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The constrained minimization (respectively maximization) of directed distances and of related generalized entropies is a fundamental task in information theory as well as in the adjacent fields of statistics, machine learning, artificial intelligence, signal processing and pattern recognition. In our previous paper "A precise bare simulation approach to the minimization of some distances. I. Foundations", we obtained such kind of constrained optima by a new dimension-free precise bare (pure) simulation method, provided basically that (i) the underlying directed distance is of f-divergence type, and that (ii) this can be connected to a light-tailed probability distribution in a certain manner. In the present paper, we extend this approach such that constrained optimization problems of a very huge amount of directed distances and generalized entropies -- and beyond -- can be tackled by a newly developed dimension-free extended bare simulation method, for obtaining both optima as well as optimizers. Almost no assumptions (like convexity) on the set of constraints are needed, within our discrete setup of arbitrary dimension, and our method is precise (i.e., converges in the limit). For instance, we cover constrained optimizations of arbitrary f-divergences, Bregman distances, scaled Bregman distances and weighted Euclidean distances. The potential for wide-spread applicability is indicated, too; in particular, we deliver many recent references for uses of the involved distances/divergences in various different research fields (which may also serve as an interdisciplinary interface)., Comment: This work has been submitted to the IEEE for possible publication

The future Ricochet experiment aims to search for new physics in the electroweak sector by measuring the Coherent Elastic Neutrino-Nucleus Scattering process from reactor antineutrinos with high precision down to the sub-100 eV nuclear recoil energy range. While the Ricochet collaboration is currently building the experimental setup at the reactor site, it is also finalizing the cryogenic detector arrays that will be integrated into the cryostat at the Institut Laue Langevin in early 2024. In this paper, we report on recent progress from the Ge cryogenic detector technology, called the CryoCube. More specifically, we present the first demonstration of a 30~eVee (electron equivalent) baseline ionization resolution (RMS) achieved with an early design of the detector assembly and its dedicated High Electron Mobility Transistor (HEMT) based front-end electronics. This represents an order of magnitude improvement over the best ionization resolutions obtained on similar heat-and-ionization germanium cryogenic detectors from the EDELWEISS and SuperCDMS dark matter experiments, and a factor of three improvement compared to the first fully-cryogenic HEMT-based preamplifier coupled to a CDMS-II germanium detector. Additionally, we discuss the implications of these results in the context of the future Ricochet experiment and its expected background mitigation performance., Comment: 10 pages, 5 figures, 1 table

Influence operations are large-scale efforts to manipulate public opinion. The rapid detection and disruption of these operations is critical for healthy public discourse. Emergent AI technologies may enable novel operations which evade current detection methods and influence public discourse on social media with greater scale, reach, and specificity. New methods with inductive learning capacity will be needed to identify these novel operations before they indelibly alter public opinion and events. We develop an inductive learning framework which: 1) determines content- and graph-based indicators that are not specific to any operation; 2) uses graph learning to encode abstract signatures of coordinated manipulation; and 3) evaluates generalization capacity by training and testing models across operations originating from Russia, China, and Iran. We find that this framework enables strong cross-operation generalization while also revealing salient indicators$\unicode{x2013}$illustrating a generic approach which directly complements transductive methodologies, thereby enhancing detection coverage.

Coherent elastic neutrino-nucleus scattering (CE$\nu$NS) offers valuable sensitivity to physics beyond the Standard Model. The Ricochet experiment will use cryogenic solid-state detectors to perform a precision measurement of the CE$\nu$NS spectrum induced by the high neutrino flux from the Institut Laue-Langevin nuclear reactor. The experiment will employ an array of detectors, each with a mass of $\sim$30 g and a targeted energy threshold of 50 eV. Nine of these detectors (the "Q-Array") will be based on a novel Transition-Edge Sensor (TES) readout style, in which the TES devices are thermally coupled to the absorber using a gold wire bond. We present initial characterization of a Q-Array-style detector using a 1 gram silicon absorber, obtaining a baseline root-mean-square resolution of less than 40 eV., Comment: 18 pages, 8 figures

In the context of direct searches of sub-GeV Dark Matter particles with germanium detectors, the EDELWEISS collaboration has tested a new technique to tag ionizing events using NbSi transition edge athermal phonon sensors. The emission of the athermal phonons generated by the Neganov-Trofimov-Luke effect associated with the drift of electrons and holes through the detectors is used to tag ionization events generated in specific parts of the detector localized in front of the NbSi sensor and to reject by more than a factor 5 (at 90% C.L.) the background from heat-only events that dominates the spectrum above 3 keV. This method is able to improve by a factor 2.8 the previous limit on spin-independent interactions of 1 GeV/c2 WIMPs obtained with the same detector and data set but without this tagging technique., Comment: 13 pages, 11 figures

The future Ricochet experiment aims at searching for new physics in the electroweak sector by providing a high precision measurement of the Coherent Elastic Neutrino-Nucleus Scattering (CENNS) process down to the sub-100 eV nuclear recoil energy range. The experiment will deploy a kg-scale low-energy-threshold detector array combining Ge and Zn target crystals 8.8 meters away from the 58 MW research nuclear reactor core of the Institut Laue Langevin (ILL) in Grenoble, France. Currently, the Ricochet collaboration is characterizing the backgrounds at its future experimental site in order to optimize the experiment's shielding design. The most threatening background component, which cannot be actively rejected by particle identification, consists of keV-scale neutron-induced nuclear recoils. These initial fast neutrons are generated by the reactor core and surrounding experiments (reactogenics), and by the cosmic rays producing primary neutrons and muon-induced neutrons in the surrounding materials. In this paper, we present the Ricochet neutron background characterization using $^3$He proportional counters which exhibit a high sensitivity to thermal, epithermal and fast neutrons. We compare these measurements to the Ricochet Geant4 simulations to validate our reactogenic and cosmogenic neutron background estimations. Eventually, we present our estimated neutron background for the future Ricochet experiment and the resulting CENNS detection significance., Comment: 14 pages, 14 figures, 1 table

Decision making theories such as Fuzzy-Trace Theory (FTT) suggest that individuals tend to rely on gist, or bottom-line meaning, in the text when making decisions. In this work, we delineate the process of developing GisPy, an open-source tool in Python for measuring the Gist Inference Score (GIS) in text. Evaluation of GisPy on documents in three benchmarks from the news and scientific text domains demonstrates that scores generated by our tool significantly distinguish low vs. high gist documents. Our tool is publicly available to use at: https://github.com/phosseini/GisPy., Comment: Accepted to the 4th Workshop on Narrative Understanding @ NAACL 2022

Coherent elastic neutrino-nucleus scattering (CE$\nu$NS) is a process in which neutrinos scatter on a nucleus which acts as a single particle. Though the total cross section is large by neutrino standards, CE$\nu$NS has long proven difficult to detect, since the deposited energy into the nucleus is $\sim$ keV. In 2017, the COHERENT collaboration announced the detection of CE$\nu$NS using a stopped-pion source with CsI detectors, followed up the detection of CE$\nu$NS using an Ar target. The detection of CE$\nu$NS has spawned a flurry of activities in high-energy physics, inspiring new constraints on beyond the Standard Model (BSM) physics, and new experimental methods. The CE$\nu$NS process has important implications for not only high-energy physics, but also astrophysics, nuclear physics, and beyond. This whitepaper discusses the scientific importance of CE$\nu$NS, highlighting how present experiments such as COHERENT are informing theory, and also how future experiments will provide a wealth of information across the aforementioned fields of physics., Comment: contribution to Snowmasss 2021. Contact authors: P. S. Barbeau, R. Strauss, L. E. Strigari

The EDELWEISS collaboration reports on the search for Dark Matter (DM) particle interactions via Migdal effect with masses between $32$ MeV$\cdot$c$^{-2}$ to $2$ GeV$\cdot$c$^{-2}$ using a $200$ g cryogenic Ge detector sensitive to simultaneously heat and ionization signals and operated underground at the Laboratoire Souterrain de Modane in France. The phonon signal was read out using a Transition Edge Sensor made of a NbSi thin film. The detector was biased at $66$ V in order to benefit from the Neganov-Trofimov-Luke amplification and resulting in a resolution on the energy of electron recoils of $4.46$ eV$_{ee}$ (RMS) and an analysis threshold of $30$ eV$_{ee}$. The sensitivity is limited by a dominant background not associated to charge creation in the detector. The search constrains a new region of parameter space for cross-sections down to $10^{-29}$ cm$^2$ and masses between $32$ and $100$ MeV$\cdot$c$^{-2}$. The achieved low threshold with the NbSi sensor shows the relevance of its use for athermal-phonon sensitive devices for low-mass DM searches., Comment: 10 pages, 9 figures

Density-based directed distances -- particularly known as divergences -- between probability distributions are widely used in statistics as well as in the adjacent research fields of information theory, artificial intelligence and machine learning. Prominent examples are the Kullback-Leibler information distance (relative entropy) which e.g. is closely connected to the omnipresent maximum likelihood estimation method, and Pearson's chisquare-distance which e.g. is used for the celebrated chisquare goodness-of-fit test. Another line of statistical inference is built upon distribution-function-based divergences such as e.g. the prominent (weighted versions of) Cramer-von Mises test statistics respectively Anderson-Darling test statistics which are frequently applied for goodness-of-fit investigations; some more recent methods deal with (other kinds of) cumulative paired divergences and closely related concepts. In this paper, we provide a general framework which covers in particular both the above-mentioned density-based and distribution-function-based divergence approaches; the dissimilarity of quantiles respectively of other statistical functionals will be included as well. From this framework, we structurally extract numerous classical and also state-of-the-art (including new) procedures. Furthermore, we deduce new concepts of dependence between random variables, as alternatives to the celebrated mutual information. Some variational representations are discussed, too.

Many low-threshold experiments observe sharply rising event rates of yet unknown origins below a few hundred eV, and larger than expected from known backgrounds. Due to the significant impact of this excess on the dark matter or neutrino sensitivity of these experiments, a collective effort has been started to share the knowledge about the individual observations. For this, the EXCESS Workshop was initiated. In its first iteration in June 2021, ten rare event search collaborations contributed to this initiative via talks and discussions. The contributing collaborations were CONNIE, CRESST, DAMIC, EDELWEISS, MINER, NEWS-G, NUCLEUS, RICOCHET, SENSEI and SuperCDMS. They presented data about their observed energy spectra and known backgrounds together with details about the respective measurements. In this paper, we summarize the presented information and give a comprehensive overview of the similarities and differences between the distinct measurements. The provided data is furthermore publicly available on the workshop's data repository together with a plotting tool for visualization., Comment: 44 pages, 20 figures; Editors: A. Fuss, M. Kaznacheeva, F. Reindl, F. Wagner; updated copyright statements and funding information

Misinformation promotes distrust in science, undermines public health, and may drive civil unrest. Vaccine misinformation, in particular, has stalled efforts to overcome the COVID-19 pandemic, prompting social media platforms' attempts to reduce it. Some have questioned whether "soft" content moderation remedies -- e.g., flagging and downranking misinformation -- were successful, suggesting that the addition of "hard" content remedies -- e.g., deplatforming and content bans -- is necessary. We therefore examined whether Facebook's vaccine misinformation content removal policies were effective. Here, we show that Facebook's policies reduced the number of anti-vaccine posts but also caused several perverse effects: pro-vaccine content was also removed, engagement with remaining anti-vaccine content repeatedly recovered to pre-policy levels, and this content became more misinformative, more politically polarised, and more likely to be seen in users' newsfeeds. We explain these results as an unintended consequence of Facebook's design goal: promoting community formation. Members of communities dedicated to vaccine refusal appear to seek out misinformation from multiple sources. Community administrators make use of several channels afforded by the Facebook platform to disseminate misinformation. Our findings suggest the need to address how social media platform architecture enables community formation and mobilisation around misinformative topics when managing the spread of online content.

Among the goals of information operations are to change the overall information environment vis-\'a-vis specific actors. For example, "trolling campaigns" seek to undermine the credibility of specific public figures, leading others to distrust them and intimidating these figures into silence. To accomplish these aims, information operations frequently make use of "trolls" -- malicious online actors who target verbal abuse at these figures. In Brazil, in particular, allies of Brazil's current president have been accused of operating a "hate cabinet" -- a trolling operation that targets journalists who have alleged corruption by this politician and other members of his regime. Leading approaches to detecting harmful speech, such as Google's Perspective API, seek to identify specific messages with harmful content. While this approach is helpful in identifying content to downrank, flag, or remove, it is known to be brittle, and may miss attempts to introduce more subtle biases into the discourse. Here, we aim to develop a measure that might be used to assess how targeted information operations seek to change the overall valence, or appraisal, of specific actors. Preliminary results suggest known campaigns target female journalists more so than male journalists, and that these campaigns may leave detectable traces in overall Twitter discourse.

The EDELWEISS collaboration aims for direct detection of light dark matter using germanium cryogenic detectors with low threshold phonon sensor technologies and efficient charge readout designs. We describe here the development of Ge bolometers equipped with high impedance thermistors based on a NbxSi1-x TES alloy. High aspect ratio spiral designs allow the TES impedance to match with JFET or HEMT front-end amplifiers. We detail the behavior of the superconducting transition properties of these sensors and the detector optimization in terms of sensitivity to out-of-equilibrium phonons. We report preliminary results of a 200 g Ge detector that was calibrated using 71Ge activation by neutrons at the LSM underground laboratory., Comment: Submitted to Journal of Low Temperature Physics, Special Issue for the 19th International Workshop on Low Temperature Detectors 19-29 July 2021 (Virtual event hold by NIST)

Previous studies have shown the efficacy of knowledge augmentation methods in pretrained language models. However, these methods behave differently across domains and downstream tasks. In this work, we investigate the augmentation of pretrained language models with commonsense knowledge in the cause-effect relation classification and commonsense causal reasoning tasks. After automatically verbalizing ATOMIC2020, a wide coverage commonsense reasoning knowledge graph, and GLUCOSE, a dataset of implicit commonsense causal knowledge, we continually pretrain BERT and RoBERTa with the verbalized data. Then we evaluate the resulting models on cause-effect pair classification and answering commonsense causal reasoning questions. Our results show that continually pretrained language models augmented with commonsense knowledge outperform our baselines on two commonsense causal reasoning benchmarks, COPA and BCOPA-CE, and the Temporal and Causal Reasoning (TCR) dataset, without additional improvement in model architecture or using quality-enhanced data for fine-tuning., Comment: Accepted to Commonsense Representation and Reasoning (CSRR) @ ACL 2022

The EDELWEISS collaboration searches for light Dark Matter (DM) particles using germanium detectors equipped with a charge and phonon signal readout. Using the Neganov-Trofimov-Luke effect, an rms resolution of 0.53 electron-hole pair was obtained on a massive (33.4 g) Ge detector operated underground at the Laboratoire Souterrain de Modane. This record sensitivity made possible a search for Dark Photon DM down to 1 eV/c2 and to DM-electron interactions below 1 MeV/c2. This demonstrates for the first time the high relevance of cryogenic Ge detectors in searches at low thresholds and is an important step of the development of Ge detectors with improved performance in the context of the EDELWEISS-SubGeV program., Comment: Submitted to Journal of Low Temperature Physics, Special Issue for the 19th International Workshop on Low Temperature Detectors 19-29 July 2021 (Virtual event hold by NIST)

We present an overview of recent progress towards the Ricochet coherent elastic neutrino nucleus scattering CE$\nu$NS experiment. The ILL research reactor in Grenoble, France has been selected as the experiment site, after in situ studies of vibration and particle backgrounds. We present background rate estimates specific to that site, along with descriptions of the planned CryoCube and Q-Array detector payloads., Comment: Proceedings for the 19th International Workshop on Low Temperature Detectors (LTD19)

In information theory -- as well as in the adjacent fields of statistics, machine learning, artificial intelligence, signal processing and pattern recognition -- many flexibilizations of the omnipresent Kullback-Leibler information distance (relative entropy) and of the closely related Shannon entropy have become frequently used tools. To tackle corresponding constrained minimization (respectively maximization) problems by a newly developed dimension-free bare (pure) simulation method, is the main goal of this paper. Almost no assumptions (like convexity) on the set of constraints are needed, within our discrete setup of arbitrary dimension, and our method is precise (i.e., converges in the limit). As a side effect, we also derive an innovative way of constructing new useful distances/divergences. To illustrate the core of our approach, we present numerous solved cases. The potential for widespread applicability is indicated, too; in particular, we deliver many recent references for uses of the involved distances/divergences and entropies in various different research fields (which may also serve as an interdisciplinary interface)., Comment: v3: considerably shortened and restructured version of v1/v2; 64 pages + 7 pages supplement. This work is accepted by the journal "IEEE Transactions on Information Theory", and is available in early-access form at https://ieeexplore.ieee.org/document/9925151

The authors transpose a discrete notion of indetermination coupling in the case of continuous probabilities. They show that this coupling, expressed on densities, cannot be captured by a specific copula which acts on cumulative distribution functions without a high dependence on the margins. Furthermore, they define a notion of average likelihood which extends the discrete notion of couple matchings and demonstrate it is minimal under indetermination. Eventually, they leverage this property to build up a statistical test to distinguish indetermination and estimate its efficiency using the Bahadur's slope.

In this work, we test the performance of two bidirectional transformer-based language models, BERT and SpanBERT, on predicting directionality in causal pairs in the textual content. Our preliminary results show that predicting direction for inter-sentence and implicit causal relations is more challenging. And, SpanBERT performs better than BERT on causal samples with longer span length. We also introduce CREST which is a framework for unifying a collection of scattered datasets of causal relations.

This paper provides constructive procedures for the indeterminacy coupling between two marginal distributions, an alternative to independence coupling. It also introduces a drawing under indeterminacy into a mixture of three independent couplings. Leveraging on this decomposition it states that indeterminacy optimally reduces couple matchings, minimizing the expected number of equal couples drawn in a row. Besides it is seen that the Janson Vegelius coefficient is nothing but a deviation to indeterminacy and it is shown that it tends to 0 when the number of modalities increases.

This paper is an attempt to set a justification for making use of some dicrepancy indexes, starting from the classical Maximum Likelihood definition, and adapting the corresponding basic principle of inference to situations where minimization of those indexes between a model and some extension of the empirical measure of the data appears as its natural extension. This leads to the so called generalized bootstrap setting for which minimum divergence inference seems to replace Maximum Likelihood one. 1 Motivation and context Divergences between probability measures are widely used in Statistics and Data Science in order to perform inference under models of various kinds, paramet-ric or semi parametric, or even in non parametric settings. The corresponding methods extend the likelihood paradigm and insert inference in some minimum "distance" framing, which provides a convenient description for the properties of the resulting estimators and tests, under the model or under misspecifica-tion. Furthermore they pave the way to a large number of competitive methods , which allows for trade-off between efficiency and robustness, among others. Many families of such divergences have been proposed, some of them stemming from classical statistics (such as the Chi-square), while others have their origin in other fields such as Information theory. Some measures of discrepancy involve regularity of the corresponding probability measures while others seem to be restricted to measures on finite or countable spaces, at least when using them as inferential tools, henceforth in situations when the elements of a model have to be confronted with a dataset. The choice of a specific discrepancy measure in specific context is somehow arbitrary in many cases, although the resulting conclusion of the inference might differ accordingly, above all under misspecification; however the need for such approaches is clear when aiming at robustness.

Satire is a form of humorous critique, but it is sometimes misinterpreted by readers as legitimate news, which can lead to harmful consequences. We observe that the images used in satirical news articles often contain absurd or ridiculous content and that image manipulation is used to create fictional scenarios. While previous work have studied text-based methods, in this work we propose a multi-modal approach based on state-of-the-art visiolinguistic model ViLBERT. To this end, we create a new dataset consisting of images and headlines of regular and satirical news for the task of satire detection. We fine-tune ViLBERT on the dataset and train a convolutional neural network that uses an image forensics technique. Evaluation on the dataset shows that our proposed multi-modal approach outperforms image-only, text-only, and simple fusion baselines., Comment: Accepted to the Third Workshop on NLP for Internet Freedom (NLP4IF): Censorship, Disinformation, and Propaganda. Co-located with COLING 2020

There is increased interest in assisting non-expert audiences to effectively interact with machine learning (ML) tools and understand the complex output such systems produce. Here, we describe user experiments designed to study how individual skills and personality traits predict interpretability, explainability, and knowledge discovery from ML generated model output. Our work relies on Fuzzy Trace Theory, a leading theory of how humans process numerical stimuli, to examine how different end users will interpret the output they receive while interacting with the ML system. While our sample was small, we found that interpretability -- being able to make sense of system output -- and explainability -- understanding how that output was generated -- were distinct aspects of user experience. Additionally, subjects were more able to interpret model output if they possessed individual traits that promote metacognitive monitoring and editing, associated with more detailed, verbatim, processing of ML output. Finally, subjects who are more familiar with ML systems felt better supported by them and more able to discover new patterns in data; however, this did not necessarily translate to meaningful insights. Our work motivates the design of systems that explicitly take users' mental representations into account during the design process to more effectively support end user requirements.

On February 2, 2020, the World Health Organization declared a COVID-19 social media "infodemic", with special attention to misinformation -- frequently understood as false claims. To understand the infodemic's scope and scale, we analyzed over 500 million posts from Twitter and Facebook about COVID-19 and other health topics, between March 8 and May 1, 2020. Following prior work, we assumed URL source credibility is a proxy for false content, but we also tested this assumption. Contrary to expectations, we found that messages about COVID-19 were more likely to contain links to more credible sources. Additionally, messages linking to government sources, and to news with intermediate credibility, were shared more often, on average, than links to non-credible sources. These results suggest that more ambiguous forms of misinformation about COVID-19 may be more likely to be disseminated through credible sources when compared to other health topics. Furthermore, the assumption that credibility is an adequate proxy for false content may overestimate the prevalence of false content online: less than 25% of posts linking to the least credible sources contained false content. Our results emphasize the importance of distinguishing between explicit falsehoods and more ambiguous forms of misinformation due to the search for meaning in an environment of scientific uncertainty.

This paper aims at comparing two coupling approaches as basic layers for building clustering criteria, suited for modularizing and clustering very large networks. We briefly use "optimal transport theory" as a starting point, and a way as well, to derive two canonical couplings: "statistical independence" and "logical indetermination". A symmetric list of properties is provided and notably the so called "Monge's properties", applied to contingency matrices, and justifying the $\otimes$ versus $\oplus$ notation. A study is proposed, highlighting "logical indetermination", because it is, by far, lesser known. Eventually we estimate the average difference between both couplings as the key explanation of their usually close results in network clustering., Comment: arXiv admin note: text overlap with arXiv:2012.14674

Iran, along with China, South Korea, and Italy was among the countries that were hit hard in the first wave of the COVID-19 spread. Twitter is one of the widely-used online platforms by Iranians inside and abroad for sharing their opinion, thoughts, and feelings about a wide range of issues. In this study, using more than 530,000 original tweets in Persian/Farsi on COVID-19, we analyzed the topics discussed among users, who are mainly Iranians, to gauge and track the response to the pandemic and how it evolved over time. We applied a combination of manual annotation of a random sample of tweets and topic modeling tools to classify the contents and frequency of each category of topics. We identified the top 25 topics among which living experience under home quarantine emerged as a major talking point. We additionally categorized broader content of tweets that shows satire, followed by news, is the dominant tweet type among the Iranian users. While this framework and methodology can be used to track public response to ongoing developments related to COVID-19, a generalization of this framework can become a useful framework to gauge Iranian public reaction to ongoing policy measures or events locally and internationally.

The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic. It has also raised concerns about the spread of hateful language and prejudice online, especially hostility directed against East Asia. In this paper we report on the creation of a classifier that detects and categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice and a neutral class. The classifier achieves an F1 score of 0.83 across all four classes. We provide our final model (coded in Python), as well as a new 20,000 tweet training dataset used to make the classifier, two analyses of hashtags associated with East Asian prejudice and the annotation codebook. The classifier can be implemented by other researchers, assisting with both online content moderation processes and further research into the dynamics, prevalence and impact of East Asian prejudice online during this global pandemic., Comment: 12 pages

This paper considers the problem of automatically characterizing overall attitudes and biases that may be associated with emerging information operations via artificial intelligence. Accurate analysis of these emerging topics usually requires laborious, manual analysis by experts to annotate millions of tweets to identify biases in new topics. We introduce extensions of the Word Embedding Association Test from Caliskan et al. to a new domain (Caliskan, 2017). Our practical and unsupervised method is used to quantify biases promoted in information operations. We validate our method using known information operation-related tweets from Twitter's Transparency Report. We perform a case study on the COVID-19 pandemic to evaluate our method's performance on non-labeled Twitter data, demonstrating its usability in emerging domains., Comment: 5 pages, 4 tables, 1 figure

Social distancing is an important component of the response to the novel Coronavirus (COVID-19) pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads, and "flattens the curve" such that the medical system can better treat infected individuals. However, it remains unclear how the public will respond to these policies. This paper presents the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We use public geolocated Twitter data to measure how much a user travels in a given week. We find a large reduction in travel in the United States after the implementation of social distancing policies, with larger reductions in states that were early adopters and smaller changes in states without policies. Our findings are presented on http://socialmobility.covid19dataresources.org and we will continue to update our analysis during the pandemic.

We propose a sequential design method aiming at the estimation of an extreme quantile based on a sample of dichotomic data corresponding to peaks over a given threshold. This study is motivated by an industrial challenge in material reliability and consists in estimating a failure quantile from trials whose outcomes are reduced to indicators of whether the specimen have failed at the tested stress levels. The solution proposed is a sequential design making use of a splitting approach, decomposing the target probability level into a product of probabilities of conditional events of higher order. The method consists in gradually targeting the tail of the distribution and sampling under truncated distributions. The model is GEV or Weibull, and sequential estimation of its parameters involves an improved maximum likelihood procedure for binary data, due to the large uncertainty associated with such a restricted information.

The EDELWEISS collaboration has performed a search for Dark Matter (DM) particles interacting with electrons using a 33.4 g Ge cryogenic detector operated underground at the LSM. A charge resolution of 0.53 electron-hole pairs (RMS) has been achieved using the Neganov-Trofimov-Luke amplification with a bias of 78 V. We set the first Ge-based constraints on sub-MeV/c$^{2}$ DM particles interacting with electrons, as well as on dark photons down to 1 eV/c$^2$. These are competitive with other searches. In particular, new limits are set on the kinetic mixing of dark photon DM in a so far unconstrained parameter space region in the 6 to 9 eV/c$^2$ mass range. These results demonstrate the high relevance of cryogenic Ge detectors for the search of DM interactions producing eV-scale electron signals., Comment: 6 pages, 4 figures, corrected typos