Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying the structure of controversial discussions with unsupervised methods: a look into the Twitter climate change conversation

Social media provides an essential platform for shaping and sharing opinions and consuming information in a decentralized way. However, users often interact with and are exposed to information mostly aligned with their beliefs, creating a positive feedback mechanism that reinforces those beliefs and excludes contrasting ones. In this paper, we study such mechanisms by analyzing the social network dynamics of controversial Twitter discussions using unsupervised methods that de-mand little computational power. Speciﬁcally, we focus on the retweet networks of the climate change conversation during 2019, when important climate social movements ﬂourished. We ﬁnd echo chambers of climate believers and climate skeptics that we identify based solely on the retweeting patterns of Twitter users. In particular, we study the information sources, or chambers , consumed by the audience of the leading users of the conversation. Users with similar (contrasting) ideological positions show signiﬁcantly high (low)-overlapping chambers, resulting in a bimodal overlap distribution. Further, we uncover the ideological position of previously unobserved high-impact users based on how many audience members fall into either echo chamber. We uncover the ideology of more than half of the retweeting population as either climate believers or skeptics and ﬁnd that the cross-group communication is small. Moreover, we ﬁnd that, while the echo chamber structures are consistent throughout the year, most users inside the echo chambers change from one week to the next, suggesting that they are a stable emergent property of the Twittersphere. Interestingly, we detect a signiﬁcant de-crease in polarization and a signiﬁcant increase on the number of climate skeptics that coincides with the main #FridaysForFuture strikes during September, 2019.
LOFAR discovery of rare large FR I jets in the low-luminosity radio galaxy NGC 5322

The discovery of faint Fanaroff–Riley type I (FR I) radio jets in the elliptical galaxy NGC 5322 is reported here using the 144-MHz image from Data Release 2 of the Low-Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS). The jets have an angular extent of ∼40 arcmin or a projected physical extent of ∼360 kpc. The faint jets remain well collimated and disappear in the intergalactic medium, without any detected hotspots or radio lobes. The relatively brighter jets, previously detected only up to the ∼21 kpc extent at higher frequencies, are within the optical extent of the galaxy. The jets become faint abruptly outside, where detection is made only in the LOFAR image. The total radio luminosity of the galaxy at 144 MHz is estimated to be (3.7 ± 0.4) × 1022 W Hz−1. The 144-MHz radio luminosity of the faint jets outside the optical extent is estimated to be (7.1 ± 2.0) × 1021 W Hz−1. The size of the jets is exceptionally large for the galaxy’s low radio luminosity, when compared with other radio galaxies. It makes NGC 5322 a rare radio galaxy, previously not detected in other radio surveys. A combined effect of stellar core depletion and low-density environment around the jets, which results in a weak entrainment of the material surrounding the jets, could be responsible for its large size despite a low radio luminosity.
Content-adaptive generation and parallel compositing of volumetric depth images for responsive visualization of large volume data

We present a content-adaptive generation and parallel compositing algorithm for view-dependent explorable representations of large three-dimensional volume data. Large distributed volume data are routinely produced in both numerical simulations and experiments, yet it remains challenging to visualize them at smooth, interactive frame rates. Volumetric Depth Images (VDIs), view-dependent piece wise-constant representations of volume data, offer a potential solution: they are more compact and less expensive to render than the original data. So far, however, there is no method to generate such representations on distributed data and to automatically adapt the representation to the contents of the data. We propose an approach that addresses both issues by enabling sort-last parallel generation of VDIs with content-adaptive parameters. The resulting VDIs can be streamed for display, providing responsive visualization of large, potentially distributed, volume data.
Matryoshka: Stealing Functionality of Private ML Data by Hiding Models in Model

In this paper, we present a novel insider attack called Matryoshka, which employs an irrelevant scheduled-to-publish DNN model as a carrier model for covert transmission of multiple secret models which memorize the functionality of private ML data stored in local data centers. Instead of treating the parameters of the carrier model as bit strings and applying conventional steganography, we devise a novel parameter sharing approach which exploits the learning capacity of the carrier model for information hiding. Matryoshka simultaneously achieves: (i) High Capacity -- With almost no utility loss of the carrier model, Matryoshka can hide a 26x larger secret model or 8 secret models of diverse architectures spanning different application domains in the carrier model, neither of which can be done with existing steganography techniques; (ii) Decoding Efficiency -- once downloading the published carrier model, an outside colluder can exclusively decode the hidden models from the carrier model with only several integer secrets and the knowledge of the hidden model architecture; (iii) Effectiveness -- Moreover, almost all the recovered models have similar performance as if it were trained independently on the private data; (iv) Robustness -- Information redundancy is naturally implemented to achieve resilience against common post-processing techniques on the carrier before its publishing; (v) Covertness -- A model inspector with different levels of prior knowledge could hardly differentiate a carrier model from a normal model.
Graphene nanocones and Pascal matrices

I conjecture three identities for the determinant of adjacency matrices of graphene triangles and trapezia with Bloch (and more general) boundary conditions. For triangles, the parametric determinant is equal to the characteristic polynomial of the symmetric Pascal matrix. For trapezia it is equal to the determinant of a sub-matrix. Finally, the determinant of the tight binding matrix equals its permanent. The conjectures are supported by analytic evaluations and Mathematica, for moderate sizes. They establish connections with counting problems of partitions, lozenge tilings of hexagons, dense loops on a cylinder.
How John Wheeler lost his faith in the law

In 1972, at a symposium celebrating the 70th birthday of Paul Dirac, John Wheeler proclaimed that „the framework falls down for everything that one has ever called a law of physics“. Responsible for this „breakage [. ] among the laws of physics“ was the general theory of relativity, more speciﬁcally its prediction of massive stars gravitationally collapsing to „black holes“, a term Wheeler himself had made popular some years earlier. In our paper, we investigate how Wheeler reached the conclusion that gravitational collapse calls into question the lawfulness of physics and how, subsequently, he tried to develop a new worldview, rethinking in his own way the lessons of quantum mechanics as well as drawing inspiration from other disciplines, not least biology.
Sgr A* Spin and Mass Estimates through the Detection of an Extremely Large Mass-ratio Inspiral

Estimating the spin of Sagittarius A* (Sgr A*) is one of the current challenges we face in understanding the center of our Galaxy. In the present work, we show that detecting the gravitational waves (GWs) emitted by a brown dwarf inspiraling around Sgr A* will allow us to measure the mass and the spin of Sgr A* with unprecedented accuracy. Such systems are known as extremely large mass-ratio inspirals (XMRIs) and are expected to be abundant and loud sources in our Galactic center. We consider XMRIs with a fixed orbital inclination and different spins of Sgr A* (s) between 0.1 and 0.9. For both cases, we obtain the number of circular and eccentric XMRIs expected to be detected by space-borne GW detectors like LISA and TianQin. We find that if the orbit is eccentric, then we expect to always have several XMRIs in band while for almost circular XMRIs, we only expect to have one source in band if Sgr A* is highly spinning. We later perform a Fisher matrix analysis to show that by detecting a single XMRI the mass of Sgr A* can be determined with an accuracy of the order 10−2 M ⊙, while the spin can be measured with an accuracy between 10−7 and 10−4 depending on the orbital parameters of the XMRI.
Local photons

The classical free-space solutions of Maxwell’s equations for light propagation in one dimension include wave packets of any shape that travel at the speed of light. This includes highly-localised wave packets that remain localised at all times. Motivated by this observation, this paper builds on recent work by Southall et al. [J. Mod. Opt. 68, 647 (2021)] and shows that a local description of the quantised electromagnetic field, which supports such solutions and which must overcome several no-go theorems, is indeed possible. Starting from the assumption that the basic building blocks of photonic wave packets are so-called bosons localised in position (blips), we identify the relevant Schrödinger equation and construct Lorentz-covariant electric and magnetic field observables. In addition we show that our approach simplifies to the standard description of quantum electrodynamics when restricted to a subspace of states.
Joint Spatial Division and Multiplexing for FDD in Intelligent Reflecting Surface-Assisted Massive MIMO Systems

Channel reciprocity, assumed in fifth-generation (5G) systems such as massive multiple-input-multiple-output (mMIMO), appears to be questioned in practice by recent studies on intelligent reflecting surface (IRS). Hence, contrary to previous works, we consider frequency division duplexing (FDD) to study the performance of an IRS-assisted mMIMO system. However, FDD is not suitable for large number of antennas architectures. For this reason we employ the joint spatial division and multiplexing (JSDM) approach exploiting the structure of the correlation of the channel vectors to reduce the channel state information (CSI) uplink feedback, and thus, allowing the use even of a large number of antennas at the base station. Specifically, we derive the sum spectral efficiency (SE) based on statistical CSI in terms of large-scale statistics by using the deterministic equivalent (DE) analysis while accounting for correlated Rayleigh fading. Subsequently, we formulate the optimization problem concerning the sum SE with respect to the reflecting beamforming matrix (RBM)and the total transmit power, which can be performed at every several coherence intervals by taking advantage of the slow time variation of the large-scale statistics. This notable property contributes further to the decrease of the feedback overhead. Numerical results, verified by Monte-Carlo (MC) simulations, enable interesting observations by elucidating how fundamental system parameters such as the rank of the covariance matrix and the number of groups of UEs affect the performance. For example, the selection of a high rank improves the channel conditioning but increases the feedback overhead.
Signature Methods in Machine Learning

Signature-based techniques give mathematical insight into the interactions between complex streams of evolving data. These insights can be quite naturally translated into numerical approaches to understanding streamed data, and perhaps because of their mathematical precision, have proved useful in analysing streamed data in situations where the data is irregular, and not stationary, and the dimension of the data and the sample sizes are both moderate. Understanding streamed multi-modal data is exponential: a word in $n$ letters from an alphabet of size $d$ can be any one of $d^n$ messages. Signatures remove the exponential amount of noise that arises from sampling irregularity, but an exponential amount of information still remain. This survey aims to stay in the domain where that exponential scaling can be managed directly. Scalability issues are an important challenge in many problems but would require another survey article and further ideas. This survey describes a range of contexts where the data sets are small enough to remove the possibility of massive machine learning, and the existence of small sets of context free and principled features can be used effectively. The mathematical nature of the tools can make their use intimidating to non-mathematicians. The examples presented in this article are intended to bridge this communication gap and provide tractable working examples drawn from the machine learning context. Notebooks are available online for several of these examples. This survey builds on the earlier paper of Ilya Chevryev and Andrey Kormilitzin which had broadly similar aims at an earlier point in the development of this machinery. This article illustrates how the theoretical insights offered by signatures are simply realised in the analysis of application data in a way that is largely agnostic to the data type.
Drone-Delivery Network for Opioid Overdose - Nonlinear Integer Queueing-Optimization Models and Methods

We propose a new stochastic emergency network design model that uses a fleet of drones to quickly deliver naxolone in response to opioid overdoses. The network is represented as a collection of M/G/K queuing systems in which the capacity K of each system is a decision variable and the service time is modelled as a decision-dependent random variable. The model is an optimization-based queuing problem which locates fixed (drone bases) and mobile (drones) servers and determines the drone dispatching decisions, and takes the form of a nonlinear integer problem, which is intractable in its original form. We develop an efficient reformulation and algorithmic framework. Our approach reformulates the multiple nonlinearities (fractional, polynomial, exponential, factorial terms) to give a mixed-integer linear programming (MILP) formulation. We demonstrate its generalizablity and show that the problem of minimizing the average response time of a network of M/G/K queuing systems with unknown capacity K is always MILP-representable. We design two algorithms and demonstrate that the outer approximation branch-and-cut method is the most efficient and scales well. The analysis based on real-life overdose data reveals that drones can in Virginia Beach: 1) decrease the response time by 78%, 2) increase the survival chance by 432%, 3) save up to 34 additional lives per year, and 4) provide annually up to 287 additional quality-adjusted life years.
Four Eccentric Mergers Increase the Evidence that LIGO–Virgo–KAGRA’s Binary Black Holes Form Dynamically

The growing population of compact binary mergers detected with gravitational waves contains multiple events that are challenging to explain through isolated binary evolution. Such events have higher masses than are expected in isolated binaries, component spin tilt angles that are misaligned, and/or nonnegligible orbital eccentricities. We investigate the orbital eccentricities of 62 binary black hole candidates from the third gravitational-wave transient catalog of the LIGO–Virgo–KAGRA Collaboration with an aligned-spin, moderate-eccentricity waveform model. Within this framework, we find that at least four of these events show significant support for eccentricity e 10 ≥ 0.1 at a gravitational-wave frequency of 10 Hz (>60% credibility, under a log-uniform eccentricity prior that spans the range 10−4 < e 10 < 0.2). Two of these events are new additions to the population: GW191109 and GW200208_22. If the four eccentric candidates are truly eccentric, our results suggest that densely populated star clusters may produce 100% of the observed mergers. However, it remains likely that other formation environments with higher yields of eccentric mergers—for example, active galactic nuclei—also contribute. We estimate that we will be able to confidently distinguish which formation channel dominates the eccentric merger rate after ≳80 detections of events with e 10 ≥ 0.05 at LIGO–Virgo sensitivity, with only ∼5 detectably eccentric events required to distinguish formation channels with third-generation gravitational-wave detectors.
The Mycophenolate-based Immunosuppressive Regimen Is Associated With Increased Mortality in Kidney Transplant Patients With COVID-19

Background. The chronic use of immunosuppressive drugs is a key risk factor of death because of coronavirus disease 2019 (COVID-19) in kidney transplant recipients (KTRs), although no evident association between the class of immunosuppressive and outcomes has been observed. Thus, we aimed to compare COVID-19–associated outcomes among KTRs receiving 3 different immunosuppressive maintenance regimes. Methods. This study included data from 1833 KTRs with COVID-19 diagnosed between March 20 and April 21 extracted from the national registry before immunization. All patients were taking calcineurin inhibitor associated with mycophenolate acid (MPA, n = 1258), azathioprine (AZA, n = 389), or mammalian targets of rapamycin inhibitors (mTORi, n = 186). Outcomes within 30 and 90 d were assessed. Results. Compared with patients receiving MPA, the 30-d (79.9% versus 87.9% versus 89.2%; P < 0.0001) and 90-d (75% versus 83.5% versus 88.2%; P < 0.0001) unadjusted patient survivals were higher in those receiving AZA or mTORi, respectively. Using adjusted multivariable Cox regression, compared with patients receiving AZA, the use of MPA was associated with a higher risk of death within 30 d (adjusted hazard ratio [aHR], 1.70; 95% confidence interval [CI], 1.21-2.40; P = 0.003), which was not observed in patients using mTORi (aHR, 0.78; 95% CI, 0.45-1.35; P = 0.365). At 90 d, although higher risk of death was confirmed in patients receiving MPA (aHR, 1.46; 95% CI, 1.09-1.98; P = 0.013), a reduced risk was observed in patients receiving mTORi (aHR, 0.59; 95% CI, 0.35-0.97; P = 0.04) compared with AZA. Conclusions. This national cohort data suggest that, in KTRs receiving calcineurin inhibitor and diagnosed with COVID-19, the use of MPA was associated with higher risk of death, whereas mTORi use was associated with lower risk of death.
Retention in treatment and therapeutic adherence: How are these associated with therapeutic success? An analysis using real‐world data

Treatment retention and adherence are used as outcomes in numerous randomized clinical trials and observational studies conducted in the addiction field. Although usual criteria are 3/6 months of treatment retention or number of sessions attended, there is not a methodological support for conclusions using these criteria. This study analyzed the usefulness of retention and adherence to predict therapeutic success.
Intelligent sort‐timing prediction for image‐activated cell sorting

Intelligent image‐activated cell sorting (iIACS) has enabled high‐throughput image‐based sorting of single cells with artificial intelligence (AI) algorithms. This AI‐on‐a‐chip technology combines fluorescence microscopy, AI‐based image processing, sort‐timing prediction, and cell sorting. Sort‐timing prediction is particularly essential due to the latency on the order of milliseconds between image acquisition and sort actuation, during which image processing is performed. The long latency amplifies the effects of the fluctuations in the flow speed of cells, leading to fluctuation and uncertainty in the arrival time of cells at the sort point on the microfluidic chip. To compensate for this fluctuation, iIACS measures the flow speed of each cell upstream, predicts the arrival timing of the cell at the sort point, and activates the actuation of the cell sorter appropriately. Here, we propose and demonstrate a machine learning technique to increase the accuracy of the sort‐timing prediction that would allow for the improvement of sort event rate, yield, and purity. Specifically, we trained an algorithm to predict the sort timing for morphologically heterogeneous budding yeast cells. The algorithm we developed used cell morphology, position, and flow speed as inputs for prediction and achieved 41.5% lower prediction error compared to the previously employed method based solely on flow speed. As a result, our technique would allow for an increase in the sort event rate of iIACS by a factor of ~2.
Ciclosporin A Cationic Emulsion 0.1% for the Management of Dry Eye Disease: Facts That Matter for Eye-Care Providers

ABSTRACT Dry eye disease (DED) is a chronic inflammatory disease of the ocular surface requiring long-term therapy. Severe forms of DED generally do not respond to tear substitutes alone or combined, and often require treatment with topical anti-inflammatory agents to break the vicious circle of inflammation. This review summarises data from randomised controlled trials and real-world evidence on the efficacy and safety of ciclosporin A 0.1% cationic emulsion (Ikervis®) for the management of DED. Improvements in clinical signs and symptoms were reported from as early as 4 weeks after treatment initiation, although it can take a few months to reach the full benefits. Treatment periods of up to 12 months provide sustained benefit to patients. In the most responsive patients, treatment discontinuation is possible with no further substantial relapse over 12 months in over 65% of patients. Transient local ocular effects are the most commonly reported adverse events.
Sustainable Design of New Ionic Forms of Vitamin B3 and Their Utilization as Plant Protection Agents

This study demonstrates the utilization of naturally occurring nicotinamide (vitamin B3) in the sustainable synthesis of organic salts with application potential as environmentally friendly agrochemicals. The designed ionic pairs, obtained with high yields, consisted of N-alkylnicotinamide cation and commercially available herbicidal anions: 2,4-dichlorophenoxyacetate (2,4-D) and 4-chloro-2-methylphenoxyacetate (MCPA). The study confirmed the strong influence of the length of alkyl chain in products on the physicochemical properties as well as the development of cornflower and oil-seed rape. The majority of tested salts showed significantly better herbicidal activity (by approx. 30–50%) compared to the reference herbicide. Furthermore, N-hexadecylnicotinamide 4-chloro-2-methylphenoxyacetate was significantly more effective than the commercial formulation in the dose–response test. Their negligible vaporization, multiple times lower than that of commonly used dimethylammonium salts, eliminates one of the greatest threats of currently applied plant protection agents. Additionally, the risk of product migration or bioaccumulation in the environment was assessed as extremely low.