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In this paper, we apply a method to quantify biases associated with named entities from various countries. We create counterfactual examples with small perturbations on target-domain data instead of relying on templates or specific datasets for bias detection. On widely used classifiers for subjectivity analysis, including sentiment, emotion, hate speech, and offensive text using Twitter data, our results demonstrate positive biases related to the language spoken in a country across all classifiers studied. Notably, the presence of certain country names in a sentence can strongly influence predictions, up to a 23\% change in hate speech detection and up to a 60\% change in the prediction of negative emotions such as anger. We hypothesize that these biases stem from the training data of pre-trained language models (PLMs) and find correlations between affect predictions and PLMs likelihood in English and unknown languages like Basque and Maori, revealing distinct patterns with exacerbate correlations. Further, we followed these correlations in-between counterfactual examples from a same sentence to remove the syntactical component, uncovering interesting results suggesting the impact of the pre-training data was more important for English-speaking-country names. Our anonymized code is [https://anonymous.4open.science/r/biases_ppl-576B/README.md](available here).

The Near-Infrared Spectrometer and Photometer (NISP) on board the Euclid satellite provides multiband photometry and R>=450 slitless grism spectroscopy in the 950-2020nm wavelength range. In this reference article we illuminate the background of NISP's functional and calibration requirements, describe the instrument's integral components, and provide all its key properties. We also sketch the processes needed to understand how NISP operates and is calibrated, and its technical potentials and limitations. Links to articles providing more details and technical background are included. NISP's 16 HAWAII-2RG (H2RG) detectors with a plate scale of 0.3" pix^-1 deliver a field-of-view of 0.57deg^2. In photo mode, NISP reaches a limiting magnitude of ~24.5AB mag in three photometric exposures of about 100s exposure time, for point sources and with a signal-to-noise ratio (SNR) of 5. For spectroscopy, NISP's point-source sensitivity is a SNR = 3.5 detection of an emission line with flux ~2x10^-16erg/s/cm^2 integrated over two resolution elements of 13.4A, in 3x560s grism exposures at 1.6 mu (redshifted Ha). Our calibration includes on-ground and in-flight characterisation and monitoring of detector baseline, dark current, non-linearity, and sensitivity, to guarantee a relative photometric accuracy of better than 1.5%, and relative spectrophotometry to better than 0.7%. The wavelength calibration must be better than 5A. NISP is the state-of-the-art instrument in the NIR for all science beyond small areas available from HST and JWST - and an enormous advance due to its combination of field size and high throughput of telescope and instrument. During Euclid's 6-year survey covering 14000 deg^2 of extragalactic sky, NISP will be the backbone for determining distances of more than a billion galaxies. Its NIR data will become a rich reference imaging and spectroscopy data set for the coming decades., Comment: Paper submitted as part of the A&A special issue 'Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'

We present an executable, proven-safe, faithful, and future-proof Coq mechanization of JavaScript regular expression (regex) matching, as specified by the latest published edition of ECMA-262 section 22.2. This is, to our knowledge, the first time that an industrial-strength regex language has been faithfully mechanized in an interactive theorem prover. We highlight interesting challenges that arose in the process (including issues of encoding, corner cases, and executability), and we document the steps that we took to ensure that the result is straightforwardly auditable and that our understanding of the specification aligns with existing implementations. We demonstrate the usability and versatility of the mechanization through a broad collection of analyses, case studies, and experiments: we prove that JavaScript regex matching always terminates and is safe (no assertion failures); we identify subtle corner cases that led to mistakes in previous publications; we verify an optimization extracted from a state-of-the-art regex engine; we show that some classic properties described in automata textbooks and used in derivatives-based matchers do not hold in JavaScript regexes; and we demonstrate that the cost of updating the mechanization to account for changes in the original specification is reasonably low. Our mechanization can be extracted to OCaml and JavaScript and linked with Unicode libraries to produce an executable regex engine that passes the relevant parts of the official Test262 conformance test suite.

Early wildfire detection is of the utmost importance to enable rapid response efforts, and thus minimize the negative impacts of wildfire spreads. To this end, we present \Pyro, a web-scraping-based dataset composed of videos of wildfires from a network of cameras that were enhanced with manual bounding-box-level annotations. Our dataset was filtered based on a strategy to improve the quality and diversity of the data, reducing the final data to a set of 10,000 images. We ran experiments using a state-of-the-art object detection model and found out that the proposed dataset is challenging and its use in concordance with other public dataset helps to reach higher results overall. We will make our code and data publicly available., Comment: Preprint of ongoing work

Modern regex languages have strayed far from well-understood traditional regular expressions: they include features that fundamentally transform the matching problem. In exchange for these features, modern regex engines at times suffer from exponential complexity blowups, a frequent source of denial-of-service vulnerabilities in JavaScript applications. Worse, regex semantics differ across languages, and the impact of these divergences on algorithmic design and worst-case matching complexity has seldom been investigated. This paper provides a novel perspective on JavaScript's regex semantics by identifying a larger-than-previously-understood subset of the language that can be matched with linear time guarantees. In the process, we discover several cases where state-of-the-art algorithms were either wrong (semantically incorrect), inefficient (suffering from superlinear complexity) or excessively restrictive (assuming certain features could not be matched linearly). We introduce novel algorithms to restore correctness and linear complexity. We further advance the state-of-the-art in linear regex matching by presenting the first nonbacktracking algorithms for matching lookarounds in linear time: one supporting captureless lookbehinds in any regex language, and another leveraging a JavaScript property to support unrestricted lookaheads and lookbehinds. Finally, we describe new time and space complexity tradeoffs for regex engines. All of our algorithms are practical: we validated them in a prototype implementation, and some have also been merged in the V8 JavaScript implementation used in Chrome and Node.js.

We propose a general method to break down a main complex task into a set of intermediary easier sub-tasks, which are formulated in natural language as binary questions related to the final target task. Our method allows for representing each example by a vector consisting of the answers to these questions. We call this representation Natural Language Learned Features (NLLF). NLLF is generated by a small transformer language model (e.g., BERT) that has been trained in a Natural Language Inference (NLI) fashion, using weak labels automatically obtained from a Large Language Model (LLM). We show that the LLM normally struggles for the main task using in-context learning, but can handle these easiest subtasks and produce useful weak labels to train a BERT. The NLI-like training of the BERT allows for tackling zero-shot inference with any binary question, and not necessarily the ones seen during the training. We show that this NLLF vector not only helps to reach better performances by enhancing any classifier, but that it can be used as input of an easy-to-interpret machine learning model like a decision tree. This decision tree is interpretable but also reaches high performances, surpassing those of a pre-trained transformer in some cases.We have successfully applied this method to two completely different tasks: detecting incoherence in students' answers to open-ended mathematics exam questions, and screening abstracts for a systematic literature review of scientific papers on climate change and agroecology.

Artificial neural networks typically struggle in generalizing to out-of-context examples. One reason for this limitation is caused by having datasets that incorporate only partial information regarding the potential correlational structure of the world. In this work, we propose TIDA (Targeted Image-editing Data Augmentation), a targeted data augmentation method focused on improving models' human-like abilities (e.g., gender recognition) by filling the correlational structure gap using a text-to-image generative model. More specifically, TIDA identifies specific skills in captions describing images (e.g., the presence of a specific gender in the image), changes the caption (e.g., "woman" to "man"), and then uses a text-to-image model to edit the image in order to match the novel caption (e.g., uniquely changing a woman to a man while maintaining the context identical). Based on the Flickr30K benchmark, we show that, compared with the original data set, a TIDA-enhanced dataset related to gender, color, and counting abilities induces better performance in several image captioning metrics. Furthermore, on top of relying on the classical BLEU metric, we conduct a fine-grained analysis of the improvements of our models against the baseline in different ways. We compared text-to-image generative models and found different behaviors of the image captioning models in terms of encoding visual encoding and textual decoding.

Accurate in-season crop type classification is crucial for the crop production estimation and monitoring of agricultural parcels. However, the complexity of the plant growth patterns and their spatio-temporal variability present significant challenges. While current deep learning-based methods show promise in crop type classification from single- and multi-modal time series, most existing methods rely on a single modality, such as satellite optical remote sensing data or crop rotation patterns. We propose a novel approach to fuse multimodal information into a model for improved accuracy and robustness across multiple years and countries. The approach relies on three modalities used: remote sensing time series from Sentinel-2 and Landsat 8 observations, parcel crop rotation and local crop distribution. To evaluate our approach, we release a new annotated dataset of 7.4 million agricultural parcels in France and Netherlands. We associate each parcel with time-series of surface reflectance (Red and NIR) and biophysical variables (LAI, FAPAR). Additionally, we propose a new approach to automatically aggregate crop types into a hierarchical class structure for meaningful model evaluation and a novel data-augmentation technique for early-season classification. Performance of the multimodal approach was assessed at different aggregation level in the semantic domain spanning from 151 to 8 crop types or groups. It resulted in accuracy ranging from 91\% to 95\% for NL dataset and from 85\% to 89\% for FR dataset. Pre-training on a dataset improves domain adaptation between countries, allowing for cross-domain zero-shot learning, and robustness of the performances in a few-shot setting from France to Netherlands. Our proposed approach outperforms comparable methods by enabling learning methods to use the often overlooked spatio-temporal context of parcels, resulting in increased preci..., Comment: submitted to Remote Sensing of Environment, special issue Deep Learning for Time Series. Version 2

We present the Touch\'e23-ValueEval Dataset for Identifying Human Values behind Arguments. To investigate approaches for the automated detection of human values behind arguments, we collected 9324 arguments from 6 diverse sources, covering religious texts, political discussions, free-text arguments, newspaper editorials, and online democracy platforms. Each argument was annotated by 3 crowdworkers for 54 values. The Touch\'e23-ValueEval dataset extends the Webis-ArgValues-22. In comparison to the previous dataset, the effectiveness of a 1-Baseline decreases, but that of an out-of-the-box BERT model increases. Therefore, though the classification difficulty increased as per the label distribution, the larger dataset allows for training better models.

Modern Just-in-Time compilers (or JITs) typically interleave several mechanisms to execute a program. For faster startup times and to observe the initial behavior of an execution, interpretation can be initially used. But after a while, JITs dynamically produce native code for parts of the program they execute often. Although some time is spent compiling dynamically, this mechanism makes for much faster times for the remaining of the program execution. Such compilers are complex pieces of software with various components, and greatly rely on a precise interplay between the different languages being executed, including on-stack-replacement. Traditional static compilers like CompCert have been mechanized in proof assistants, but JITs have been scarcely formalized so far, partly due to their impure nature and their numerous components. This work presents a model JIT with dynamic generation of native code, implemented and formally verified in Coq. Although some parts of a JIT cannot be written in Coq, we propose a proof methodology to delimit, specify and reason on the impure effects of a JIT. We argue that the daunting task of formally verifying a complete JIT should draw on existing proofs of native code generation. To this end, our work successfully reuses CompCert and its correctness proofs during dynamic compilation. Finally, our prototype can be extracted and executed., Comment: Proceedings of the ACM on Programming Languages, 2023

The NISP (Near Infrared Spectrometer and Photometer) is one of the two Euclid instruments. It operates in the near-IR spectral region (950-2020nm) as a photometer and spectrometer. The instrument is composed of: a cold (135 K) optomechanical subsystem consisting of a Silicon carbide structure, an optical assembly, a filter wheel mechanism, a grism wheel mechanism, a calibration unit, and a thermal control system, a detection system based on a mosaic of 16 H2RG with their front-end readout electronic, and a warm electronic system (290 K) composed of a data processing / detector control unit and of an instrument control unit that interfaces with the spacecraft via a 1553 bus for command and control and via Spacewire links for science data. This paper presents: the final architecture of the flight model instrument and subsystems, and the performance and the ground calibration measurement done at NISP level and at Euclid Payload Module level at operational cold temperature., Comment: 18 pages, to appear in Proceedings of the SPIE

Accurate, detailed, and timely crop type mapping is a very valuable information for the institutions in order to create more accurate policies according to the needs of the citizens. In the last decade, the amount of available data dramatically increased, whether it can come from Remote Sensing (using Copernicus Sentinel-2 data) or directly from the farmers (providing in-situ crop information throughout the years and information on crop rotation). Nevertheless, the majority of the studies are restricted to the use of one modality (Remote Sensing data or crop rotation) and never fuse the Earth Observation data with domain knowledge like crop rotations. Moreover, when they use Earth Observation data they are mainly restrained to one year of data, not taking into account the past years. In this context, we propose to tackle a land use and crop type classification task using three data types, by using a Hierarchical Deep Learning algorithm modeling the crop rotations like a language model, the satellite signals like a speech signal and using the crop distribution as additional context vector. We obtained very promising results compared to classical approaches with significant performances, increasing the Accuracy by 5.1 points in a 28-class setting (.948), and the micro-F1 by 9.6 points in a 10-class setting (.887) using only a set of crop of interests selected by an expert. We finally proposed a data-augmentation technique to allow the model to classify the crop before the end of the season, which works surprisingly well in a multimodal setting., Comment: accepted to CECEO22@IJCAI

Due to the high cost and reliability of sensors, the designers of a pump reduce the needed number of sensors for the estimation of the feasible operating point as much as possible. The major challenge to obtain a good estimation is the low amount of data available. Using this amount of data, the performance of the estimation method is not enough to satisfy the client requests. To solve this problem of scarcity of data, getting high quality data is important to obtain a good estimation. Based on these considerations, we develop an active learning framework for estimating the operating point of a Modular Multi Pump used in energy field. In particular we focus on the estimation of the surge distance. We apply Active learning to estimate the surge distance with minimal dataset. Results report that active learning is a valuable technique also for real application.

The Beam Expander Testing X-ray facility (BEaTriX) is a unique X-ray apparatus now operated at the Istituto Nazionale di Astrofisica (INAF), Osservatorio Astronomico di Brera (OAB), in Merate, Italy. It has been specifically designed to measure the point spread function (PSF) and the effective area (EA) of the X-ray mirror modules (MMs) of the Advanced Telescope for High-ENergy Astrophysics (ATHENA), based on silicon pore optics (SPO) technology, for verification before integration into the mirror assembly. To this end, BEaTriX generates a broad, uniform, monochromatic, and collimated X-ray beam at 4.51 keV. [...] In BEaTriX, a micro-focus X-ray source with a titanium anode is placed in the focus of a paraboloidal mirror, which generates a parallel beam. A crystal monochromator selects the 4.51 keV line, which is expanded to the final size by a crystal asymmetrically cut with respect to the crystalline planes. [...] After characterization, the BEaTriX beam has the nominal dimensions of 170 mm x 60 mm, with a vertical divergence of 1.65 arcsec and a horizontal divergence varying between 2.7 and 3.45 arcsec, depending on the monochromator setting: either high collimation or high intensity. The flux per area unit varies from 10 to 50 photons/s/cm2 from one configuration to the other. The BEaTriX beam performance was tested using an SPO MM, whose entrance pupil was fully illuminated by the expanded beam, and its focus was directly imaged onto the camera. The first light test returned a PSF and an EA in full agreement with expectations. As of today, the 4.51 keV beamline of BEaTriX is operational and can characterize modular X-ray optics, measuring their PSF and EA with a typical exposure of 30 minutes. [...] We expect BEaTriX to be a crucial facility for the functional test of modular X-ray optics, such as the SPO MMs for ATHENA., Comment: Accepted on 29 June 2022 for publication in Astronomy & Astrophysics, Sect. 13, Astronomical instrumentation of Astronomy and Astrophysics. Version after language correction

Silicon Pore Optics (SPO) uses commercially available monocrystalline double-sided super-polished silicon wafers as a basis to produce mirrors that form lightweight high-resolution X-ray optics. The technology has been invented by cosine Measurement Systems and the European Space Agency (ESA) and developed together with scientific and industrial partners to mass production levels. It leverages techniques and processes developed over decades by the semiconductor industry to handle, process, and clean silicon wafers and plates. SPO is an enabling technology for large space-borne X-ray telescopes such as Athena and ARCUS, operating in the 0.2 to 12 keV band, with angular resolution aiming for 5 arc seconds. SPO has also shown to be a versatile technology that can be further developed for gamma-ray optics, medical applications and for material research., Comment: 37 pages, 41 figures, invited contribution to Section 2 of the Handbook for X-ray and Gamma-ray Astrophysics, "Optics for X-ray Astrophysics", section editors: Jessica Gaskin, Daniele Spiga, and Rene Hudec

This article describes the setup and performance of the near and far detectors in the Double Chooz experiment. The electron antineutrinos of the Chooz nuclear power plant were measured in two identically designed detectors with different average baselines of about 400 m and 1050 m from the two reactor cores. Over many years of data taking the neutrino signals were extracted from interactions in the detectors with the goal of measuring a fundamental parameter in the context of neutrino oscillation, the mixing angle {\theta}13. The central part of the Double Chooz detectors was a main detector comprising four cylindrical volumes filled with organic liquids. From the inside towards the outside there were volumes containing gadolinium-loaded scintillator, gadolinium-free scintillator, a buffer oil and, optically separated, another liquid scintillator acting as veto system. Above this main detector an additional outer veto system using plastic scintillator strips was installed. The technologies developed in Double Chooz were inspiration for several other antineutrino detectors in the field. The detector design allowed implementation of efficient background rejection techniques including use of pulse shape information provided by the data acquisition system. The Double Chooz detectors featured remarkable stability, in particular for the detected photons, as well as high radiopurity of the detector components., Comment: 49 pages, 29 figures

In a classification task, dealing with text snippets and metadata usually requires dealing with multimodal approaches. When those metadata are textual, it is tempting to use them intrinsically with a pre-trained transformer, in order to leverage the semantic information encoded inside the model. This paper describes how to improve a humanitarian classification task by adding the crisis event type to each tweet to be classified. Based on additional experiments of the model weights and behavior, it identifies how the proposed neural network approach is partially over-fitting the particularities of the Crisis Benchmark, to better highlight how the model is still undoubtedly learning to use and take advantage of the metadata's textual semantics., Comment: Oral ISCRAM2021

Tweets are specific text data when compared to general text. Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult to find huge annotated corpora for non-English languages. The recent rise of the transformer models in Natural Language Processing allows to achieve unparalleled performances in many tasks, but these models need a consequent quantity of text to adapt to the tweet domain. We propose the use of a multilingual transformer model, that we pre-train over English tweets and apply data-augmentation using automatic translation to adapt the model to non-English languages. Our experiments in French, Spanish, German and Italian suggest that the proposed technique is an efficient way to improve the results of the transformers over small corpora of tweets in a non-English language., Comment: Accepted to COLING2020

We present a search for signatures of neutrino mixing of electron anti-neutrinos with additional hypothetical sterile neutrino flavors using the Double Chooz experiment. The search is based on data from 5 years of operation of Double Chooz, including 2 years in the two-detector configuration. The analysis is based on a profile likelihood, i.e.\ comparing the data to the model prediction of disappearance in a data-to-data comparison of the two respective detectors. The analysis is optimized for a model of three active and one sterile neutrino. It is sensitive in the typical mass range $5 \cdot 10^{-3} $ eV$^2 \lesssim \Delta m^2_{41} \lesssim 3\cdot 10^{-1} $ eV$^2$ for mixing angles down to $\sin^2 2\theta_{14} \gtrsim 0.02$. No significant disappearance additionally to the conventional disappearance related to $\theta_{13} $ is observed and correspondingly exclusion bounds on the sterile mixing parameter $\theta_{14} $ as function of $ \Delta m^2_{41} $ are obtained., Comment: accepted for publication by EPJC

In this paper we present a new method to learn a model robust to typos for a Named Entity Recognition task. Our improvement over existing methods helps the model to take into account the context of the sentence inside a court decision in order to recognize an entity with a typo. We used state-of-the-art models and enriched the last layer of the neural network with high-level information linked with the potential of the word to be a certain type of entity. More precisely, we utilized the similarities between the word and the potential entity candidates in the tagged sentence context. The experiments on a dataset of French court decisions show a reduction of the relative F1-score error of 32%, upgrading the score obtained with the most competitive fine-tuned state-of-the-art system from 94.85% to 96.52%., Comment: accepted at NoDaLiDa'19, Turku, Finland

In this paper, the main goal is to detect a movie reviewer's opinion using hidden conditional random fields. This model allows us to capture the dynamics of the reviewer's opinion in the transcripts of long unsegmented audio reviews that are analyzed by our system. High level linguistic features are computed at the level of inter-pausal segments. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the ICT-MMMO corpus. We obtain a F1-score of 82\%, which is better than logistic regression and recurrent neural network approaches. We also offer a discussion that sheds some light on the capacity of our system to adapt the word embedding model learned from general written texts data to spoken movie reviews and thus model the dynamics of the opinion., Comment: Oral Interspeech 2017

We study dynamic changes of agents' observational power in logics of knowledge and time. We consider CTL*K, the extension of CTL* with knowledge operators, and enrich it with a new operator that models a change in an agent's way of observing the system. We extend the classic semantics of knowledge for perfect-recall agents to account for changes of observation, and we show that this new operator strictly increases the expressivity of CTL*K. We reduce the model-checking problem for our logic to that for CTL*K, which is known to be decidable. This provides a solution to the model-checking problem for our logic, but its complexity is not optimal. Indeed we provide a direct decision procedure with better complexity.

Ballistic injection in a nanodevice is a complex process where electrons can either be transmitted or reflected, thereby introducing deviations from the otherwise quantized conductance. In this context, quantum rings (QRs) appear as model geometries: in a semiclassical view, most electrons bounce against the central QR antidot, which strongly reduces injection efficiency. Thanks to an analogy with Rutherford scattering, we show that a local partial depletion of the QR close to the edge of the antidot can counter-intuitively ease ballistic electron injection. On the contrary, local charge accumulation can focus the semi-classical trajectories on the hard-wall potential and strongly enhance reflection back to the lead. Scanning gate experiments on a ballistic QR, and simulations of the conductance of the same device are consistent, and agree to show that the effect is directly proportional to the ratio between the strength of the perturbation and the Fermi energy. Our observation surprisingly fits the simple Rutherford formalism in two-dimensions in the classical limit.

The yields and production rates of the radioisotopes $^9$Li and $^8$He created by cosmic muon spallation on $^{12}$C, have been measured by the two detectors of the Double Chooz experiment. The identical detectors are located at separate sites and depths, which means they are subject to different muon spectra. The near (far) detector has an overburden of $\sim$120 m.w.e. ($\sim$300 m.w.e.) corresponding to a mean muon energy of $32.1\pm2.0\,\mathrm{GeV}$ ($63.7\pm5.5\,\mathrm{GeV}$). Comparing the data to a detailed simulation of the $^9$Li and $^8$He decays, the contribution of the $^8$He radioisotope at both detectors is found to be compatible with zero. The observed $^9$Li yields in the near and far detectors are $5.51\pm0.51$ and $7.90\pm0.51$, respectively, in units of $10^{-8}\mu ^{-1} \mathrm{g^{-1} cm^{2} }$. The shallow overburdens of the near and far detectors give a unique insight when combined with measurements by KamLAND and Borexino to give the first multi--experiment, data driven relationship between the $^9$Li yield and the mean muon energy according to the power law $Y = Y_0( / 1\,\mathrm{GeV})^{\overline{\alpha}}$, giving $\overline{\alpha}=0.72\pm0.06$ and $Y_0=(0.43\pm0.11)\times 10^{-8}\mu ^{-1} \mathrm{g^{-1} cm^{2}}$. This relationship gives future liquid scintillator based experiments the ability to predict their cosmogenic $^9$Li background rates., Comment: 15 pages, 5 figures

The "Square Kilometre Array" (SKA) is a large international radio telescope project characterised, as suggested by its name, by a total collecting area of approximately one square kilometre, and consisting of several interferometric arrays to observe at metric and centimetric wavelengths. The deployment of the SKA will take place in two sites, in South Africa and Australia, and in two successive phases. From its Phase 1, the SKA will be one of the most formidable scientific machines ever deployed by mankind, and by far the most impressive in terms of data throughput and required computing power. With the participation of almost 200 authors from forty research institutes and six private companies, the publication of this French SKA white paper illustrates the strong involvement in the SKA project of the French astronomical community and of a rapidly growing number of major scientific and technological players in the fields of Big Data, high performance computing, energy production and storage, as well as system integration., Comment: Editor in chief: C. Ferrari; Editors: M. Alves, S. Bosse, S. Corbel, A. Ferrari, K. Ferri\`ere, S. Gauffre, E. Josselin, G. Lagache, S. Lambert, G. Marquette, J.-M. Martin, M.-A. Miville-Desch\^enes, L. Montier, B. Semelin, G. Theureau, S. Vergani, N. Vilmer, P. Zarka; Original file with high resolution figures at SKA-France link: https://ska-france.oca.eu/images/SKA-France-Media/FWB_051017.pdf

Understanding the origin of the flaring activity from the Galactic center supermassive black hole, Sagittarius A*, is a major scientific goal of the NuSTAR Galactic plane survey campaign. We report on the data obtained between July 2012 and April 2015, including 27 observations on Sgr A* with a total exposure of ~ 1 Ms. We found a total of ten X-ray flares detected in the NuSTAR observation window, with luminosities in the range of $L_{3-79~keV}$~$(0.2-4.0) \times 10^{35}~erg~s^{-1}$. With this largest hard X-ray Sgr A* flare dataset to date, we studied the flare spectral properties. Seven flares are detected above 5{\sigma} significance, showing a range of photon indices ({\Gamma} ~ 2.0-2.8) with typical uncertainties of +/-0.5 (90% confidence level). We found no significant spectral hardening for brighter flares as indicated by a smaller sample. The accumulation of all the flare spectra in 1-79 keV can be well fit with an absorbed power-law model with {\Gamma}=2.2+/-0.1, and does not require the existence of a spectral break. The lack of variation in X-ray spectral index with luminosity would point to a single mechanism for the flares and is consistent with the synchrotron scenario. Lastly, we present the quiescent state spectrum of Sgr A*, and derived an upper limit on the quiescent luminosity of Sgr A* above 10 keV to be $L_{Xq, 10-79 keV}$ < $(2.9{\pm}0.2) \times 10^{34}~erg~s^{-1}$., Comment: 11 pages, 4 figures, Accepted for publication in ApJ

We present the first survey results of hard X-ray point sources in the Galactic Center (GC) region by NuSTAR. We have discovered 70 hard (3-79 keV) X-ray point sources in a 0.6 deg^2 region around Sgr A* with a total exposure of 1.7 Ms, and 7 sources in the Sgr B2 field with 300 ks. We identify clear Chandra counterparts for 58 NuSTAR sources and assign candidate counterparts for the remaining 19. The NuSTAR survey reaches X-ray luminosities of ~4 x and ~8 x 10^32 erg s^-1 at the GC (8 kpc) in the 3-10 and 10-40 keV bands, respectively. The source list includes three persistent luminous X-ray binaries and the likely run-away pulsar called the Cannonball. New source-detection significance maps reveal a cluster of hard (>10 keV) X-ray sources near the Sgr A diffuse complex with no clear soft X-ray counterparts. The severe extinction observed in the Chandra spectra indicates that all the NuSTAR sources are in the central bulge or are of extragalactic origin. Spectral analysis of relatively bright NuSTAR sources suggests that magnetic cataclysmic variables constitute a large fraction (>40-60%). Both spectral analysis and logN-logS distributions of the NuSTAR sources indicate that the X-ray spectra of the NuSTAR sources should have kT > 20 keV on average for a single temperature thermal plasma model or an average photon index of Gamma = 1.5 - 2 for a power-law model. These findings suggest that the GC X-ray source population may contain a larger fraction of X-ray binaries with high plasma temperatures than the field population., Comment: 32 pages, 15 figures, 7 tables, accepted for publication in ApJ

Using the Double Chooz detector, designed to measure the neutrino mixing angle $\theta_{13}$, the products of $\mu^-$ capture on $^{12}$C, $^{13}$C, $^{14}$N and $^{16}$O have been measured. Over a period of 489.5 days, $2.3\times10^6$ stopping cosmic $\mu^-$ have been collected, of which $1.8\times10^5$ captured on carbon, nitrogen, or oxygen nuclei in the inner detector scintillator or acrylic vessels. The resulting isotopes were tagged using prompt neutron emission (when applicable), the subsequent beta decays, and, in some cases, $\beta$-delayed neutrons. The most precise measurement of the rate of $^{12}\mathrm C(\mu^-,\nu)^{12}\mathrm B$ to date is reported: $6.57^{+0.11}_{-0.21}\times10^{3}\,\mathrm s^{-1}$, or $(17.35^{+0.35}_{-0.59})\%$ of nuclear captures. By tagging excited states emitting gammas, the ground state transition rate to $^{12}$B has been determined to be $5.68^{+0.14}_{-0.23}\times10^3\,\mathrm s^{-1}$. The heretofore unobserved reactions $^{12}\mathrm C(\mu^-,\nu\alpha)^{8}\mathrm{Li}$, $^{13}\mathrm C(\mu^-,\nu\mathrm n\alpha)^{8}\mathrm{Li}$, and $^{13}\mathrm C(\mu^-,\nu\mathrm n)^{12}\mathrm B$ are measured. Further, a population of $\beta$n decays following stopping muons is identified with $5.5\sigma$ significance. Statistics limit our ability to identify these decays definitively. Assuming negligible production of $^{8}$He, the reaction $^{13}\mathrm C(\mu^-,\nu\alpha)^{9}\mathrm{Li}$ is found to be present at the $2.7\sigma$ level. Limits are set on a variety of other processes., Comment: 21 pages, 14 figures. Minor revisions. Corresponding author: M. Strait

The Double Chooz collaboration presents a measurement of the neutrino mixing angle $\theta_{13}$ using reactor $\overline{\nu}_{e}$ observed via the inverse beta decay reaction in which the neutron is captured on hydrogen. This measurement is based on 462.72 live days data, approximately twice as much data as in the previous such analysis, collected with a detector positioned at an average distance of 1050m from two reactor cores. Several novel techniques have been developed to achieve significant reductions of the backgrounds and systematic uncertainties. Accidental coincidences, the dominant background in this analysis, are suppressed by more than an order of magnitude with respect to our previous publication by a multi-variate analysis. These improvements demonstrate the capability of precise measurement of reactor $\overline{\nu}_{e}$ without gadolinium loading. Spectral distortions from the $\overline{\nu}_{e}$ reactor flux predictions previously reported with the neutron capture on gadolinium events are confirmed in the independent data sample presented here. A value of $\sin^{2}2\theta_{13} = 0.095^{+0.038}_{-0.039}$(stat+syst) is obtained from a fit to the observed event rate as a function of the reactor power, a method insensitive to the energy spectrum shape. A simultaneous fit of the hydrogen capture events and of the gadolinium capture events yields a measurement of $\sin^{2}2\theta_{13} = 0.088\pm0.033$(stat+syst).

We present the first sub-arcminute images of the Galactic Center above 10 keV, obtained with NuSTAR. NuSTAR resolves the hard X-ray source IGR J17456-2901 into non-thermal X-ray filaments, molecular clouds, point sources and a previously unknown central component of hard X-ray emission (CHXE). NuSTAR detects four non-thermal X-ray filaments, extending the detection of their power-law spectra with $\Gamma\sim1.3$-$2.3$ up to ~50 keV. A morphological and spectral study of the filaments suggests that their origin may be heterogeneous, where previous studies suggested a common origin in young pulsar wind nebulae (PWNe). NuSTAR detects non-thermal X-ray continuum emission spatially correlated with the 6.4 keV Fe K$\alpha$ fluorescence line emission associated with two Sgr A molecular clouds: MC1 and the Bridge. Broad-band X-ray spectral analysis with a Monte-Carlo based X-ray reflection model self-consistently determined their intrinsic column density ($\sim10^{23}$ cm$^{-2}$), primary X-ray spectra (power-laws with $\Gamma\sim2$) and set a lower limit of the X-ray luminosity of Sgr A* flare illuminating the Sgr A clouds to $L_X \stackrel{>}{\sim} 10^{38}$ erg s$^{-1}$. Above ~20 keV, hard X-ray emission in the central 10 pc region around Sgr A* consists of the candidate PWN G359.95-0.04 and the CHXE, possibly resulting from an unresolved population of massive CVs with white dwarf masses $M_{\rm WD} \sim 0.9 M_{\odot}$. Spectral energy distribution analysis suggests that G359.95-0.04 is likely the hard X-ray counterpart of the ultra-high gamma-ray source HESS J1745-290, strongly favoring a leptonic origin of the GC TeV emission., Comment: 27 pages. Accepted for publication in the Astrophysical Journal

It has been shown that many networks associated with complex systems are small-world (they have both a large local clustering coefficient and a small diameter) and they are also scale-free (the degrees are distributed according to a power law). Moreover, these networks are very often hierarchical, as they describe the modularity of the systems that are modeled. Most of the studies for complex networks are based on stochastic methods. However, a deterministic method, with an exact determination of the main relevant parameters of the networks, has proven useful. Indeed, this approach complements and enhances the probabilistic and simulation techniques and, therefore, it provides a better understanding of the systems modeled. In this paper we find the radius, diameter, clustering coefficient and degree distribution of a generic family of deterministic hierarchical small-world scale-free networks that has been considered for modeling real-life complex systems.

We present a new way of describing the flares from Sgr A* with a self-consistent calculation of the particle distribution. All relevant radiative processes are taken into account in the evolution of the electron distribution and resulting spectrum. We present spectral modelling for new X-ray flares observed by NuSTAR, together with older observations in different wavelengths, and discuss the changes in plasma parameters to produce a flare. We show that under certain conditions, the real particle distribution can differ significantly from standard distributions assumed in most studies. We conclude that the flares are likely generated by magnetized plasma consistent with our understanding of the accretion flow. Including non-thermal acceleration, injection, escape, and cooling losses produces a spectrum with a break between the infrared and the X-ray, allowing a better simultaneous description of the different wavelengths. We favour the non-thermal synchrotron interpretation, assuming the infrared flare spectrum used is representative. We also consider the effects on Sgr A*s quiescent spectrum in the case of a density increase due to the G2 encounter with Sgr A*., Comment: 14 pages, 1 table, 10 figures

Here, we report on observations of two hard X-ray sources that were originally discovered with the INTEGRAL satellite: IGR J04059+5416 and IGR J08297-4250. We use the Chandra X-ray Observatory to localize the sources and then archival near-IR images to identify the counterparts. Both sources have counterparts in the catalog of extended 2 Micron All-Sky Survey sources, and the counterpart to IGR J04059+5416 has been previously identified as a galaxy. Thus, we place IGR J04059+5416 in the class of Active Galactic Nuclei (AGN), and we suggest that IGR J08297-4250 is also an AGN. If this identification is correct, the near-IR images suggest that the host galaxy of IGR J08297-4250 may be merging with a smaller nearby galaxy. For IGR J04059+5416, the 0.3-86 keV spectrum from Chandra and INTEGRAL is consistent with an absorbed power-law with a column density of N_H = 3.1(+2.0)(-1.5)e22 cm-2 and a photon index of Gamma = 1.4+/-0.7, and we suggest that it is a Seyfert galaxy. For IGR J08297-4250, the photon index is similar, Gamma = 1.5+/-0.8, but the source is highly absorbed (N_H = 6.1(+10.1)(-4.3)e23 cm-2)., Comment: 10 pages, 5 figures, 5 tables

We present a statistical analysis of the X-ray flux distribution of Sgr A* from the Chandra X-ray Observatory's 3 Ms Sgr A* X-ray Visionary Project (XVP) in 2012. Our analysis indicates that the observed X-ray flux distribution can be decomposed into a steady quiescent component, represented by a Poisson process with rate $Q=(5.24\pm0.08)\times10^{-3}$ cts s$^{-1},$ and a variable component, represented by a power law process ($dN/dF\propto F^{-\xi},$ $\xi=1.92_{-0.02}^{+0.03}$). This slope matches our recently-reported distribution of flare luminosities. The variability may also be described by a log-normal process with a median unabsorbed 2-8 keV flux of $1.8^{+0.9}_{-0.6}\times10^{-14}$ erg s$^{-1}$ cm$^{-2}$ and a shape parameter $\sigma=2.4\pm0.2,$ but the power law provides a superior description of the data. In this decomposition of the flux distribution, all of the intrinsic X-ray variability of Sgr A* (spanning at least three orders of magnitude in flux) can be attributed to flaring activity, likely in the inner accretion flow. We confirm that at the faint end, the variable component contributes ~10% of the apparent quiescent flux, as previously indicated by our statistical analysis of X-ray flares in these Chandra observations. Our flux distribution provides a new and important observational constraint on theoretical models of Sgr A*, and we use simple radiation models to explore the extent to which a statistical comparison of the X-ray and infrared can provide insights into the physics of the X-ray emission mechanism., Comment: 13 pages, 7 figures, accepted for publication in ApJ. Comments welcome