0.27 mag in V band), which allows us to classify it as a LBV. The star J004415.04+420156.2 shows characteristics typical for B[e]-supergiants. The star J004411.36+413257.2 is classified as FeII star. We confirm that the stars J004621.08+421308.2 and J004507.65+413740.8 are warm hypergiants. We for the first time obtain NIR spectrum of the Galactic LBV candidate MN112. We use both optical and NIR spectra of MN112 for comparison with similar stars in M31 and notice identical spectra and the same temperature in the J004341.84+411112.0. This allows us to confirm that MN112 is a LBV, which should show its brightness variability in longer time span observations.
Avoiding Negative Side Effects due to Incomplete Knowledge of AI Systems

Autonomous agents acting in the real-world often operate based on models that ignore certain aspects of the environment. The incompleteness of any given model – handcrafted or machine acquired – is inevitable due to practical limitations of any modeling technique for complex real-world settings. Due to the limited fidelity of its model, an agent’s actions may have unexpected, undesirable consequences during execution. Learning to recognize and avoid such negative side effects (NSEs) of an agent’s actions is critical to improve the safety and reliability of autonomous systems. Mitigating NSEs is an emerging research topic that is attracting increased attention due to the rapid growth in the deployment of AI systems and their broad societal impacts. This article provides a comprehensive overview of different forms of NSEs and the recent research efforts to address them. We identify key characteristics of NSEs, highlight the challenges in avoiding NSEs, and discuss recently developed approaches, contrasting their benefits and limitations. The article concludes with a discussion of open questions and suggestions for future research directions.
Hybrid quantum–classical optimization with cardinality constraints and applications to finance

In this work we develop tools to address combinatorial optimization problems with a cardinality constraint, in which only a subset of variables end up having nonzero values. Firstly, we introduce a new heuristic pruning method that iteratively discards variables through a hybrid quantum–classical optimization step. Secondly, we analyse the use of soft constraints in the form of ‘chemical potentials’ to control the number of non-zero variables. We illustrate the power of both techniques using the problem of index tracking, which aims to mimicking the performance of a financial index with a balanced subset of assets. We also compare the performance of different state-of-the-art quantum variational optimization algorithms in our pruning method.
Implicit Regularization in Matrix Sensing: A Geometric View Leads to Stronger Results

We may think of low-rank matrix sensing as a learning problem with infinitely many possible outcomes where the desired learning outcome is the unique low-rank matrix that agrees with the available data. To find this desired low-rank matrix, for example, nuclear norm explicitly biases the learning outcome towards being low-rank. In contrast, we establish here that simple gradient flow of the feasibility gap is implicitly biased towards low-rank outcomes and successfully solves the matrix sensing problem, provided that the initialization is low-rank and sufficiently close to the manifold of feasible outcomes. Compared to the state of the art, the geometric perspective adopted here leads to substantially stronger results.
Homogenization and hypocoercivity for Fokker–Planck equations driven by weakly compressible shear flows

We study the long-time dynamics of two-dimensional linear Fokker-Planck equations driven by a drift that can be decomposed in the sum of a large shear component and the gradient of a regular potential depending on one spatial variable. The problem can be interpreted as that of a passive scalar advected by a slightly compressible shear flow, and undergoing small diffusion. For the corresponding stochastic differential equation, we give explicit homogenization rates in terms of a family of time-scales depending on the parameter measuring the strength of the incompressible perturbation. This is achieved by exploiting an auxiliary Poisson problem, and by computing the related effective diffusion coefficients. Regarding the long-time behaviour of the solution of the Fokker-Planck equation, we provide explicit decay rates to the unique invariant measure by employing a quantitative version of the classical hypocoercivity scheme. From a fluid mechanics perspective, this turns out to be equivalent to quantifying the phenomenon of enhanced diffusion for slightly compressible shear flows.
ECONOMIC RELATIONSHIP BETWEEN ASIA AND EUROPE: CASE STUDIES OF SINGAPORE’S EXPORTS TO CHINA AND THE EU

Inter-regional trade negotiations between the EU and ASEAN have not met with much success since they were first tabled in the Asia-Europe Meeting (ASEM) in 1996. This has led to separate discussions between the EU and individual ASEAN member countries to establish bilateral free trade agreements (FTAs). Singapore was the first ASEAN country to formalise a bilateral FTA with the EU, initialled in 2014 as the EU-Singapore Free Trade Agreement (EUSFTA). At the same time, Singapore has also developed a close trade relationship with China by establishing a China-Singapore Free Trade Agreement (CSFTA) in 2008. Despite the obvious significance of the EUSFTA and CSFTA, there has been little systematic research on the export-growth nexus in Singapore. Thus, this paper examines whether Singapore’s exports to China and the EU are beneficial for its economic development. Empirical findings indicate that there is unidirectional causality between exports to China and economic development, consistent with the export-led economic development hypothesis. Nevertheless the findings also show that, in line with the neutrality hypothesis, there is an independent relationship between exports to the EU and economic development. These findings have considerable implications on Singapore’s international trade policy and practices.
BANK COMPETITION AND LIQUIDITY RISK: THE CASE OF BRICS COUNTRIES

This paper investigates the effect of bank competition on liquidity risk using evidence from Brazil, Russia, India, China and South Africa. The paper employs bank fixed effects to mitigate the concern of endogeneity and the results show that an increase in competition would result in a reduction in liquidity risk. From the perspective of policy makers in BRICS countries, this evidence implies that it is necessary to improve competition in banking sector for a more stable and safer financial markets.
Micropublishing during and after the COVID-19 era

In the COVID-19 situation, social and behavioral science evidence is accumulating rapidly through online data collection, but the options to share and publish this information are scarce. As a remedy, I recommend the adoption of micropublishing in the fields of social and behavioral sciences. While micropublishing has been gaining popularity, it is not yet widely accepted or utilized by existing academic journals. Greater implementation would improve the availability of data in the immediate COVID-19 era and establish a post-COVID-19 publishing methodology that could increase researcher and practitioner engagement in real time. I recommend micropublishing in a specific manner that bifurcates an experiment’s methodology or survey method from the subsequently published data based on that experiment protocol. Published findings could be presented in a series and edited as new data emerges. This publishing system promotes cumulative science. To provide a visual example that supports my argument, I created a demo journal with sample papers organized according to the structure I recommend. The demo journal has features—except a Digital Object Finder (DOI)—that make it possible to publish social and behavioral sciences research. It could be replicated for a newly established journal. Alternatively, existing journals could add a section dedicated to micropublication.
Echocardiographic Features in Patients with Critical Covid-19 Requiring Endotracheal Intubation

Abstract Aim: COVID-19 has changed the way that we manage severe cardiorespiratory insufficiency caused by severe compromise of both the lungs and the cardiovascular system. The aim of this report is to share our experience of the use of echocardiography in patients with critical COVID-19 requiring endotracheal intubation. Methods: We used transthoracic echocardiography for a rapid evaluation of the hemodynamic status and for monitoring patient’s response to therapy and ventilator management. This enabled us to identify cardiac involvement that was unpredictable and complex. Results: The main cardiac abnormalities found were hyperdynamic status, Tako-Tsubo syndrome, right ventricular dilatation and acute pulmonary hypertension, and myocardial injury. Conclusion: Use of echocardiography in patients with COVID-19 is useful for diagnostic purposes. It is also useful for pulmonary management and is particularly useful for cardiac management.
Measurement of Quantum Turbulence in Superfluid 4He

In this work we present measurements of quantum turbulence generated by an oscillating quartz tuning fork submerged in superfluid 4He at various temperatures under Tλ = 2.17K. The observed turbulent mode is quantitatively characterized by a vortex line density L, which was indirectly measured by the attenuation of second sound.
The ECoC Title for a Central European City: the Case of Pécs 2010

The paper analyses the culture-led economic regeneration of Pécs, the Hungarian city which has been awarded the title of European Capital of Culture (ECoC) in 2010. The aim is to shed light on its social and economic development over the past twenty-five years, by considering the culture-oriented transformation as implemented within the city and the role played by the project.
Designing Total User Experiences: design, value creation and physical space

The simple consumption view is probably a reminiscent from old-fashioned economic thinking where firms were supposed to produce in the meaning of creating value, while households were seen as mere consumers that added no value by itself. Such a view does not explain the activity of people, for instance that they constantly improve their homes and add to the value of individual products e.g. by (re) framing them in connection with other items, or putting them in a spatial context that is meaningful and practical for these people. A multitude of books and magazine articles on households, addressing how good practical sense and taste may form people’s homes in ways that add value to the “bundle of commodities” belonging to the households. An interesting insight may be taken from Christine Frederick (1912/1926) who argued for “scientific management” in the kitchen. Without pursuing that idea per se, it is interesting to note, that Christine Frederick clearly saw a potential for the consumers own value creation, of what we may call “prosumption”, Toffler (1980).
The Use of Collective Method for Improvement of Regression Modeling Stability

The collective methods of regression analysis are discussed in the paper as the tools to improve the mean discrepancy of regression modeling. The mean discrepancy in the paper is calculated not only by the space of multidimensional observations but also by the space of training sets of fixed size. It was shown that mean discrepancy may be represented as the sum of three component. The first one is irremovable “noise”, the second component is the mean squared deviation of mean regression function from conditional means of dependent variable in points of independent variables space, the third component is instability term that is the variance of regression functions on the space of training sets. It was shown that the using of simplest voting procedure by the initial set of regression methods allows to receive new regression model with instability component less than average instability component of the initial set . The large scale experiments with Monte-Carlo simulated data demonstrated that voting procedures really allow to improve regression performance and that the cause of such improvement is the decrease of instability of training.
A Controller with an Extended State Observer in Aerial Camera Position-angle System

As one of the main loads of aerial reconnaissance, aerial camera has the advantages of flexibility, high efficiency and so on. In this paper, a tracking differentiator and an extended state observer are designed to control the positionangle control system of aerial camera. The simulation result is achieved and the result is satisfactory. Keywords—aerial camera; tracking differentiator; extended state observer
Tensor Memories

The hippocampal memory indexing theory of Teyler and DiScenna is one of the leading theories of memory formation. The main idea is that high-dimensional latent representations are formed in the hierarchical sensory processing layers of the brain as response to sensory inputs, and that these representations are linked to indices in the hippocampal area, forming a basis for an episodic memory. We extend the hippocampal memory indexing theory by including a decoder that extracts explicit information in the form of semantic triples using latent representations of entities and predicates. We demonstrate that, if a tensor model is used for explicit decoding, a semantic memory can be derived from episodic memory by a marginalization operation. Thus, our model supports the assumption that semantic memory is derived from episodic memory and that both rely on the same latent representations of generalized entities.
Bifurcated Backbone Strategy for RGB-D Salient Object Detection

Multi-level feature fusion is a fundamental topic in computer vision. It has been exploited to detect, segment and classify objects at various scales. When multi-level features meet multi-modal cues, the optimal feature aggregation and multi-modal learning strategy become a hot potato. In this paper, we leverage the inherent multi-modal and multi-level nature of RGB-D salient object detection to devise a novel Bifurcated Backbone Strategy Network (BBS-Net). Our architecture, is simple, efficient, and backbone-independent. In particular, first, we propose to regroup the multi-level features into teacher and student features using a bifurcated backbone strategy (BBS). Second, we introduce a depth-enhanced module (DEM) to excavate informative depth cues from the channel and spatial views. Then, RGB and depth modalities are fused in a complementary way. Extensive experiments show that BBS-Net significantly outperforms 18 state-of-the-art (SOTA) models on eight challenging datasets under five evaluation measures, demonstrating the superiority of our approach (~4% improvement in S-measure $vs$ . the top-ranked model: DMRA). In addition, we provide a comprehensive analysis on the generalization ability of different RGB-D datasets and provide a powerful training set for future research. The complete algorithm, benchmark results, and post-processing toolbox are publicly available at https://github.com/zyjwuyan/BBS-Net.
Greyhound: Directed Greybox Wi-Fi Fuzzing

The recent rise in complex Wi-Fi vulnerabilities, such as KRACK and Dragonslayer, indicates the critical need for effective Wi-Fi protocol testing tools. In this article, we conceptualize, design and implement a directed fuzzing methodology named Greyhound that automatically tests the Wi-Fi client implementations against vulnerabilities such as crashes or non-compliant behaviors. Leveraging a holistic Wi-Fi protocol model, Greyhound directs the fuzzer in specific states of target Wi-Fi client. By exchanging mutated packets with a Wi-Fi client, Greyhound aims to induce the client to exhibit anomalous behaviors that badly deviate from Wi-Fi protocols. We have implemented Greyhound and evaluated it on a variety of real-world Wi-Fi clients, including smartphone, Raspberry Pi, IoT device microcontrollers and a medical device. Our evaluation indicates that Greyhound not only automatically discovers known vulnerabilities (including KRACK and Dragonslayer) that would require specialized verification otherwise, but, more importantly, it also has uncovered four new vulnerabilities in popular Wi-Fi client devices. All discovered vulnerabilities have been confirmed by manufacturers and they have been assigned three different common vulnerability exposure (CVE) IDs. We also win a bug bounty of 2,200 USD for discovering the security vulnerabilities. Furthermore, our evaluation with three existing Wi-Fi fuzz testing tools reveals that all such tools fail to discover any of the vulnerabilities (including crashes) uncovered by Greyhound. Last but not the least, we have deployed Greyhound to test the Wi-Fi client implementation on automotive head units. Greyhound automatically discovers KRACK, Dragonslayer and other anomalies in these Wi-Fi implementations. Such a real world try-out justifies the necessity and efficacy of Greyhound.
NATURAL-LANGUAGE CHAT & CONTROL HMI FOR MANUFACTURING SHOPFLOOR

The ability to instantly and successfully interact among technical engineers, management team and machines is vital to the productivity and efficiency of manufacturing shopfloor. Chat messenger with smart mobile has obvious advantages for shopfloor management in group communication, instant notification and remote control. This paper presents a natural-language chat & control HMI for manufacturing shopfloor. The system successfully realizes a smooth two-way communication between users and shopfloor machines. Users can access just-in-time information, receive instant notifications, and remotely control shopfloor machines. All relevant parties can communicate over shopfloor matters in a chat group. The system comprises of four core modules, i.e. Chat Messenger, Chat Service Engine, Control & Communication Engine and Local Command Service. Chat Messenger provides chatting user interface and group management dealing with end-user’s enquiries and notifications via natural language. Chat Service Engine and Control & Communication Engine are two cloud-based service modules, which process questionnaire logic and transmit relevant commands and data bi-directionally
OVOL1/2: Drivers of Epithelial Differentiation in Development, Disease and Reprogramming

OVOL proteins (OVOL1 and OVOL2), vertebrate homologs of Drosophila OVO, are critical regulators of epithelial lineage determination and differentiation during embryonic development in tissues such as kidney, skin, mammary epithelia, and testis. OVOL can inhibit epithelial-mesenchymal transition and/or can promote mesenchymal-epithelial transition. Moreover, they can regulate the stemness of cancer cells, thus playing an important role during cancer cell metastasis. Due to their central role in differentiation and maintenance of epithelial lineage, OVOL overexpression has been shown to be capable of reprogramming fibroblasts to epithelial cells. Here, we review the roles of OVOL-mediated epithelial differentiation across multiple contexts, including embryonic development, cancer progression, and cellular reprogramming.
Design and Implementation of High-speed Rail Body Blast Cleaning Equipment

Now, there are some defects in the industry's cleaning of high-speed rail cars and similar large welded parts. The design is based on the subject, considering the future development of the design, in view of the existing research on the basis of the jet shot blasting machine. Which focuses on solving the following two problems: 1. the process of high speed car body cleaning requires manual participation and low production efficiency. 2. nozzles need manual hand hold, the surface of the high speed car body is not completely cleaned, the quality can not be guaranteed. The above two problems have been successfully solved by the design of the high speed iron body shot peening machine. In view of the above problems, the design of special equipment for high-speed rail car body surface cleaning high-speed rail car body shot peening machine. The equipment has realized the high efficiency, high quality and low cost cleaning of the high speed car body. Compared to the previous manual operation, the equipment adopts mechanical arm specially designed to replace the manual, so the equipment for high speed rail vehicle and similar large welding parts cleaning to achieve a high degree of mechanization, automation, avoids the cleaning process of the artificial participation. At the same time no pit design makes the device has the advantages of low cost, easy maintenance. For the shot blasting room, the use of rock wool sandwich color steel plate reduces the noise pollution in factories and responds to the call of the state.