Miracle-dz/UniFAR-FTUs huggingface dataset:
DEBRIS FLOW TRIGGERED BY RAPID SNOWMELT

Debris flows in the Glei √ arhjalli area in northwestern Iceland occurred after a sudden and intensive snowmelt period during 10–12 June, 1999. The area, in the northwestern part of the town of Ísafjör √ ur, was chosen for a detailed study. Meteorological data and bedrock conditions, triggering mechanisms and geomorphological and human impacts were examined. This paper describes and emphasises the role of rapid snowmelt as a mechanism for the release of debris flows in a subpolar basaltic fjord setting. Post-event mapping of erosional and depositional landforms showed strong geomorphic impacts of debris flows and their role in mass transfer in a mountainous environment. The estimated denudation rate for the single event is 0.29 mm/km 2 . The use of a new lichen growth curve provides relative dating of previous unreported events. Finally, the paper estimates the mean return period for debris-flow events in the Glei √ arhjalli area as 4–5 years, thus constituting a serious threat to the community.
Imaging of cell-based therapy using 89Zr-oxine ex vivo cell labeling for positron emission tomography

With the rapid development of anti-cancer cell-based therapies, such as adoptive T cell therapies using tumor-infiltrating T cells, T cell receptor transduced T cells, and chimeric antigen receptor T cells, there has been a growing interest in imaging technologies to non-invasively track transferred cells in vivo. Cell tracking using ex vivo cell labeling with positron emitting radioisotopes for positron emission tomography (PET) imaging has potential advantages over single-photon emitting radioisotopes. These advantages include intrinsically higher resolution, higher sensitivity, and higher signal-to-background ratios. Here, we review the current status of recently developed Zirconium-89 (89Zr)-oxine ex vivo cell labeling with PET imaging focusing on its applications and future perspectives. Labeling of cells with 89Zr-oxine is completed in a series of relatively simple steps, and its low radioactivity doses required for imaging does not interfere with the proliferation or function of the labeled immune cells. Preclinical studies have revealed that 89Zr-oxine PET allows high-resolution in vivo tracking of labeled cells for 1-2 weeks after cell transfer both in mice and non-human primates. These results provide a strong rationale for the clinical translation of 89Zr-oxine PET-based imaging of cell-based therapy.
Design of data-driven Decision Support using Open and Shared Data in the Tourism Sector

In a tourism destination, there is a wide range of decision relevant data sources available. The stakeholders of a tourism association have access to large amounts of transactional data, such as sales, consumer consumption or online booking data. Additionally, there are some actors providing open data sources for tourism destination like traffic or weather data. Therefore, data-driven decision support is becoming increasingly important for the members of a tourism association. The application of open data sources and data sources in collaboration shared by stakeholders of the tourism destination with the help of BI&A offer great possibilities to enhance the data-driven decision-making of tourism associations and its stakeholders. However, these data sources are not typically utilized for decision support by tourism associations. This master thesis’ aim is to design a data-driven decision support using open data and data sources shared by stakeholders of a tourism destination. According to this research goal, the following research question is investigated: How can open data sources and data sources shared by stakeholders of a tourism destination be applied or combined to design a data-driven decision support which is comprehensible and valuable for the stakeholders of the particular tourism destination? To achieve these objective and research questions, the design science research (DSR) methodology is applied. The building and evaluating processes are performed by BI&A within the case of the tourism association Mayrhofen-Hippach in Tyrol, Austria. Touristic key metrics for traffic, price, and occupancy are developed based on traffic, online booking and registration system data sources of the case destination, and demonstrated on a dashboard to enhance the data-driven decision-making of the tourism association Mayrhofen-Hippach. Analyses of the key metrics show that there is a strong positive correlation between the independent variable arrivals and the dependent variable vehicles. The results indicate that the values of independent variable arrivals can be estimated with values of vehicles, if only the values of arrivals are known. Additionally, the designed dashboard and its knowledge services are evaluated based on two semi-structured interviews with two experts within the case environment. The results of the evaluation confirm the usefulness and usability of the dashboard and its visualised touristic key metrics as well as the data-driven decision support for the tourism association Mayrhofen-Hippach and its stakeholders. On this basis, it is recommended that tourism associations consider using more open and shred data sources, such as weather data or shared sales and booking data, to even more improve their decision support. Further research is needed to investigate in more open data sources in combination with stakeholders’ shared data sources to examine further effects of the tourism sector and to generate more detailed data-driven decision support for a tourism destination.
Why galaxies care about AGB stars II : shining examples and common inhabitants : proceedings of a conference held at University Campus, Vienna, Austria, 16-20 August 2010

Based on dynamic model atmospheres for Mira variables with carbon-rich atmospheric chemistry we have calculated synthetic spectra and photometry for standard broadband filters from the visual to the near-infrared. The modeling allows us to study in detail the influence of two effects characteristic of such objects: (i) the pulsation of the stellar interior, and (ii) the dusty circumstellar envelope resulting from an outflow containing grains of amorphous carbon. A comparison with observational data shows that the models used describe the outer layers of pulsating, mass-losing AGB stars reasonably well.
Relationships Between Soil Characteristics and Concentrationsof Lead and Zinc in Soils and in Oriental Tobacco

Increasing efforts to curb tobacco use worldwide have been made in recent years. Harmful substances in tobacco raw material and cigarettes have been the primary focus of attention. Obtaining information regarding the relationship between soil characteristics and the absorption of heavy metals by tobacco plants could improve the estimations for metal uptake by the plants and the associated health risks with exposure to tobacco smoke. This study was conducted in the Eastern, Central, and parts of the Western Rhodope Mountains using Oriental tobacco plants. The determined soil characteristics were: pH, humus content, and texture. Inductively coupled plasma atomic emission spectroscopy was used to measure the concentration of Pb and Zn in mature tobacco leaves, and the total content and mobile forms of the same elements in the soil. The correlation/regression analyses showed statistically significant linear relationships between the total content and mobile forms of Pb and Zn in soils. The concentration of Zn in tobacco leaves was linearly proportional to the Zn content in soils. The power model adequately reflected the relationship between the Pb total content and mobile forms in soils. The relationships between clay content and the concentrations of Pb and Zn in tobacco leaves were statistically significant.
The changes in the types and amounts of fatty acids of adult Acanthoscelides obtectus (Coleoptera: Bruchidae) in terms of age and sex

This study examined the changes in the types and amounts of fatty acids of 0, 4 and 8 day-old adult Acanthoscelides obtectus bred at 30 ̊C in terms of age and sex. Tests were conducted under 30±2 ̊C temperature and % 60±5 moist. The types and amounts of the fatty acids of adults were determined with gas chromatography. 11 fatty acids were found in 0, 4 and 8 day-old adult Acanthoscelides obtectus breeded at 30 ̊C as lauric acid (C12:0), myristic acid (C14:0), palmitic acid (C16:0), palmitoleic acid (C16:1), stearic acid (C18:0), oleic acid (C18:1n9c), linolelaidic acid (C18:2n6t), linoleic acid (C18:2n6c), linolenic+cis-11-eicosenoic acid (C18:3n3+C20:1) and cis-11,14-eicosadienoic acid (C20:2). Of these 11 fatty acids, oleic and palmitic acids were determined as dominant fatty acids. While the types of fatty acids found in A. obtectus adults remained stable in general, their contents were found to change in terms of sex and age.
Sensitivity analysis of directivity effects on PSHA

Careful characterization of seismic hazard is especially important in structural engineering if critical facilities (e.g., strategic buildings, chemical deposits, energy production plants, etc.) are the object of analysis. Moreover, if the construction site is close to active faults, particular attention is required. In fact, in this condition, the fault’s dynamics affect ground motion differently from site to site, resulting in systematic spatial variability, and, generally, in higher seismic demand, with respect to the far-source case. The most important of the near-source (NS) effects is known as forward rupture directivity, and can be identified by a large full-cycle pulse at the beginning of the velocity records, and containing the most of carried energy. Recent research demonstrates that, from the structural engineering point of view, hazard assessment should account for NS effects (i.e., pulse-like ground motions), but ordinary probabilistic seismic hazard analysis (PSHA) is not able to do it appropriately. On the other hand, semi-empirical models calibrated for introducing NS effects in classical PSHA are now available, and some preliminary attempts of numerical implementation of NS-PSHA exist. In the presented study, numerical applications of strike-slip (SS) fault scenarios are provided and, for a fixed return period, uniform hazard spectra are computed in order to quantify hazard increments (HIs) due to NS-PSHA with respect to ordinary-PSHA. It is shown that: i) depending on source-site geometry, these may be significant (more than a 100% increment); ii) different spectral periods are affected by significantly different values of HIs, which have their shape controlled only by the magnitude of generated earthquakes; iii) it is possible to identify a zone beyond which directivity effects are expected to become negligible, at least with a first-order approximation, independently of the characteristics of the considered SS fault.
E-ChRPs: Engineered Chromatin Remodeling Proteins for Precise Nucleosome Positioning

Regulation of chromatin structure is essential for controlling the access of DNA to factors that require association with specific DNA sequences. Here we describe the development of engineered chromatin remodeling proteins (E-ChRPs), which can precisely alter positions of target nucleosomes throughout the genome. E-ChRPs are highly versatile and capable of blocking access of DNA sequences to endogenous DNA binding proteins, thus providing a means to investigate and manipulate DNA-dependent processes.
Role Taxonomy of Units in Deep Neural Networks

Identifying the role of network units in deep neural networks (DNNs) is critical in many aspects including giving understandings on the mechanisms of DNNs and building basic connections between deep learning and neuroscience. However, there remains unclear on which roles the units in DNNs with different generalization ability could present. To this end, we give role taxonomy of units in DNNs via introducing the retrieval-of-function test, where units are categorized into four types in terms of their functional preference on separately the training set and testing set. We show that ratios of the four categories are highly associated with the generalization ability of DNNs from two distinct perspectives, based on which we give signs of DNNs with well generalization.
Competition in Fund Management and Forward Relative Performance Criteria

In an Ito-diffusion market, two fund managers trade under relative performance concerns. For both the asset specialization and diversification settings, we analyze the passive and competitive cases. We measure the performance of the managers' strategies via forward relative performance criteria, leading to the respective notions of forward best-response criterion and forward Nash equilibrium. The motivation to develop such criteria comes from the need to relax various crucial, but quite stringent, existing assumptions -- such as, the a priori choices of both the market model and the investment horizon, the commonality of the latter for both managers as well as the full a priori knowledge of the competitor's policies for the best-response case. We focus on locally riskless criteria and deduce the random forward equations. We solve the CRRA cases, thus also extending the related results in the classical setting. An important by-product of the work herein is the development of forward performance criteria for investment problems in Ito-diffusion markets under the presence of correlated random endowment process for both the perfectly and the incomplete market cases.
Combining Domain-Specific Meta-Learners in the Parameter Space for Cross-Domain Few-Shot Classification

The goal of few-shot classification is to learn a model that can classify novel classes using only a few training examples. Despite the promising results shown by existing meta-learning algorithms in solving the few-shot classification problem, there still remains an important challenge: how to generalize to unseen domains while meta-learning on multiple seen domains? In this paper, we propose an optimization-based meta-learning method, called Combining Domain-Specific Meta-Learners (CosML), that addresses the cross-domain few-shot classification problem. CosML first trains a set of meta-learners, one for each training domain, to learn prior knowledge (i.e., meta-parameters) specific to each domain. The domain-specific meta-learners are then combined in the \emph{parameter space}, by taking a weighted average of their meta-parameters, which is used as the initialization parameters of a task network that is quickly adapted to novel few-shot classification tasks in an unseen domain. Our experiments show that CosML outperforms a range of state-of-the-art methods and achieves strong cross-domain generalization ability.
Revisiting the dust destruction efficiency of supernovae

Dust destruction by supernovae is one of the main processes removing dust from the interstellar medium (ISM). Estimates of the efficiency of this process, both theoretical and observational, typically assume a shock propagating into a homogeneous medium, whereas the ISM possesses significant substructure in reality. We self-consistently model the dust and gas properties of the shocked ISM in three supernova remnants (SNRs), using X-ray and infrared (IR) data combined with corresponding emission models. Collisional heating by gas with properties derived from X-ray observations produces dust temperatures too high to fit the far-IR fluxes from each SNR. An additional colder dust component is required, which has a minimum mass several orders of magnitude larger than that of the warm dust heated by the X-ray emitting gas. Dust-to-gas mass ratios indicate that the majority of the dust in the X-ray emitting material has been destroyed, while the fraction of surviving dust in the cold component is plausibly close to unity. As the cold component makes up virtually all the total dust mass, destruction time-scales based on homogeneous models, which cannot account for multiple phases of shocked gas and dust, may be significantly overestimating actual dust destruction efficiencies, and subsequently underestimating grain lifetimes.
Relaxed Multibang Regularization for the Combinatorial Integral Approximation

Multibang regularization and combinatorial integral approximation decompositions are two actively researched techniques for integer optimal control. We consider a class of polyhedral functions that arise particularly as convex lower envelopes of multibang regularizers and show that they have beneficial properties with respect to regularization of relaxations of integer optimal control problems. We extend the algorithmic framework of the combinatorial integral approximation such that a subsequence of the computed discrete-valued controls converges to the infimum of the regularized integer control problem.
Targeted Poisoning Attacks on Black-Box Neural Machine Translation

As modern neural machine translation (NMT) systems have been widely deployed, their security vulnerabilities require close scrutiny. Most recently, NMT systems have been shown to be vulnerable to targeted attacks which cause them to produce specific, unsolicited, and even harmful translations. These attacks are usually exploited in a white-box setting, where adversarial inputs causing targeted translations are discovered for a known target system. However, this approach is less useful when the target system is black-box and unknown to the adversary (e.g., secured commercial systems). In this paper, we show that targeted attacks on black-box NMT systems are feasible, based on poisoning a small fraction of their parallel training data. We demonstrate that this attack can be realised practically via targeted corruption of web documents crawled to form the system's training data. We then analyse the effectiveness of the targeted poisoning in two common NMT training scenarios, which are the one-off training and pre-train&fine-tune paradigms. Our results are alarming: even on the state-of-the-art systems trained with massive parallel data (tens of millions), the attacks are still successful (over 50% success rate) under surprisingly low poisoning rates (e.g., 0.006%). Lastly, we discuss potential defences to counter such attacks.
Adaptive Bernstein Copulas and Risk Management

We present a constructive approach to Bernstein copulas with an admissible discrete skeleton in arbitrary dimensions when the underlying marginal grid sizes are smaller than the number of observations. This prevents an overfitting of the estimated dependence model and reduces the simulation effort for Bernstein copulas a lot. In a case study, we compare different approaches of Bernstein and Gaussian copulas regarding the estimation of risk measures in risk management.
Channel Estimation in Reconfigurable Intelligent Surface Assisted mmWave MIMO Systems

Reconfigurable intelligent surfaces (RISs) can be used to establish a communication link when there is no or weak line-of-sight (LoS) path between a base station (BS) and user equipment (UE) in millimeter-wave MIMO systems. RIS comprises of many passive phase shifters, which can be controlled from the BS to focus a signal to a desired location and modify the propagation environment. Due to the passive-only elements, pilots cannot be decoded at the RIS. Thus channel estimation in RIS-assisted MIMO communication systems is challenging. Although the LoS channel between the BS and RIS can be obtained from the knowledge of their locations, the RIS-UE channel needs to be estimated. Estimating the RIS-UE channel at the BS amounts to localizing the UE. In this paper, we present a pilot-based uplink channel estimation technique to estimate the LoS channel between the RIS and UE at the BS. To do so, we assume an angular channel model. Central to the proposed algorithm is a new technique in which we observe the channel through multiple soundings with different phase shifts at the RIS. These multiple measurements allow us to resolve the underlying ambiguity in resolving the complex path gain and the angle of arrival at the RIS and to estimate the RIS-UE channel. Simulation results indicate that the performance of the proposed method is comparable to that of an oracle estimator, which assumes a partial channel state information with perfect knowledge of the locations of the BS, UE, and RIS.
Hawking temperature and phonon emission in acoustic holes

Acoustic holes are the hydrodynamic analogue of standard black holes. Featuring an acoustic horizon, these systems spontaneously emit phonons at the Hawking temperature. We derive the Hawking temperature of the acoustic horizon by fully exploiting the analogy between black and acoustic holes within a covariant kinetic theory approach. After deriving the phonon distribution function from the covariant kinetic equations, we reproduce the expression of the Hawking temperature by equating the entropy and energy losses of the acoustic hole and the entropy and energy gains of the spontaneously emitted phonons. Differently from previous calculations we do not need a microscopical treatment of normal modes propagation. Our approach opens a different perspective on the meaning of Hawking temperature and its connection with entropy which may allow an easier study of non stationary horizons beyond thermodynamic equilibrium.
Deep Pairwise Hashing for Cold-Start Recommendation

Recommendation efficiency and data sparsity problems have been regarded as two challenges of improving performance for online recommendation. Most of the previous related work focus on improving recommendation accuracy instead of efficiency. In this paper, we propose a Deep Pairwise Hashing (DPH) to map users and items to binary vectors in Hamming space, where a user's preference for an item can be efficiently calculated by Hamming distance, which significantly improves the efficiency of online recommendation. To alleviate data sparsity and cold-start problems, the user-item interactive information and item content information are unified to learn effective representations of items and users. Specifically, we first pre-train robust item representation from item content data by a Denoising Auto-encoder instead of other deterministic deep learning frameworks; then we finetune the entire framework by adding a pairwise loss objective with discrete constraints; moreover, DPH aims to minimize a pairwise ranking loss that is consistent with the ultimate goal of recommendation. Finally, we adopt the alternating optimization method to optimize the proposed model with discrete constraints. Extensive experiments on three different datasets show that DPH can significantly advance the state-of-the-art frameworks regarding data sparsity and item cold-start recommendation.
Capillary-scale solid rebounds: experiments, modelling and simulations

Abstract A millimetre-size superhydrophobic sphere impacting on the free surface of a quiescent bath can be propelled back into the air by capillary effects and dynamic fluid forces, whilst transferring part of its energy to the fluid. We report the findings of a thorough investigation of this phenomenon, involving different approaches. Over the range from minimum impact velocities required to produce rebounds to impact velocities that cause the sinking of the solid sphere, we focus on the dependence of the coefficient of restitution, contact time and maximum surface deflection on the different physical parameters of the problem. Experiments, simulations and asymptotic analysis reveal trends in the rebound metrics, uncover new phenomena at both ends of the Weber number spectrum, and collapse the data. Direct numerical simulations using a pseudo-solid sphere successfully reproduce experimental data whilst also providing insight into flow quantities that are challenging to determine from experiments. A model based on matching the motion of a perfectly hydrophobic impactor to a linearised fluid free surface is validated against direct numerical simulations and used in the low-Weber-number regime. The hierarchical and cross-validated models in this study allow us to explore the entirety of our target parameter space within a challenging multi-scale system.