Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Bermudans

American and Bermudan-type financial instruments are often priced with specific Monte Carlo techniques whose efficiency critically depends on the effective dimensionality of the problem and the available computational power. In our work we focus on Bermudan Swaptions, well-known interest rate derivatives embedded in callable debt instruments or traded in the OTC market for hedging or speculation purposes, and we adopt an original pricing approach based on Supervised Learning (SL) algorithms. In particular, we link the price of a Bermudan Swaption to its natural hedges, i.e. the underlying European Swaptions, and other sound financial quantities through SL non-parametric regressions. We test different algorithms, from linear models to decision tree-based models and Artificial Neural Networks (ANN), analyzing their predictive performances. All the SL algorithms result to be reliable and fast, allowing to overcome the computational bottleneck of standard Monte Carlo simulations; the best performing algorithms for our problem result to be Ridge, ANN and Gradient Boosted Regression Tree. Moreover, using feature importance techniques, we are able to rank the most important driving factors of a Bermudan Swaption price, confirming that the value of the maximum underlying European Swaption is the prevailing feature.
Application of Deep Convolutional Neural Networks for automated and rapid identification and characterization of thin cracks in SHCCs

Previous research has showcased that the characterization of surface cracks is one of the key steps towards understanding the durability of strain hardening cementitious composites (SHCCs). Under laboratory conditions, surface crack statistics can be obtained from images of specimen surfaces through manual inspection or image processing techniques. Since these techniques require optimal lighting conditions, proper surface treatment, and prior (manual) selection of the correct region for proper inference, they are strenuous and time-consuming. Through this work, we explored and tailored deep convolutional networks (DCCNs) for the rapid characterization of cracks in SHCC from various kinds of photographs. The results from the controlled study suggest that the inference ability of the tailored DCCN (TDCNN) is quite good, resilient against epistemic uncertainty, and tunable for completely independent but adverse observations. From the crack pattern computed using TDCCN, average crack width (ACW) and crack density (CD) can be calculated to facilitate durability design and conditional assessment in a practical environment.
Semiconductor Laser Linewidth Theory Revisited

More and more applications require semiconductor lasers distinguished not only by large modulation bandwidths or high output powers, but also by small spectral linewidths. The theoretical understanding of the root causes limiting the linewidth is therefore of great practical relevance. In this paper, we derive a general expression for the calculation of the spectral linewidth step by step in a self-contained manner. We build on the linewidth theory developed in the 1980s and 1990s but look from a modern perspective, in the sense that we choose as our starting points the time-dependent coupled-wave equations for the forward and backward propagating fields and an expansion of the fields in terms of the stationary longitudinal modes of the open cavity. As a result, we obtain rather general expressions for the longitudinal excess factor of spontaneous emission (K-factor) and the effective α-factor including the effects of nonlinear gain (gain compression) and refractive index (Kerr effect), gain dispersion, and longitudinal spatial hole burning in multi-section cavity structures. The effect of linewidth narrowing due to feedback from an external cavity often described by the so-called chirp reduction factor is also automatically included. We propose a new analytical formula for the dependence of the spontaneous emission on the carrier density avoiding the use of the population inversion factor. The presented theoretical framework is applied to a numerical study of a two-section distributed Bragg reflector laser.
Federated Word2Vec: Leveraging Federated Learning to Encourage Collaborative Representation Learning

Large scale contextual representation models have significantly advanced NLP in recent years, understanding the semantics of text to a degree never seen before. However, they need to process large amounts of data to achieve high-quality results. Joining and accessing all these data from multiple sources can be extremely challenging due to privacy and regulatory reasons. Federated Learning can solve these limitations by training models in a distributed fashion, taking advantage of the hardware of the devices that generate the data. We show the viability of training NLP models, specifically Word2Vec, with the Federated Learning protocol. In particular, we focus on a scenario in which a small number of organizations each hold a relatively large corpus. The results show that neither the quality of the results nor the convergence time in Federated Word2Vec deteriorates as compared to centralised Word2Vec.
Active peer effects in residential photovoltaic adoption: evidence on impact drivers among potential and current adopters in Germany

While the importance of peer influences has been demonstrated in several studies, little is known about the underlying mechanisms of active peer effects in residential photovoltaic (PV) diffusion. Empirical evidence indicates that the impacts of inter-subjective exchanges are dependent on the subjective mutual evaluation of the interlocutors. This paper aims to quantify, how subjective evaluations of peers affect peer effects across different stages of PV adoption decision-making. The findings of a survey among potential and current adopters in Germany(N=1,165)confirm two hypotheses. First, peer effects play a role in residential PV adoption: the number of peer adopters in the decision-maker's social circle has a positive effect on the decision-maker's belief that their social network supports PV adoption; their ascription of credibility on PV-related topics to their peers; and their interest in actively seeking information from their peers in all decision-making stages. Second, there is a correlation between the perceived positive attributes of a given peer and the reported influence of said peer within the decision-making process, suggesting that decision-makers' subjective evaluations of peers play an important role in active peer effects. Decision-makers are significantly more likely to engage in and be influenced by interactions with peers who they perceive as competent, trustworthy, and likeable. In contrast, attributes such as physical closeness and availability have a less significant effect. From a policymaking perspective, this study suggests that the density and quality of peer connections empower potential adopters. Accordingly, peer consultation and community-led outreach initiatives should be promoted to accelerate residential PV adoption.
Model Checking Quantum Continuous-Time Markov Chains

Verifying quantum systems has attracted a lot of interests in the last decades. In this paper, we initialised the model checking of quantum continuous-time Markov chain (QCTMC). As a real-time system, we specify the temporal properties on QCTMC by signal temporal logic (STL). To effectively check the atomic propositions in STL, we develop a state-of-art real root isolation algorithm under Schanuel's conjecture; further, we check the general STL formula by interval operations with a bottom-up fashion, whose query complexity turns out to be linear in the size of the input formula by calling the real root isolation algorithm. A running example of an open quantum walk is provided to demonstrate our method.
NaijaNER : Comprehensive Named Entity Recognition for 5 Nigerian Languages

Most of the common applications of Named Entity Recognition (NER) is on English and other highly available languages. In this work, we present our ﬁndings on Named Entity Recognition for 5 Nigerian Languages (Nige-rian English, Nigerian Pidgin English, Igbo, Yoruba and Hausa). These languages are considered low-resourced, and very little openly available Natural Language Processing work has been done in most of them. In this work, individual NER models were trained and metrics recorded for each of the languages. We also worked on a combined model that can handle Named Entity Recognition (NER) for any of the ﬁve languages. The combined model works well for Named Entity Recog-nition(NER) on each of the languages and with better performance compared to individual NER models trained speciﬁcally on annotated data for the speciﬁc language. The aim of this work is to share our learning on how information extraction using Named Entity Recognition can be optimized for the listed Nigerian Languages for inclusion, ease of deployment in production and reusability of models. Models developed during this project are available on GitHub and an interactive web app here.
Causal inference for process understanding in Earth sciences

There is growing interest in the study of causal methods in the Earth sciences. However, most applications have focused on causal discovery, i.e. inferring the causal relationships and causal structure from data. This paper instead examines causality through the lens of causal inference and how expert-defined causal graphs, a fundamental from causal theory, can be used to clarify assumptions, identify tractable problems, and aid interpretation of results and their causality in Earth science research. We apply causal theory to generic graphs of the Earth system to identify where causal inference may be most tractable and useful to address problems in Earth Science, and avoid potentially incorrect conclusions. Specifically, causal inference may be useful when: (1) the effect of interest is only causally affected by the observed portion of the state space; or: (2) the cause of interest can be assumed to be independent of the evolution of the system's state; or: (3) the state space of the system is reconstructable from lagged observations of the system. However, we also highlight through examples how causal graphs can be used to explicitly define and communicate assumptions and hypotheses, and help to structure analyses, even if causal inference is ultimately challenging given the data availability, limitations and uncertainties.
Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization

The widely-used cross-entropy (CE) loss-based deep networks achieved significant progress w.r. t. the classification accuracy. However, the CE loss can essentially ignore the risk of misclassification which is usually measured by the distance between the prediction and label in a semantic hierarchical tree. In this paper, we propose to incorporate the risk-aware inter-class correlation in a discrete optimal transport (DOT) training framework by configuring its ground distance matrix. The ground distance matrix can be pre-defined following a priori of hierarchical semantic risk. Specifically, we define the tree induced error (TIE) on a hierarchical semantic tree and extend it to its increasing function from the optimization perspective. The semantic similarity in each level of a tree is integrated with the information gain. We achieve promising results on several large scale image classification tasks with a semantic tree structure in a plug and play manner.
870 μm Dust Continuum of the Youngest Protostars in Ophiuchus

We present a 0. ″15 resolution (21 au) ALMA 870 μm continuum survey of 25 pointings containing 31 young stellar objects in the Ophiuchus molecular clouds. Using the dust continuum as a proxy for dust mass and circumstellar disk radius in our sample, we report mean masses of 2.8−1.3+2.1 and 2.5−1.1+9.2 M ⊕ and mean radii of 23.5−1.2+1.8 and 16.5−0.9+2.8 au, for Class I and Flat spectrum protostars, respectively. In addition, we calculate the multiplicity statistics of the dust surrounding young stellar objects in Ophiuchus. The multiplicity fraction and companion star fraction of the combined Class I and Flats based solely on this work are 0.25 ± 0.09 and 0.33 ± 0.10, respectively, which are consistent with the values for Perseus and Orion. While we see clear differences in mass and radius between the Ophiuchus and Perseus/Orion protostellar surveys, we do not see any significant differences in the multiplicities of the various regions. We posit that there are some differences in the conditions for star formation in Ophiuchus that strongly affect disk size (and consequently disk mass), but does not affect system multiplicity, which could imply important variation in planet formation processes.
BERT based freedom to operate patent analysis

In this paper we present a method to apply BERT to freedom to operate patent analysis and patent searches. According to the method, BERT is fine-tuned by training patent descriptions to the independent claims. Each description represents an invention which is protected by the corresponding claims. Such a trained BERT could be able to identify or order freedom to operate relevant patents based on a short description of an invention or product. We tested the method by training BERT on the patent class G06T1/00 and applied the trained BERT on five inventions classified in G06T1/60, described via DOCDB abstracts. The DOCDB abstract are available on ESPACENET of the European Patent Office.
Linking ice and gas in the lambda Orionis Barnard 35A cloud

Context. Dust grains play an important role in the synthesis of molecules in the interstellar medium, from the simplest species, such as H2, to complex organic molecules. How some of these solid-state molecules are converted into gas-phase species is still a matter of debate. Aims. Our aim is to directly compare ice and gas abundances of methanol (CH3OH) and carbon monoxide (CO) obtained from near-infrared (2.5-5 μm) and millimetre (1.3 mm) observations and to investigate the relationship between ice, dust, and gas in low-mass protostellar envelopes. Methods. We present Submillimeter Array (SMA) and Atacama Pathfinder EXperiment (APEX) observations of gas-phase CH3OH (JK = 5K-4K), 13CO, and C18O (J = 2-1) towards the multiple protostellar system IRAS 05417+0907, which is located in the B35A cloud, λ Orionis region. We use archival IRAM 30 m data and AKARI H2O, CO, and CH3OH ice observations towards the same target to compare ice and gas abundances and directly calculate CH3OH and CO gas-to-ice ratios. Results. The CO isotopologue emissions are extended, whereas the CH3OH emission is compact and traces the giant molecular outflow emanating from IRAS 05417+0907. A discrepancy between sub-millimetre dust emission and H2O ice column density is found for B35A-4 and B35A-5, similar to what has previously been reported. B35A-2 and B35A-3 are located where the sub-millimetre dust emission peaks and show H2O column densities lower than that of B35A-4. Conclusions. The difference between the sub-millimetre continuum emission and the infrared H2O ice observations suggests that the distributions of dust and H2O ice differ around the young stellar objects in this dense cloud. The reason for this may be that the four sources are located in different environments resolved by the interferometric observations: B35A-2, B35A-3, and, in particular, B35A-5 are situated in a shocked region that is plausibly affected by sputtering and heating, which in turn impacts the sub-millimetre dust emission pattern, while B35A-4 is situated in a more quiescent part of the cloud. Gas and ice maps are essential for connecting small-scale variations in the ice composition with the large-scale astrophysical phenomena probed by gas observations.
Perturbation-Tolerant Structural Controllability for Linear Systems

This paper proposes a notion termed perturbation-tolerant structural controllability (PTSC) to study the generic property of controllability preservation/resilience of structured linear systems under structured perturbations. A structured system is said to be PTSC with respect to a perturbation structure if for almost all controllable realizations of this system, there are no complex-valued perturbations obeying the zero/nonzero pattern prescribed by the perturbation structure that can make the perturbed systems uncontrollable. We prove a generic property in this notion, that for almost all controllable realizations of a structured system, either there exist such structured perturbations rendering the systems uncontrollable, or there are no such perturbations. We present a decomposition-based necessary and sufficient condition for the PTSC of single-input linear systems, whose verification has polynomial time complexity. We then discuss some intuitive graph-theoretic conditions for PTSC. As an application, our results can serve as some feasibility conditions for the conventional structured controllability radius problems from a generic viewpoint.
Unexpected Short-period Variability in Dwarf Carbon Stars from the Zwicky Transient Facility

Dwarf carbon (dC) stars, main-sequence stars showing carbon molecular bands, are enriched by mass transfer from a previous asymptotic-giant-branch (AGB) companion, which has since evolved to a white dwarf. While previous studies have found radial-velocity variations for large samples of dCs, there are still relatively few dC orbital periods in the literature and no dC eclipsing binaries have yet been found. Here, we analyze photometric light curves from DR5 of the Zwicky Transient Facility for a sample of 944 dC stars. From these light curves, we identify 34 periodically variable dC stars. Remarkably, of the periodic dCs, 82% have periods less than two days. We also provide spectroscopic follow-up for four of these periodic systems, measuring radial velocity variations in three of them. Short-period dCs are almost certainly post-common-envelope binary systems, because the periodicity is most likely related to the orbital period, with tidally locked rotation and photometric modulation on the dC either from spots or from ellipsoidal variations. We discuss evolutionary scenarios that these binaries may have taken to accrete sufficient C-rich material while avoiding truncation of the thermally pulsing AGB phase needed to provide such material in the first place. We compare these dCs to common-envelope models to show that dC stars probably cannot accrete enough C-rich material during the common-envelope phase, suggesting another mechanism like wind-Roche lobe overflow is necessary. The periodic dCs in this paper represent a prime sample for spectroscopic follow-up and for comparison to future models of wind-Roche lobe overflow mass transfer.
Three Shades of Partial Protection in Elastic Optical Networks

Partial protection strategies based on the observation that in failure events, a service can tolerate a certain amount of degradation and therefore by reducing the protection traffic in the network, better spectrum utilization could be attained. Such concept has been widely studied in the traditional WDM context and yet has been somehow faded due to the fact that fixed transmission technologies allow small room for spectral improvement. This paper aims to renew the interest in partial protection and re-adapt it to the context of elastic optical networks. The evolutionary perspective we lay out in this paper identifies a new route for achieving greater spectral efficiency in a pragmatic way by simply differentiating the protection services for each demand rather than the uniform treatment for all demands. In doing so, we present a new research problem entitled, routing, modulation level, spectrum and protection service assignment which is an extension of the well-established one, that is, routing, modulation level, and spectrum assignment as the (partial) protection service for each demand is taken into account and optimized. Three variants of that problem reflecting shades of applying partial protection are covered in details. Specifically, the first one considers the intuitive case as the relative amount of protection traffic for each demand is given as the input to the network planning process while the second one is dedicated to the special case of enforcing the same figure of partial protection for all demands. More interesting is brought in the third variant where given the service level agreement for the total network traffic, we provide the optimal solution that determine the protection service for each individual demand so as to minimize the spectral occupancy.
A Lightweight Deep Learning-Based Cloud Detection Method for Sentinel-2A Imagery Fusing Multiscale Spectral and Spatial Features

Clouds are a very important factor in the availability of optical remote sensing images. Recently, deep learning (DL)-based cloud detection methods have surpassed classical methods based on rules and physical models of clouds. However, most of these deep models are very large, which limits their applicability and explainability, while other models do not make use of the full spectral information in multispectral images, such as Sentinel-2. In this article, we propose a lightweight network for cloud detection, fusing multiscale spectral and spatial features (CD-FM3SFs) and tailored for processing all spectral bands in Sentinel-2A images. The proposed method consists of an encoder and a decoder. In the encoder, three input branches are designed to handle spectral bands at their native resolution and extract multiscale spectral features. Three novel components are designed: a mixed depthwise separable convolution (MDSC) and a shared and dilated residual block (SDRB) to extract multiscale spatial features, and a concatenation and sum (CS) operation to fuse multiscale spectral and spatial features with little calculation and no additional parameters. The decoder of CD-FM3SF outputs three cloud masks at the same resolution as input bands to enhance the supervision information of small, middle, and large clouds. To validate the performance of the proposed method, we manually labeled 36 Sentinel-2A scenes evenly distributed over mainland China. The experiment results demonstrate that CD-FM3SF outperforms traditional cloud detection methods and state-of-the-art DL-based methods in both accuracy and speed.
Improving Landslide Detection on SAR Data Through Deep Learning

In this letter, we use deep learning convolutional neural networks (CNNs) to compare the landslide mapping and classification performances of optical images (from Sentinel-2) and synthetic aperture radar (SAR) images (from Sentinel-1). The training, validation, and test zones used to independently evaluate the performance of the CNN on different datasets are located in the eastern Iburi subprefecture in Hokkaido, where, at 03.08 local time (JST) on September 6, 2018, an Mw 6.6 earthquake triggered about 8000 coseismic landslides. We analyzed the conditions before and after the earthquake exploiting multipolarization SAR as well as optical data by means of a CNN implemented in TensorFlow that points out the locations where the landslide class is predicted as more likely. As expected, the CNN runs on optical images proved itself excellent for the landslide detection task, achieving an overall accuracy of 98.96%, while CNNs based on the combination of ground range detected (GRD) SAR data reached overall accuracies beyond 95%. Our findings show that the integrated use of SAR data may also allow for rapid detection even during storms and under dense cloud cover and provides comparable accuracy to classical optical change detection in landslide recognition and detection.