Miracle-dz/UniFAR-FTUs huggingface dataset:
Bank Aggregation, Competition and Bank Credit Risk

In this paper, I use generalized moment estimation to investigate the impact of banking competition and agglomeration on bank credit risk, based on the unbalanced panel data of China's banks from 2011 to 2020.The results show that there is a long-term and short-term dynamic correlation between bank competition and bank credit risk, there is a long-term significant negative correlation in the whole investigation period, but there is a nonlinear "U" correlation in the short term. Bank agglomeration and asset size also have a significant impact on bank credit risk. Therefore, the formulation of macro prudential regulatory policies should fully consider the competition and agglomeration of banks, strengthen the coordination of regulatory policies, and effectively deal with the accumulated risks of unbalanced development of the banking industry, so as to promote economic and financial stability.
Dynamic Spatial Spillover Effects of Financial Risk: A Comparative Analysis between the Subprime Crisis and the COVID-19 Crisis

The subprime crisis and the COVID-19 crisis have severely impacted the development of global financial stability, which arouses scholars' thinking about the financial risk. Clarifying the contagion mechanism and spatial spillover effect of financial risk under the subprime crisis and the COVID-19 crisis is of great significance for preventing and controlling financial risk. This paper explores and compares the main contagion mechanism, contagion channels, and spatial spillover effects of financial risk during the subprime crisis and the COVID-19 crisis by using the dynamic Spatial Dubin Model. The results show that the transnational contagion mechanism of financial risk during the subprime crisis is the monsoon effect. The inflation rate has a positive spatial spillover effect on financial risk. During the COVID-19 crisis, the transnational contagion mechanism of financial risk is the spillover effect. Net exports and stock market returns have positive spatial spillover effects on financial risk. Financial risk is mainly transmitted across borders through commodity prices during the subprime crisis, while during the COVID-19 crisis, it's through trade exchanges and financial capital flows. In addition, the spatial contagion intensity of financial risks during the COVID-19 crisis is higher than during the subprime crisis. This paper provides experience and reference for countries to formulate financial risk prevention plans.
Efficient Neural Style Transfer for Volumetric Simulations

Artistically controlling fluids has always been a challenging task. Recently, volumetric Neural Style Transfer (NST) techniques have been used to artistically manipulate smoke simulation data with 2D images. In this work, we revisit previous volumetric NST techniques for smoke, proposing a suite of upgrades that enable stylizations that are significantly faster, simpler, more controllable and less prone to artifacts. Moreover, the energy minimization solved by previous methods is camera dependent. To avoid that, a computationally expensive iterative optimization performed for multiple views sampled around the original simulation is needed, which can take up to several minutes per frame. We propose a simple feed-forward neural network architecture that is able to infer view-independent stylizations that are three orders of the magnitude faster than its optimization-based counterpart.
Talk, Listen and Keep Me Company: A Mixed Methods Analysis of Children’s Perspectives Towards Robot Reading Companions

The potential for robots as an education support tool is being rapidly realized. However, much of the existing research with education robots has involved studies which arbitrarily select robots for interventions without a foundational understanding of the features that make them best suited to serve and meet the expectations and needs of users. This study explored how children’s perceptions, expectations and experiences were shaped by aesthetic and functional features during interactions with three different, commercially-available robot ‘reading buddies’. We collected a range of quantitative and qualitative measures of subjective experience before and after children read a book with a robot of their choice. Overall, our findings indicated that social robots do indeed show strong potential to promote reading engagement in children. This was supported by robot features that signaled the perception of robots as intelligent, literate and attentive. Such features included the robot’s ability to speak and react to the story plot in a way that was both emotionally- and temporally-appropriate, so as to engage but not distract children when reading. As such, controlling the timing of robot animations during reading activities – either using human-control methods or automation – presents a key challenge in realizing the effective deployment of robots to promote reading engagement in children, particularly for those who experience reading difficulty and associated reading anxiety.
Older Adults’ Perception of the Furhat Robot

With more robots entering social environments, such as care homes for older adults, it is increasingly important to understand the target user groups’ perception of different robot platforms that are introduced into their immediate surroundings. To add to existing research on older adults’ attitudes toward robots after meeting a robot, and social acceptance toward certain robots, we conducted a short-term interaction study with the Furhat robot. Furhat, a blended embodiment of a physical robot head with a virtual (back-projected) face, is being marketed as one of the most social robots with high human-likeness. However, this study’s results indicates that older adults do not perceive Furhat as anthropomorphic or sentient, and they especially reported a negative attitude toward robots with emotions after meeting Furhat. Despite these scores, all participants were engaged in the interaction with the robot. However, our results differ from previous studies by indicating that older adults have relatively low social acceptance for Furhat and a relatively negative attitude toward robots after meeting Furhat.
Flexile: meeting bandwidth objectives almost always

Wide-area cloud provider networks must support the bandwidth requirements of network traffic despite failures. Existing traffic engineering (TE) schemes perform no better than an approach that optimally routes traffic for each failure scenario. We show that this results in sub-optimal routing decisions that hurt performance, and are potentially unfair to some traffic across scenarios. To tackle this, we develop Flexile, which exploits and discovers opportunities to improve network performance by prioritizing certain traffic in each failure state so that it can meet its bandwidth requirements. Flexile seeks to minimize a desired percentile of loss across all traffic flows, while modeling diverse needs of different traffic classes. To achieve this, Flexile consists of (i) an offline phase that identifies which failure states are critical for each flow; and (ii) an online phase, which on failure allocates bandwidth prioritizing critical flows for that failure state, while also judiciously allocating bandwidth to non-critical flows. For tractability, Flexile's offline phase uses a decomposition algorithm aided with problem-specific accelerations. Evaluations using real topologies, and validated with emulation testbed experiments, show that Flexile outperforms state-of-the-art TE schemes including SWAN, SMORE, and Teavar in reducing flow loss at desired percentiles by 46% or more in the median case.
Intersectoral Linkages in Booming Sector Economies

There is a general perception that the Gaming sector has contributed to the overall growth of Macao through the trickle-down effect, passing on the benefits of growth to other sectors. After the liberalization of the gaming industry in 2002, this booming sector experienced several years of exponential growth, becoming the driving industry for Macao's economy. Several scholars and researchers have dedicated their studies to the effects of the casino gaming industry as a booming sector in such a small economy. Most of these studies aimed to quantify economic growth indicators' evolution and point out the possible problems related to the sustainability of an economy dependent on just one pillar sector. However, there is a gap in what concerns the measurement of the influence of the Gaming sector as a driving industry for the several other sectors or following industries of Macao's economy. The purpose of this research study is to investigate in what measure the Gaming sector in Macao leveraged the other economic sectors and how related or correlated are the some of the industries of Macao's Economy. The research adopted statistical tests such as Granger causality test and Vector Autoregressive (VAR) technique based upon the Impulse Response Function (IRF) and Variance Decomposition (VD) to observe linkages among variables. The empirical results show a significant causal linkage from Gaming to the other sectors of Macao's economy. Our findings are consistent with respect to the statistical tests used. Overall, the results indicate a forward linkage between Gaming and the other sectors.
User experience of human-robot long-term interactions

Since interactions with social robots are novel and exciting for many people, one particular concern in this specific area of human-robot interaction (HRI) is the extent to which human users will experience the interactions positively over time, when the robot’s novelty is particularly salient. In the current paper, we investigated users’ experience in long-term HRIs; how users perceive the ongoing interactions and the robot’s ability to sustain it over time. Therefore, here we examine the effect of the repeated measures (10 testing sessions) and the discussion theme (Covid-19 related vs general) on the way participants experienced the interaction quality with a social robot and perceived the robot’s communication competency over time. We found that despite individual differences between the participants, over time participants found the interactions with Pepper to be of higher quality and that Pepper’s communication skills got better. Nevertheless, our results also stressed that the discussion theme has no meaningful nor significant effect on the way people perceive Pepper and the interaction.
XAI: Using Smart Photobooth for Explaining History of Art

The rise of Artificial Intelligence has led to advancements in daily life, including applications in industries, telemedicine, farming, and smart cities. It is necessary to have human-AI synergies to guarantee user engagement and provide interactive expert knowledge, despite AI’s success in "less technical" fields. In this article, the possible synergies between humans and AI to explain the development of art history and artistic style transfer are discussed. This study is part of the "Smart Photobooth" project that is able to automatically transform a user’s picture into a well-known artistic style as an interactive approach to introduce the fundamentals of the history of art to the common people and provide them with a concise explanation of the various art painting styles. This study investigates human-AI synergies by combining the explanation produced by an explainable AI mechanism with a human expert’s insights to provide reasons for school students and a larger audience.
S3-Slicer

Multi-axis motion introduces more degrees of freedom into the process of 3D printing to enable different objectives of fabrication by accumulating materials layers upon curved layers. An existing challenge is how to effectively generate the curved layers satisfying multiple objectives simultaneously. This paper presents a general slicing framework for achieving multiple fabrication objectives including support free, strength reinforcement and surface quality. These objectives are formulated as local printing directions varied in the volume of a solid, which are achieved by computing the rotation-driven deformation for the input model. The height field of a deformed model is mapped into a scalar field on its original shape, the isosurfaces of which give the curved layers of multi-axis 3D printing. The deformation can be effectively optimized with the help of quaternion fields to achieve the fabrication objectives. The effectiveness of our method has been verified on a variety of models.
Better Understanding the Costs and Benefits of Automatic Memory Management

Automatic memory management relieves programmers of the burden of having to reason about object lifetimes in order to soundly reclaim allocated memory. However, this automation comes at a cost. The cost and benefits of garbage collection relative to manual memory management have been the subject of contention for a long time, and will likely remain so. However, until now, the question is surprisingly under-studied. We examine the costs and benefits of garbage collection through four studies, exploring: We conduct this study in a contemporary setting using recent CPU microarchitectures, and novel methodologies including a mark-sweep collector built upon off-the-shelf free-list allocators, allowing us to shed new light on garbage collection overheads in a modern context. We find that: The costs and benefits of garbage collection are likely to remain subject to contentious discussion. However, the methodologies and evaluations we present here provide a deeper understanding of the differences in costs between manual memory management and garbage collection.
The Networked Energy Systems Emulation Center at the German Aerospace Center DLR – bridging the gap between digital simulation and real operation of energy grids

Abstract This paper describes the Networked Energy Systems Emulation Center – DLR-NESTEC – a platform for research on power grids of the future. The DLR-NESTEC consists of a large number of networked power electronic components with which real hardware can be emulated using a real-time simulation system. The grid networking is realized via cable emulators. The laboratory works with real physical power flows and has a connected load of 800 kVA. In addition to the emulators, real network components can be integrated into the laboratory. The sector coupling is addressed by the coupling of charging infrastructure of electric cars as well as the integration of electricity-controlled heating systems. In addition, hydrogen technologies can be integrated. The laboratory is controlled by a SCADA system coupled to high-resolution measurement equipment. With the help of DLR-NESTEC, various future questions regarding robust and safe operation sector-coupled energy networks can be addressed – for instance the operation of a grid structure with a high share of controllable loads without a superordinate control.
Shuffle algebras for quivers and wheel conditions

Abstract We show that the shuffle algebra associated to a doubled quiver (determined by 3-variable wheel conditions) is generated by elements of minimal degree. Together with results of Varagnolo–Vasserot and Yu Zhao, this implies that the aforementioned shuffle algebra is isomorphic to the localized 𝐾-theoretic Hall algebra associated to the quiver by Grojnowski, Schiffmann–Vasserot and Yang–Zhao. With small modifications, our theorems also hold under certain specializations of the equivariant parameters, which will allow us in joint work with Sala and Schiffmann to give a generators-and-relations description of the Hall algebra of any curve over a finite field (which is a shuffle algebra due to Kapranov–Schiffmann–Vasserot). When the quiver has no edge loops or multiple edges, we show that the shuffle algebra, localized 𝐾-theoretic Hall algebra, and the positive half of the corresponding quantum loop group are all isomorphic; we also obtain the non-degeneracy of the Hopf pairing on the latter quantum loop group.
Spatial Interaction Patterns

An algebraic examination of spatial models leads to the conclusion that a convenient description of the pattern of flows implicit in a geographical interaction table is obtained by displaying a field of vectors computed from the relative net exchanges. The vector field approximates the gradient of a scalar potential, and this may be invoked to explain the flows. The method can be applied to asymmetrical tables of a non-geographical nature.
Spatio-temporal modelling of the effect of selected environmental and land-use factors on species-rich calcareous grasslands: overgrazing and nitrogen deposition

The abundance of sensitive plant species in calcareous grasslands are threatened by agricultural intensification with nutrient addition and increased livestock densities as well as by land abandonment. In order to quantify the effect of selected environmental and land-use factors on the observed variation and changes in the vegetation of calcareous grasslands, large-scale spatial and temporal pin-point plant cover monitoring data are fitted in a structural equation model. The analyzed vegetation data come from 100 Danish sites monitored over an eight year period. The important sources of measurement- and sampling uncertainties have been included using a hierarchical model structure. Furthermore, the measurement- and sampling uncertainties are separated from the process uncertainty, which is important when generating ecological predictions that may feed into local conservation management decisions. There were significant negative effects of grazing and nitrogen deposition on the change in cover of sensitive plant species. Whereas the negative effect of nitrogen deposition on the cover of sensitive species was expected, it was surprising that the model results suggest that the class of sensitive species may be subjected to overgrazing by the grazing regimes that are currently applied at protected Danish calcareous grasslands. The standardized regression coefficients suggest that the effects of both grazing and nitrogen deposition are relatively benign compared to the effects of soil type, soil pH and precipitation. The relatively large effect of precipitation is interesting, since it is predicted that the amount of precipitation and distribution over the season will change due to climate change, and the results suggest that climate change may lead to important species compositional changes in calcareous grasslands. The fitted model may be used to quantify the uncertainties when generating ecological forecasting and local adaptive management plans.
Optical frequency comb profilometry with a compressive sensing–based single-pixel camera composed of digital micromirror devices and a two-frequency method for meter-order depth measurements

Abstract. We demonstrated optical-frequency-comb profilometry with a compressive sensing–based single-pixel camera composed of digital micromirror devices and a photodetector for measurement of large-depth objects. Use of two harmonic frequencies allowed us to measure an object with a meter-order depth without any 2π-phase ambiguity. Profilometry with 10×10 pixels using the 12th and 13th harmonics of the fundamental frequency of 76 MHz gave a noise level of ∼10 μm in terms of standard deviation. Linking the measurement results obtained using one and two frequencies gave an axial dynamic range of ∼3.9×105.
RBPMS promotes contractile smooth muscle splicing and alters phenotypic behaviour of human embryonic stem cell derived vascular smooth muscle cells

Differentiated Vascular Smooth Muscle Cells (VSMCs) express a unique network of splice isoforms (smooth muscle specific alternative splicing - SM-AS) in functionally critical genes including those comprising the contractile machinery. We previously described RNA Binding Protein Multiple Splicing (RBPMS) as a potent driver of contractile, aortic tissue like SM-AS in VSMCs using rodent models. What is unknown is how RBPMS affects VSMC phenotype and behaviour. Here, we use human embryonic stem cell-derived VSMCs (hES-VSMCs) to dissect the role of RBPMS in SM-AS in human cells and determine the impact on VSMC phenotypic properties. hES-VSMCs are inherently immature and display only partially differentiated SM-AS patterns while RBPMS levels are undetectable endogenously. Hence, we used an over-expression system and found that RBPMS induces SM-AS patterns in hES-VSMCs akin to the contractile tissue VSMC splicing patterns in multiple events. We present in silico and experimental findings that support RBPMS’ splicing activity as mediated through direct binding and via functional cooperativity with splicing factor RBFOX2 on a significant subset of targets. Finally, we demonstrate that RBPMS is capable of altering the motility and the proliferative properties of hES-VSMCs to mimic a more differentiated state. Overall, this study emphasizes a critical splicing regulatory role for RBPMS in human VSMCs and provides evidence of phenotypic modulation by RBPMS.
Trust Your ∇: Gradient-based Intervention Targeting for Causal Discovery

Inferring causal structure from data is a challenging task of fundamental importance in science. Observational data are often insufficient to identify a system's causal structure uniquely. While conducting interventions (i.e., experiments) can improve the identifiability, such samples are usually challenging and expensive to obtain. Hence, experimental design approaches for causal discovery aim to minimize the number of interventions by estimating the most informative intervention target. In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function. We provide extensive experiments in simulated and real-world datasets and demonstrate that GIT performs on par with competitive baselines, surpassing them in the low-data regime.
Fast protein structure searching using structure graph embeddings

Comparing and searching protein structures independent of primary sequence has proved useful for remote homology detection, function annotation and protein classification. With the recent leap in accuracy of protein structure prediction methods and increased availability of protein models, attention is turning to how to best make use of this data. Fast and accurate methods to search databases of millions of structures will be essential to this endeavour, in the same way that fast protein sequence searching underpins much of bioinformatics. We train a simple graph neural network using supervised contrastive learning to learn a low-dimensional embedding of protein structure. The embedding can be used to search structures against large structural databases with accuracy comparable to current methods. The speed of the method and ability to scale to millions of structures makes it suitable for this structure-rich era. The method, called Progres, is available at https://github.com/jgreener64/progres.