fast.ai is dedicated to making the power of deep learning accessible to all. Deep learning is dramatically improving medicine, education, agriculture, transport and many other fields, with the greatest potential impact in the developing world. For its full potential to be met, the technology needs to be much easier to use, more reliable, and more intuitive than it is today.
However, there is also great potential for harm. We are worried about unethical uses of data science, and about the ways that society’s racial and gender biases (summary here) are being encoded into our machine learning systems. We are concerned that an extremely homogeneous group is building technology that impacts everyone. People can’t address problems that they’re not aware of, and with more diverse practitioners, a wider variety of important societal problems will be tackled.
We want to get deep learning into the hands of as many people as possible, from as many diverse backgrounds as possible. People with different backgrounds have different problems they’re interested in solving. The traditional approach is to start with an AI expert and then give them a problem to work on; at fast.ai we want people who are knowledgeable and passionate about the problems they are working on, and we’ll teach them the deep learning they need.
While some people worry that it’s risky for more people to have access to AI; I believe the opposite. We’ve already seen the harm wreaked by elite and exclusive companies such as Facebook, Palantir, and YouTube/Google. Getting people from a wider range of backgrounds involved can help us address these problems.
The fast.ai approach
We began fast.ai with an experiment: to see if we could teach deep learning to coders, with no math pre-requisites beyond high school math, and get them to state-of-the-art results in just 7 weeks. This was very different from other deep learning materials, many of which assume a graduate level math background, focus on theory, only work on toy problems, and don’t even include the practical tips. We didn’t even know if what we were attempting was possible, but the fast.ai course has been a huge success!
fast.ai is not just an educational resource; we also do cutting-edge research and have achieved state-of-the-art results. Our wins (and here) in Stanford’s DAWNBench competition against much better funded teams from Google and Intel were covered in the MIT Tech Review and the Verge. Jeremy’s work with Sebastian Ruder achieving state-of-the art on 6 language classification datasets was accepted by ACL and is being built upon by OpenAI. All this research is incorporated into our course, teaching students state-of-the-art techniques.
Med borgerforslaget om, at Danmak officielt bør opdatere en lov fra 1893, er det måske på sin plads at spørge: Hvad er UTC? Det rette sted at gå til er U.S. Naval Observatory, 3450 Massachusetts Ave, NW, Washington, DC .
USNO strengthens national security and critical infrastructure by serving as DoD’s authoritative source for the positions and motion of celestial bodies, motions of the Earth, and precise time. USNO provides tailored products, performs relevant research, develops leading edge technologies and instrumentation, and operates state of the art systems in support of the U.S. Navy, DoD, Federal Agencies, international partners, and the general public.
USNO products support activities in the following areas:
USNO Master Clock, Network Time Protocol (NTP) servers, web-based time synchronization, GPS timing products and services, Two-Way Satellite Time Transfer, and Loran-C timing products.
Af særlig interesse er aktiviteterne i Earth Orientation:
The U.S. Naval Observatory is responsible for determining and predicting the time-varying alignment of the Earth’s terrestrial reference frame with respect to the celestial reference frame. USNO is the International Earth Rotation and Reference Systems Service (IERS) Rapid Service/Prediction Center (RS/PC) for Earth Orientation.
Information regarding commonly used variables (General Information), Information for GPS Users (GPS User Information), frequently asked questions about Earth Orientation, and format descriptions for data sets (Read Me files).
Publications providing background material (Explanatory Supplement), documentation of procedures and quality of results (Annual Reports), and technical details regarding the procedures (Scientific Publications).
Supporting software for searching through Earth orientation results and for calculating the rotation matrices between terrestrial and celestial reference frames. Recommended support and auxiliary software for use with Earth Orientation products as input.
Det er måske her på sin plads at indskyde en forklaring af de begreber, som bliver omtalt på USNO’s hjemmeside. UTC er en kombination af atomtid (TAI) og astronomisk tid (UT1):
Observations from a number of different techniques can be used to determine Earth Orientation Parameters such as the Earth’s polar motion and the length of day. One such technique is Very Long Baseline Interferometry (VLBI). The Earth Orientation Parameters (EOP) obtained through periodic VLBI observations also connect the Celestial Reference Frame (CRF) to the Terrestrial Reference Frame (TRF). VLBI-based EOP products are updated daily as new VLBI data become available and can be used individually or combined with EOP results from other techniques.
Formålet med VLBI målinger af kvasarer er at definere et inertialsystem som reference for Jordens rotation. NASA har her beskrevet metoden:
Over its 40-year history of development and operation, the space geodetic technique called very long baseline interferometry (VLBI) has provided an unprecedented record of the motions of the solid Earth. VLBI is unique in its ability to define an inertial reference frame and to measure the Earth’s orientation in this frame. Changes in the Earth’s orientation in inertial space have two causes: the gravitational forces of the Sun and Moon and the redistribution of total angular momentum among the solid Earth, ocean, and atmosphere. VLBI makes a direct measurement of the Earth’s orientation in space from which geoscientists then study such phenomena as atmospheric angular momentum, ocean tides and currents, and the elastic response of the solid Earth.
VLBI is a geometric technique; it measures the time difference between the arrival at two Earth-based antennas of a radio wavefront emitted by a distant quasar. Using large numbers of time difference measurements from many quasars observed with a global network of antennas, VLBI determines the inertial reference frame defined by the quasars and simultaneously, the precise positions of the antennas. Because the time difference measurements are precise to a few picoseconds, VLBI determines the relative positions of the antennas to a few millimeters and the quasar positions to fractions of a milliarcsecond. Since the antennas are fixed to the Earth, their locations track the instantaneous orientation of the Earth in the inertial reference frame. Relative changes in the antenna locations from a series of measurements indicate tectonic plate motion, regional deformation, and local uplift or subsidence.
The heritage of VLBI is 40 years of NASA-led technology development that included the highly successful Crustal Dynamics Project, during which the first contemporary measurements of tectonic plate motion were made. Today VLBI observations, analysis and development are coordinated by the International VLBI Service for Geodesy and Astrometry (IVS), comprising some 80 components (including 45 antennas) sponsored by 40 organizations located in 20 countries. The IVS Coordinating Center is located at Goddard Space Flight Center in Greenblet, MD. VLBI determines with unequaled accuracy the terrestrial reference frame (antenna locations on the Earth), the International Celestial Reference Frame (quasar positions on the sky), and Earth’s orientation in space. In the future, VLBI development will continue in measurement systems technology, research on the neutral atmosphere, and integration with other space geodetic techniques.
VLBI is a valuable asset in NASA’s mission of science-driven technology leadership. Earth science research requires VLBI’s Earth orientation data coupled to a stable, accurate terrestrial reference frame.
Bemærk: Antennernes placeringer er bestemt med en nøjagtighed af nogle få mm! Der er styr på Jordens varierende rotation.
Men hvordan måler man middelsolens gang over himlen? Det gør man ikke. Man er kun interesseret i Jordens vinkelhastighed i forhold til Solens vinkelhastighed. Det betyder intet, hvor den fiktive “middelsol” befinder sig. I gamle dage (før VLBI af kvasarer) talte man om et siderisk (i forhold til stjerner) år og et siderisk døgn. Jeg vil i stedet indføre et kvasardøgn Pk og et kvasarår Uk. Disse størrelser kan måles med meget stor nøjagtighed. Et kvasarår måles ikke direkte på himlen. Det måles ud fra den beregnede bane for Jorden. Vi interesserer os for Jordens vinkelhastighed i forhold til Solens vilkelhastighed. Jeg indfører derfor et middelsoldøgn Ps, som helst skal være tæt på 24 timer = 24*60*60 = 86400 s.
Jordens vinkelhastighed i forhold til middelsolen: ΩsJ = 2π/Ps.
Jordens vinkelhastighed i forhold til kvasarerne: ΩkJ = 2π/Pk.
Middelsolens vinkelhastighed i forhold til kvasarerne: ΩkS = 2π/Uk.
Der gælder, at ΩsJ = ΩkJ – ΩkS, hvorfor 1/Ps = 1/Pk – 1/Uk.
Døgnet baseret på middelsolen kan derfor let findes ud fra kvasardøgnet og kvasaråret i sekunder.
Far beneath the deeply frozen ice cap at Mars’s south pole lies a lake of liquid water—the first to be found on the Red Planet. Detected from orbit using ice-penetrating radar, the lake is probably frigid and full of salts—an unlikely habitat for life. But the discovery, reported online today in Science, is sure to intensify the hunt for other buried layers of water that might be more hospitable. “It’s a very exciting result: the first indication of a briny aquifer on Mars,” says geophysicist David Stillman of Southwest Research Institute in Boulder, Colorado, who was not a part of the study.
The lake resembles one of the interconnected pools that sit under several kilometers of ice in Greenland and Antarctica, says Martin Siegert, a geophysicist at Imperial College London, who heads a consortium trying to drill into Lake Ellsworth under West Antarctica. But the processes that gave rise to a deep lake on Mars are likely to be different. “It will open up a very interesting area of science on Mars,” he says.
Water is thought to have flowed across the surface of Mars billions of years ago, when its atmosphere was thicker and warmer, cutting gullies and channels that are still visible. But today, low atmospheric pressures mean that any surface water would boil away. Water survives frozen in polar ice caps and in subsurface ice deposits. Some deposits have been mapped by the Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS), an instrument on the European Space Agency’s Mars Express orbiter, which launched in 2003. MARSIS beams down pulses of radio waves and listens for reflections. Some of the waves bounce off the surface, but others penetrate up to 3 kilometers and can be reflected by sharp transitions in the buried layers, such as going from ice to rock.
Several years into the mission, MARSIS scientists began to see small, bright echoes under the south polar ice cap—so bright that the reflection could indicate not just rock underlying the ice, but liquid water. The researchers doubted the signal was real, however, because it appeared in some orbital passes but not others.
Later the team realized that the spacecraft’s computer was averaging across pixels to reduce the size of large data streams—and in the process, smoothing away the bright anomalies. “We were not seeing the thing that was right under our noses,” says Roberto Orosei, a principal investigator (PI) for MARSIS at the Italian National Institute for Astrophysics in Bologna.
To bypass this problem, the team commandeered a memory chip on Mars Express to store raw data during short passes over intriguing areas. Between 2012 and 2015, the spacecraft confirmed the existence of the bright reflections during 29 passes over the south polar region. The brightest patch, offset 9° from the pole, lies 1.5 kilometers under the ice and spans 20 kilometers, Orosei and his colleagues report.
The radar brightness alone isn’t enough to prove that liquid water is responsible. Another clue comes from the permittivity of the reflecting material: its ability to store energy in an electric field. Water has a higher permittivity than rock and ice. Calculating permittivity requires knowing the signal power reflected by the bright patch, something the researchers could only estimate. But they find the permittivity of the patch to be higher than anywhere else on Mars—and comparable to the subglacial lakes on Earth. Although the team cannot measure the thickness of the water layer, Orosei says it is much more than a thin film.
Not everyone on the MARSIS team is convinced. “I would say the interpretation is plausible, but it’s not quite a slam dunk yet,” says Jeffrey Plaut, the other MARSIS PI at NASA’s Jet Propulsion Laboratory in Pasadena, California, who is not an author on the study.
After all, it isn’t easy to explain the presence of water at Mars’s south pole. In Earth’s polar regions, the pressure of the overlying ice lowers its melting point, and geothermal heat warms it from below to create the subglacial lakes. But there’s little heat flowing from the geologically dead interior of Mars, and under the planet’s weak gravity, the weight of 1.5 kilometers of ice does not lower the melting point by much. Orosei suspects that salts, especially the perchlorates that have been found in the planet’s soils, could be lowering the ice’s melting point. “They are the prime suspects,” he says.
High levels of salt and temperatures dozens of degrees below zero do not bode well for any microbes trying to live there, Stillman says. “If martian life is like Earth life, this is too cold and too salty.” But he says researchers will want to look for other lakes under the ice and find out whether they are connected—and whether they point to an even deeper water table.
Lakes might even turn up at lower, warmer latitudes—a location more suitable for a martian microbe, says Valèrie Ciarletti of the University of Paris-Saclay, who is developing a radar instrument for Europe’s ExoMars rover, due to launch in 2020. “The big, big finding would be water at depth outside the polar cap.”
Integrated information theory provides a mathematical framework to fully characterize the cause-effect structure (CES) of a physical system. Here, we introduce PyPhi, a Python software package that implements this framework for causal analysis and unfolds the full cause-effect structure of discrete dynamical systems of binary elements. The software allows users to easily study these structures, serves as an up-to-date reference implementation of the formalisms of integrated information theory, and has been applied in research on complexity, emergence, and certain biological questions. We first provide an overview of the main algorithm and demonstrate PyPhi’s functionality in the course of analyzing an example system, and then describe details of the algorithm’s design and implementation.
PyPhi can be installed with Python’s package manager via the command ‘pip install pyphi’ on Linux and macOS systems equipped with Python 3.4 or higher. PyPhi is open-source and licensed under the GPLv3; the source code is hosted on GitHub at this https URL . Comprehensive and continually-updated documentation is available at this https URL . The pyphi-users mailing list can be joined at this https URL . A web-based graphical interface to the software is available at this http URL .
Den integrerede informationsteori (IIT) tilvejebringer en matematisk ramme til fuldstændigt at karakterisere et fysisk systems årsag/virkning struktur (CES). Forfatterne introducerer PyPhi , en Python software pakke, som implementerer denne årsagsanalyse og afslører den fuldstændige årsag/virkning-struktur (CES) for et diskret dynamisk system af binære elementer. Denne software tillader brugerne en let adgang til at studere disse strukturer, tjener som en up-to-date reference-implementering af IIT-formalismen, samt har været anvendt til forskning inden for kompleksitet, “emergence” og visse biologiske spørgsmål. Artiklen giver først en oversigt over hovedalgoritmen og demonstrerer funktionaliteten ved at analysere et eksempel på et system. Derefter beskrives detaljerne ved algoritmens design og implementering.
PyPhi kan installeres med Python’s package manager via kommandoen ‘pip install pyphi’ på Linux og macOS systemer med Python 3.4 eller højere. PyPhi is open-source and licensed under the GPLv3. Den teoretiske baggrund findes i denne artikel:
Det er på sin plads at begynde med dette programs begrænsninger:
PyPhi’s main limitation is that the execution time of the algorithm is exponential in the number of nodes. This is because the number of states, subsystems, mechanisms, purviews, and partitions that must be considered each grows exponentially with the size of the system.This limits the size of systems that can be practically analyzed to ∼10-12 nodes. For example, calculating the major complex of systems of three, five, and seven stochastic majority gates, connected in a circular chain of bidirectional edges, takes ∼1 s, ∼12 s, and ∼2.75 h, respectively.
Dette er meget langt fra det realistiske antal neuroner i hjernen. PyPhi er altså meget langt fra at kunne analysere realistiske systemer.
Man har foreslået, at den integrerede informationsteori (IIT) er en matematisk teori for bevidsthed (i hjernen). Den centrale hypotese er, at et fysisk system skal opfylde 5 betingelser for at kunne tjene som et fysisk substrat for en subjektiv oplevelse: (1) intrinsic existence (the system must be able to make a difference to itself); (2) composition (it must be composed of parts that have causal power within the whole); (3) information (its causal power must be specific); (4) integration (its causal power must not be reducible to that of its parts); and (5) exclusion (it must be maximally irreducible). Jeg har her anvendt de engelske betegnelser, da programmet selvfølgelig anvender engelske betegnelser.
Ud fra disse 5 postulater udvikles en matematisk formalisme til beskrivelsen af årsag/virkning-strukturen (CES) for et diskret dynamisk system. Hovedmålet for styrken af årsag/virkning er størrelsen integrated information (med betegnelsen Φ), som er et tal for, hvor irreducibel (til de enkelte deles CES) et systems årsag/virkning-struktur er. Φ tjener også som et generelt mål for kompleksitet (i hvilken grad er systemet både integreret og differentieret?).
The software has two primary functions: (1) to unfold the full CES of a discrete dynamical system of interacting elements and compute its Φ value, and (2) to compute the maximally-irreducibly cause-effect repertoire of a particular set of elements within the system. The first function is implemented by pyphy.compute.major_complex(), which returns a SystemIrreducibilityAnalysis object (Fig. 1A). The system’s CES is contained in the ‘ces’ attribute and its Φ value is contained in ‘phi’. Other attributes are detailed in the online documentation.
The CES is composed of Concept objects, which are the output of the second main function: Subsystem.concept() (Fig 1B). Each Concept is specified by a set of elements within the system (contained in its ‘mechanism’ attribute). A Concept containes a maximally-irreducibly cause and effect repertoire (’cause_repertoire’ and ‘effect_repertoire’), which are probability distributions that capture how the mechanism elements in their current state constrain the previous and next state of the system, respectively; a φ value (‘phi’), which measures the irreducibility of the repertoires; and several other attributes discussed below and detailed in the online documentation.
The starting point for the IIT analysis is a discrete dynamical system S composed of n interacting elements. Such a system can be represented by a directed graph of interconnected nodes, each equipped with a Markovian (Sergey Markov, 1878-1918) function that outputs the node’s state at the next timestep t+1 given the state of its parents at the previous timestep t (Fig. 2). At present, PyPhi can analyze both deterministic and stochastic systems consisting of elements with two states.
Such a discrete dynamical system is completely specified by its transition probability matrix (TPM), which contains the probabilities of all state transitions from t to t+1. It can be obtained from the graphical representation of the system by perturbing the system into each of its possible states and observing the following state at the next timestep (for stochastic systems, repeated trials of perturbation/observation will yield the probabilities of each state transition). In PyPhi, the TPM is the fundamental representation of the system.
Formally, if we let St be the random variable of the system state at t, then TPM specifies the conditional probability distribution over the next state St+1 given each current state st:
P(St+1|St=st), ∀ st ∈ ΩS,
where ΩS denotes the set of possible states. Furthermore, given a marginal distribution over the previous states of the system, the TPM fully specifies the joint distribution over state transistions.Here IIT imposes uniformity on the marginal distribution of the previous state because the aim of the analysis is to capture direct causal relationships across a single timestep without confounding factors, such as influences from system states before t-1. The marginal distribution thus corresponds to an interventional (causal), not observed, state distribution.
Moreover, IIT assumes that there is no instantaneous causation; that is, it is assumed that the elements of a dynamical system influence one another only from one timestep to the next. Therefore we require that the system satisfies the following Markov condition, called the conditional independence property: each element’s state at t+1 must be independent of the state of the others, given the state of the system at t,
For systems of binary elements, a TPM that satisfies Eq. (1) can be represented in state-by-node form (Fig. 2, right), since we need only store each element’s marginal distribution rather than the full joint distribution.
In PyPhi, the system under analysis is represented by a Network object. A Network is created by passing its TPM as the first argument: network = pyphy.Network(tpm) (see setup). Optionally, a connectivity matrix (CM) can also be provided via the cm keyword argument: network = pyphi.Network(tpm, cm=cm). Because the TPM completely specifies the system, providing a CM is not necessary; however, explicit connectivity information can be used to make computations more efficient, especially for sparse networks, because PyPhi can rule out certain causal influences a priory if there are missing connections.
Dette er blot tænkt som et eksempel på et simpelt netværk af deterministiske knuder bestående af OR, AND og XOR logiske gates.
Actual causation is concerned with the question “what caused what?”. Consider a transition between two subsequent observations within a system of elements. Even under perfect knowledge of the system, a straightforward answer to this question may not be available. Counterfactual accounts of actual causation based on graphical models, paired with system interventions, have demonstrated initial success in addressing specific problem cases. We present a formal account of actual causation, applicable to discrete dynamical systems of interacting elements, that considers all counterfactual states of a state transition from t-1 to t. Within such a transition, causal links are considered from two complementary points of view: we can ask if any occurrence at time t has an actual cause at t-1, but also if any occurrence at time t-1 has an actual effect at t. We address the problem of identifying such actual causes and actual effects in a principled manner by starting from a set of basic requirements for causation (existence, composition, information, integration, and exclusion). We present a formal framework to implement these requirements based on system manipulations and partitions. This framework is used to provide a complete causal account of the transition by identifying and quantifying the strength of all actual causes and effects linking two occurrences. Finally, we examine several exemplary cases and paradoxes of causation and show that they can be illuminated by the proposed framework for quantifying actual causation.
Faktiske årsagsforhold beskæftiger sig med spørgsmålet: Hvad var årsag til hvad?. Lad os betragte en overgang mellem to på hinanden følgende observationer inden for et system af elementer. Selv med en perfekt viden om systemet vil et enkelt svar på dette spørgsmål ikke findes. Kontrfaktuelle beskrivelser af faktiske årsagsforhold baseret på grafiske modeller, kombineret med indgriben, har haft en indledende succes med at håndtere specifikke tilfælde. (der hentydes til Pearls do-operator, do(X=x), som angiver, at visse variable X aktivt er blevet tildelt faktuelle tilstande x; beskrivelsen kaldes derfor kontrafaktuel). Forfatterne intruducerer en formel beskrivelse af faktiske årsagsforhold, som kan anvendes på diskrete dynamiske systemer bestående af vekselvirkende elementer, og som tager hensyn til alle kontrafaktuelle tilstande for en tilstandsovergang fra tiden t-1 til tiden t. Kausale kæder inden for en sådan overgang betragtes ud fra to komplementære synspunkter: vi kan spørge, om enhver hændelse til tiden t har en faktisk årsag til tiden t-1, men også om enhver hændelse til tiden t-1 har en faktisk virkning til tiden t. Forfatterne tager fat på problemet med at identificere sådanne faktiske årsager og faktiske virkninger ved at starte med nogle grundlæggende postulater for årsagsforhold (eksistens, komposition, information, integration og indskrænkning). Forfatterne præsenterer en formel ramme for implementering af disse krav gennem håndtering og opdeling af systemet. Denne ramme anvendes til at opnå en fuldstændig kausal beskrivelse af overgangen ved at identificere og kvantificere styrken af alle faktiske årsager og virkninger, som kæder to hændelser sammen. Forfatterne undersøger til slut eksempler og paradokser for af vise, at man kan kaste lys over disse problemer med den foreslåede kvantificering af faktiske årsagsforhold.
Artiklen præsenterer en formel fremstilling af faktiske årsagsforhold, som inddrager alle kontrafaktuelle tilstande, som tillader os at udtrykke en årsagsanalyse ved sandsynligheder og informationsudtryk. Målet er at give en årsagsfremstilling af “Hvad var årsag til hvad?” ud fra en overgang Xt-1=xt-1 ≺ Yt=yt (“xt-1 går forud for yt“) mellem to på hinanden følgende observationer inden for et diskret dynamisk system S med Xt-1,Yt ⊆ S.
I den statistiske litteratur angiver et fedt stort bogstav, X, en stokastisk variabel, hvis mulige faktiske observationsværdier angives ved et fedt lille bogstav, x, hvorimod en faktisk såvel som en kontrafaktisk tilstand angives ved et normalt lille bogstav, x. Forskellen mellem de to typer tilstande er, at en faktisk tilstand kan observeres, hvorimod en kontrafaktisk tilstand betragtes som fast, idet den tilhører omgivelserne. Det er denne forskel mellem en indre tilstand og en ydre tilstand, som gør det muligt at adskille årsag og virkning.
Forfatterne spørger både, om en hændelse Yt=yt⊆Yt=yt til tiden t har en faktisk årsag til tiden t-1, og om en hændelse Xt-1=xt-1⊆Xt-1=xt-1 til tiden t-1 har en faktisk virkning til tiden t. De identificerer sådanne faktiske årsager og faktiske virkninger og demonstrerer, at begge perspektiver er nødvendige for at kunne give en fuldstændig årsagsfremstilling for overgangen Xt-1=xt-1 ≺ Yt=yt. De bliver desuden, ved at inddrage alle mulige kontrafaktiske tilstande, i stand til at kvantificere styrken af kausale kæder mellem hændelser og deres faktiske årsag/virkning ved begreber fra den integrerede informationsteori (IIT = Integrated Information Theory).
IIT udtrykker mulige årsagsforhold som styrken af den indre årsag-virkning for et fysisk system (indre eksistens). Udgangspunktet for IIT-formalismen er et dynamisk system S i dets nuværende tilstand st. Man spørger dernæst, hvordan systemets elementer alene og i kombination (komposition) begrænser de mulige fortidige og fremtidige tilstande for systemet (information), samt hvorvidt de gør dette ud over systemets enkelte dele (integration). Man kan for enhver delmængde S=st⊆S=st finde den maksimalt irreducible mængde af mulige årsager og virkninger inden for systemet (exclusion=indskrænkning; forsikringsudtryk?) og kvantificere dets irreducible styrke til at anvende årsag/virkning-sammenhænge (integreret information φ). Oversat til faktiske årsagsforhold er de fem principper som følger:
(1) Eksistens: Årsager og virkninger er faktiske. Den faktiske årsag til en hændelse til tiden t må have fundet sted til tiden t-1, og den faktiske virkning af en hændelse til tiden t-1 må have fundet sted til tiden t i et system, som faktisk eksisterer. En faktisk hændelse er en observeret tilstand.
(2) Komposition: Årsager og virkninger er strukturerede. Enhver delmængde af en observation kan være en hændelse med sin egen faktiske årsag eller virkning. På samme måde kan en delmængde af en faktisk årsag/virkning selv være en adskilt faktisk årsag/virkning for en anden hændelse inden for overgangen.
(3) Information: Årsager og virkninger er specifikke. En hændelse må forøge sandsynligheden for, at dens faktiske årsag/virkning fandt sted sammenlignet med dens forventede sandsynlighed , hvis alle hændelsens mulige tilstande har samme sandsynlighed. En faktisk årsag/virkning må kunne skilnes fra støj.
(4) Integration: Årsager og virkninger er irreducible. Kun irreducible hændelser kan have faktiske årsager eller virkninger. En hændelse må derfor bestemme dens faktiske årsag/virkning irreducibelt ud over de enkelte dele.
(5) Indskrænkning: Årsager og virkninger er bestemte. En hændelse kan højst have èn faktisk årsag/virkning, som er den mindste mængde af elementer, hvis tilstand er den mest irreducibelt bestemt af hændelsen.
Disse postulater forekommer måske noget luftige og abstrakte. Artiklen fortsætter med beskrive, hvad de betyder for overgangen mellem to på hinanden følgende tilstande ved t-1 og t. Tiden antages også at være diskret, idet intervallet mellem to tilstande er 1. Det er mit håb, at den følgende introduktion er tilstrækkelig baggrund for læsning af artiklen om PyPhi programmet.
For at kunne anvende disse principper på en analyse af faktiske årsagsforhold vil vi (som tidligere nævnt) antage et diskret dynamisk system S bestående af n vekselvirkende elementer Si, hvor i=1,…,n (Fig. 1A). Hvert element må have mindst to interne tilstande, som kan observeres og behandles. Systemet er desuden udstyret med en input-output funktion fi, som bestemmer et elements output-tilstand alene ud fra systemets foregående tilstand: si,t=fi(St-1=st-1). Dette betyder, at alle elementer er betinget uafhængige og givet alene ved systemets forudgående tilstand st-1, så enhver overgang St-1=st-1 ≺ St=st kan repræsenteres inden for et ordnet aperiodisk kausalt netværk (Fig. 1B). S er derfor fuldt beskrevet ved systemets overgangssandsynligheder (Fig. 1C):
p(St=st|St-1=st-1) = Πip(Si,t=si,t|St-1=st-1), ∀ st,st-1.
Forfatterne fortolker S som et fysisk system med vekselvirkende fysiske elementer i modsætning til abstrakte variabler. Formålet er at formulere en kvantitativ beskrivelse af faktiske årsagsforhold uden sammenblanding af problemer med relation til ufuldstændig viden. Vi antager derfor fuld viden om det fysiske system. Vi definerer en observation som den faktuelle tilstand for en delmængde af elementer inden for systemetet S til et bestemt tidspunkt. Systemets overgang er fuldt bestemt ved dens transition probability matrix (TPM).
STOCKHOLM—Last week, here at the International Conference on Machine Learning (ICML), a group of researchers described a turtle they had 3D printed. Most people would say it looks just like a turtle, but an artificial intelligence (AI) algorithm saw it differently. Most of the time, the AI thought the turtle looked like a rifle. Similarly, it saw a 3D-printed baseball as an espresso. These are examples of “adversarial attacks”—subtly altered images, objects, or sounds that fool AIs without setting off human alarm bells.
Impressive advances in AI—particularly machine learning algorithms that can recognize sounds or objects after digesting training data sets—have spurred the growth of living room voice assistants and autonomous cars. But these AIs are surprisingly vulnerable to being spoofed. At the meeting here, adversarial attacks were a hot subject, with researchers reporting novel ways to trick AIs as well as new ways to defend them. Somewhat ominously, one of the conference’s two best paper awards went to a study suggesting protected AIs aren’t as secure as their developers might think. “We in the field of machine learning just aren’t used to thinking about this from the security mindset,” says Anish Athalye, a computer scientist at the Massachusetts Institute of Technology (MIT) in Cambridge, who co-led the 3D-printed turtle study.
Computer scientists working on the attacks say they are providing a service, like hackers who point out software security flaws. “We need to rethink all of our machine learning pipeline to make it more robust,” says Aleksander Madry, a computer scientist at MIT. Researchers say the attacks are also useful scientifically, offering rare windows into AIs called neural networks whose inner logic cannot be explained transparently. The attacks are “a great lens through which we can understand what we know about machine learning,” says Dawn Song, a computer scientist at the University of California, Berkeley.
The attacks are striking for their inconspicuousness. Last year, Song and her colleagues put some stickers on a stop sign, fooling a common type of image recognition AI into thinking it was a 45-mile-per-hour speed limit sign—a result that surely made autonomous car companies shudder. A few months ago, Nicholas Carlini, a computer scientist at Google in Mountain View, California, and a colleague reported adding inaudible elements to a voice sample that sounded to humans like “without the data set the article is useless,” but that an AI transcribed as “OK Google, browse to evil.com.”
Researchers are devising even more sophisticated attacks. At an upcoming conference, Song will report a trick that makes an image recognition AI not only mislabel things, but hallucinate them. In a test, Hello Kitty loomed in the machine’s view of street scenes, and cars disappeared.
Some of these assaults use knowledge of the target algorithms’ innards, in what’s called a white box attack. The attackers can see, for instance, an AI’s “gradients,” which describe how a slight change in the input image or sound will move the output in a predicted direction. If you know the gradients, you can calculate how to alter inputs bit by bit to obtain the desired wrong output—a label of “rifle,” say—without changing the input image or sound in ways obvious to humans. In a more challenging black box attack, an adversarial AI has to probe the target AI from the outside, seeing only the inputs and outputs. In another study at ICML, Athalye and his colleagues demonstrated a black box attack against a commercial system, Google Cloud Vision. They tricked it into seeing an invisibly perturbed image of two skiers as a dog.
AI developers keep stepping up their defenses. One technique embeds image compression as a step in an image recognition AI. This adds jaggedness to otherwise smooth gradients in the algorithm, foiling some meddlers. But in the cat-and-mouse game, such “gradient obfuscation” has also been one-upped. In one of the ICML’s award-winning papers, Carlini, Athalye, and a colleague analyzed nine image recognition algorithms from a recent AI conference. Seven relied on obfuscated gradients as a defense, and the team was able to break all seven, by, for example, sidestepping the image compression. Carlini says none of the hacks took more than a couple days.
A stronger approach is to train an algorithm with certain constraints that prevent it from being led astray by adversarial attacks, in a verifiable, mathematical way. “If you can verify, that ends the game,” says Pushmeet Kohli, a computer scientist at DeepMind in London. But these verifiable defenses, two of which were presented at ICML, so far do not scale to the large neural networks in modern AI systems. Kohli says there is potential to expand them, but Song worries they will have real-world limitations. “There’s no mathematical definition of what a pedestrian is,” she says, “so how can we prove that the self-driving car won’t run into a pedestrian? You cannot!”
Carlini hopes developers will think harder about how their defenses work—and how they might fail—in addition to their usual concern: performing well on standard benchmarking tests. “The lack of rigor is hurting us a lot,” he says.
The discovery of the ultra-diffuse galaxy NGC 1052-DF2 and its peculiar population of star clusters has raised new questions about the connections between galaxies and dark matter halos at the extremes of galaxy formation. In light of debates over the measured velocity dispersion of its star clusters and the associated mass estimate, we constrain mass models of DF2 using its observed kinematics with a range of priors on the halo mass. Models in which the galaxy obeys a standard stellar-halo mass relation are in tension with the data and also require a large central density core. Better fits are obtained when the halo mass is left free, even after accounting for increased model complexity. The dynamical mass-to-light ratio for our model with a weak prior on the halo mass is 1.7+0.7-0.5 M⊙/L⊙,V, consistent with the stellar population estimate for DF2. We use tidal analysis to find that the low-mass models are consistent with the undisturbed isophotes of DF2. Finally we compare with Local Group dwarf galaxies and demonstrate that DF2 is an outlier in both its spatial extent and its relative dark matter deficit.
Recent observations of the Cosmic Microwave Background (CMB) have allowed claims for statistical anomalies in the behaviour of the CMB fluctuations to be made. Although the statistical significance of these remain only at the ∼(2-3)σ significance level, the fact that there are many different anomalies, several of which support a possible deviation from statistical isotropy, warrants the search for models affording a common mechanism to generate them. The goal of this paper is to investigate whether all these anomalies could originate from non-Gaussianity and to determine which properties such non-Gaussian models should have. We present a simple isotropic, non-Gaussian class of toy-models which can reproduce six heavily debated anomalies. We compare the presence of anomalies in simulated toy-model maps as well as Gaussian maps. We find that the following anomalies which are also found in Planck data, are commonly occuring in the toy-model maps: (1) Large scale hemispherical asymmetry (large scale dipolar modulation), (2) small scale hemispherical asymmetry (alignment of the spatial distribution of CMB power over all scales ℓ=[2,1500]) , (3) a strongly non-Gaussian hot or cold spot, (4) a low power spectrum amplitude for ℓ<30, including specifically (5) a low quadrupole and an unusual alignment between the quadrupole and the octopole, and (6) parity asymmetry of the lowest multipoles. We remark that this class of toy-models resembles models of primordial non-Gaussianity characterized by strongly scale-dependent gNL-like trispectra.
De senere mange års observationer af den kosmiske mikrobølge-baggrundsstråling (CMB) har tilladt påstande om statistiske anomalier i baggrundsstrålingens fluktuationer. Skønt den statistiske signifikans begrænser sig til et (2-3)σ-niveau, er det et faktum, at flere af disse anomalier støtter en mulig afvigelse fra statistisk isotropi. Disse anomalier definerer nogle faste retninger i rummet. Man er derfor på udkik efter en fælles fysisk mekanisme, som kan forklare alle anomalierne.
Det er artiklens formål at undersøge, om anomalierne kan skyldes en ikke-gaussisk fordeling af fluktuationerne, samt at bestemme, hvilke egenskaber sådanne ikke-gaussiske modeller må have. Forfatterne præsenterer en simpel isotrop, ikke-gaussisk klasse af legetøjsmodeller, som kan reproducere seks meget omdiskuterede anomalier.
Forfatterne er blevet inspireret af de ikke-liniære led i gravitationspotentialet, som forekommer i visse inflationsmodeller. De undersøger isotrope, men ikke-gaussiske modeller, hvori de ikke-gaussiske fluktioner er årsagen til den tilsyneladende afvigelse fra statistisk isotropi i de observerede data.
Inflationsmodeller kan have både 2.-ordens- og 3.-ordensled i gravitationspotentialet:
Φ(x) = ΦG(x) + fNL(ΦG2(x) – <ΦG2(x)>) + gNLΦG3(x)
ΦG(x) er den lineære gaussiske del af gravitationspotentialet. Baggrundsstrålingen har en kold plet, som kun kan frembringes af 3.-ordensleddet, så forfatterne retter opmærksomheden mod gNL-leddet. Forskerholdet bag Planck har imidlertid vist, et skalauafhængigt gNL-led ikke kan forklare anomalierne, så artiklen undersøger i stedet gNL-lignende legetøjsmodeller med en kraftig skalaafhængighed.
Forfatterne understreger, at artiklens formål ikke er at finde en fysisk model, som kan tilpasses de observerede data, men i stedet at bestemme, hvilke egenskaber en fysisk model må have.
We present cosmological parameter results from the final full-mission Planck measurements of the CMB anisotropies. We find good consistency with the standard spatially-flat 6-parameter ΛCDM cosmology having a power-law spectrum of adiabatic scalar perturbations (denoted “base-ΛCDM” in this paper), from polarization, temperature, and lensing, separately and in combination. A combined analysis gives dark matter density Ωch2 = 0.120±0.001, baryon density Ωbh2 = 0.0224±0.0001, scalar spectral index ns = 0.965±0.004, and optical depth τ = 0.054±0.007 (in this abstract we quote 68% confidence regions on measured parameters and 95% on upper limits). The angular acoustic scale is measured to 0.03% precision, with 100θ* = 1.0411±0.0003. These results are only weakly dependent on the cosmological model and remain stable, with somewhat increased errors, in many commonly considered extensions. Assuming the base-ΛCDM cosmology, the inferred late-Universe parameters are: Hubble constant H0 = (67.4±0.5) km/s/Mpc; matter density parameter Ωm = 0.315±0.007; and matter fluctuation amplitude σ8 = 0.811±0.006. We find no compelling evidence for extensions to the base-ΛCDM model. Combining with BAO we constrain the effective extra relativistic degrees of freedom to be Neff = 2.99±0.17, and the neutrino mass is tightly constrained to ∑ mν < 0.12 eV. The CMB spectra continue to prefer higher lensing amplitudes than predicted in base-ΛCDM at over 2σ, which pulls some parameters that affect the lensing amplitude away from the base-ΛCDM model; however, this is not supported by the lensing reconstruction or (in models that also change the background geometry) BAO data.
Forfatterne finder ikke nogen overbevisende evidens for nødvendigheden af en udvidelse af ΛCDM-modellen.
Neutrinos interact only very weakly with matter, but giant detectors have succeeded in detecting small numbers of astrophysical neutrinos. Aside from a diffuse background, only two individual sources have been identified: the Sun and a nearby supernova in 1987. A multiteam collaboration detected a high-energy neutrino event whose arrival direction was consistent with a known blazar—a type of quasar with a relativistic jet oriented directly along our line of sight. The blazar, TXS 0506+056, was found to be undergoing a gamma-ray flare, prompting an extensive multiwavelength campaign. Motivated by this discovery, the IceCube collaboration examined lower-energy neutrinos detected over the previous several years, finding an excess emission at the location of the blazar. Thus, blazars are a source of astrophysical neutrinos.
Neutrinos are tracers of cosmic-ray acceleration: electrically neutral and traveling at nearly the speed of light, they can escape the densest environments and may be traced back to their source of origin. High-energy neutrinos are expected to be produced in blazars: intense extragalactic radio, optical, x-ray, and, in some cases, γ-ray sources characterized by relativistic jets of plasma pointing close to our line of sight. Blazars are among the most powerful objects in the Universe and are widely speculated to be sources of high-energy cosmic rays. These cosmic rays generate high-energy neutrinos and γ-rays, which are produced when the cosmic rays accelerated in the jet interact with nearby gas or photons. On 22 September 2017, the cubic-kilometer IceCube Neutrino Observatory detected a ~290-TeV neutrino from a direction consistent with the flaring γ-ray blazar TXS 0506+056. We report the details of this observation and the results of a multiwavelength follow-up campaign.
Multimessenger astronomy aims for globally coordinated observations of cosmic rays, neutrinos, gravitational waves, and electromagnetic radiation across a broad range of wavelengths. The combination is expected to yield crucial information on the mechanisms energizing the most powerful astrophysical sources. That the production of neutrinos is accompanied by electromagnetic radiation from the source favors the chances of a multiwavelength identification. In particular, a measured association of high-energy neutrinos with a flaring source of γ-rays would elucidate the mechanisms and conditions for acceleration of the highest-energy cosmic rays. The discovery of an extraterrestrial diffuse flux of high-energy neutrinos, announced by IceCube in 2013, has characteristic properties that hint at contributions from extragalactic sources, although the individual sources remain as yet unidentified. Continuously monitoring the entire sky for astrophysical neutrinos, IceCube provides real-time triggers for observatories around the world measuring γ-rays, x-rays, optical, radio, and gravitational waves, allowing for the potential identification of even rapidly fading sources.
A high-energy neutrino-induced muon track was detected on 22 September 2017, automatically generating an alert that was distributed worldwide within 1 min of detection and prompted follow-up searches by telescopes over a broad range of wavelengths. On 28 September 2017, the Fermi Large Area Telescope Collaboration reported that the direction of the neutrino was coincident with a cataloged γ-ray source, 0.1° from the neutrino direction. The source, a blazar known as TXS 0506+056 at a measured redshift of 0.34, was in a flaring state at the time with enhanced γ-ray activity in the GeV range. Follow-up observations by imaging atmospheric Cherenkov telescopes, notably the Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescopes, revealed periods where the detected γ-ray flux from the blazar reached energies up to 400 GeV. Measurements of the source have also been completed at x-ray, optical, and radio wavelengths. We have investigated models associating neutrino and γ-ray production and find that correlation of the neutrino with the flare of TXS 0506+056 is statistically significant at the level of 3 standard deviations (sigma). On the basis of the redshift of TXS 0506+056, we derive constraints for the muon-neutrino luminosity for this source and find them to be similar to the luminosity observed in γ-rays.
The energies of the γ-rays and the neutrino indicate that blazar jets may accelerate cosmic rays to at least several PeV. The observed association of a high-energy neutrino with a blazar during a period of enhanced γ-ray emission suggests that blazars may indeed be one of the long-sought sources of very-high-energy cosmic rays, and hence responsible for a sizable fraction of the cosmic neutrino flux observed by IceCube.
Previous detections of individual astrophysical sources of neutrinos are limited to the Sun and the supernova 1987A, whereas the origins of the diffuse flux of high-energy cosmic neutrinos remain unidentified. On 22 September 2017, we detected a high-energy neutrino, IceCube-170922A, with an energy of ~290 tera–electron volts. Its arrival direction was consistent with the location of a known γ-ray blazar, TXS 0506+056, observed to be in a flaring state. An extensive multiwavelength campaign followed, ranging from radio frequencies to γ-rays. These observations characterize the variability and energetics of the blazar and include the detection of TXS 0506+056 in very-high-energy γ-rays. This observation of a neutrino in spatial coincidence with a γ-ray–emitting blazar during an active phase suggests that blazars may be a source of high-energy neutrinos.
The bright BL Lac object TXS 0506+056 is a most likely counterpart of the IceCube neutrino event EHE 170922A. The lack of this redshift prevents a comprehensive understanding of the modeling of the source. We present high signal-to-noise optical spectroscopy, in the range 410-900 nm, obtained at the 10.4m Gran Telescopio Canarias. The spectrum is characterized by a power law continuum and is marked by faint interstellar features. In the regions unaffected by these features, we found three very weak (EW ∼ 0.01 nm) emission lines that we identify with [O II] 372.7 nm, [O III] 500.7 nm, and [N II] 658.3 nm, yielding the redshift z=0.3365±0.0010.
On September 22nd 2017, the IceCube Neutrino Observatory reported a muon track from a neutrino with a very good positional accuracy. The alert triggered a number of astronomical follow-up campaigns, and the Fermi gamma-ray telescope found as counterpart an object named TXS0506+056 in a very bright, flaring state; this observation may be the first direct evidence for an extragalactic source of very high-energy cosmic rays. While this and subsequent observations provide the observational picture across the electromagnetic spectrum, answering where in the spectrum signatures of cosmic rays arise and what the source properties must be, given the observational constraints, requires a self-consistent description of the processes at work. Here we perform a detailed time-dependent modeling of these relevant processes and study a set of self-consistent models for the source. We find a slow but over-proportional increase of the neutrino flux during the flare compared to the production enhancement of energetic cosmic rays. We also demonstrate that interactions of energetic cosmic-ray ions result in predominantly hard X-ray emission, strongly constraining the expected number of neutrinos, and to a lesser degree in TeV gamma rays. Optical photons and GeV-scale gamma rays are predominantly radiated by electrons. Our results indicate that especially future X-ray and TeV-scale gamma-ray observations of nearby objects can be used to identify more such events.
The discovery of high-energy astrophysical neutrinos by IceCube kicked off a new line of research to identify the electromagnetic counterparts producing these neutrinos. Among the extragalactic sources, active galactic nuclei (AGN), and in particular Blazars, are promising candidate neutrino emitters. Their structure, with a relativistic jet pointing to the Earth, offers a natural accelerator of particles and for this reason a perfect birthplace of high energy neutrinos. A good characterisation of the spectral energy distribution (SED) of these sources can improve the understanding of the physical composition of the source and the emission processes involved. Starting from our previous works in which we assumed a correlation between the γ-ray and the neutrino flux of the BL Lacs of the 2FHL catalogue (detected by Fermi above 50GeV), we select those BL Lac in spatial correlation with the IceCube events. We obtain a sample of 7 sources and we start an observational campaign to have a better characterisation of the synchrotron peak. During the analysis of the data a new source has been added because of its position inside the angular uncertainty of a muon track event detected by IceCube. This source, namely TXS0506+056, was in a high-state during the neutrino event and we will consider it as benchmark to check the proprieties of the other sources of the sample during the related neutrino detection.
We obtain a better characterisation of the SED for the sources of our sample. A prospective extreme Blazar, a very peculiar low synchrotron peak (LSP) source with a large separation of the two peaks and a twin of TXS0506+056 come up. We also provide the γ-ray light curve to check the trend of the sources around the neutrino detection but no clears patterns are in common among the sources.
A neutrino with energy of ∼290 TeV, IceCube-170922A, was detected in coincidence with the BL Lac object TXS0506+056 during enhanced gamma-ray activity, with chance coincidence being rejected at ∼3σ level. We monitored the object in the very-high-energy (VHE) band with the MAGIC telescopes for ∼41 hours from 1.3 to 40.4 days after the neutrino detection. Day-timescale variability is clearly resolved. We interpret the quasi-simultaneous neutrino and broadband electromagnetic observations with a novel one-zone lepto-hadronic model, based on interactions of electrons and protons co-accelerated in the jet with external photons originating from a slow-moving plasma sheath surrounding the faster jet spine. We can reproduce the multiwavelength spectra of TXS0506+056 with neutrino rate and energy compatible with IceCube-170922A, and with plausible values for the jet power of ∼1045 – 4×1046 erg/s. The steep spectrum observed by MAGIC is concordant with internal γγ absorption above a few tens of GeV entailed by photohadronic production of a ∼290 TeV neutrino, corroborating a genuine connection between the multi-messenger signals. In contrast to previous predictions of predominantly hadronic emission from neutrino sources, the gamma-rays can be mostly ascribed to inverse Compton up-scattering of external photons by accelerated electrons. The X-ray and VHE bands provide crucial constraints on the emission from both accelerated electrons and protons. We infer that the maximum energy of protons in the jet co-moving frame can be in the range ∼1014 to 1018 eV.
While active galactic nuclei with relativistic jets have long been prime candidates for the origin of extragalactic cosmic rays and neutrinos, the BL Lac object TXS 0506+056 is the first astrophysical source observed to be associated with some confidence (∼3σ) with a high-energy neutrino, IceCube-170922A, detected by the IceCube Observatory. The source was found to be active in high-energy gamma-rays with Fermi-LAT and in very-high-energy gamma-rays with the MAGIC telescopes. To consistently explain the observed neutrino and multi-wavelength electromagnetic emission of TXS 0506+056, we investigate in detail single-zone models of lepto-hadronic emission, assuming co-spatial acceleration of electrons and protons in the jet, and synchrotron photons from the electrons as targets for photo-hadronic neutrino production. The parameter space concerning the physical conditions of the emission region and particle populations is comprehensively explored for scenarios where the gamma-rays are dominated by either 1) proton synchrotron emission or 2) synchrotron-self-Compton emission, with a minor but non-negligible contribution from photo-hadronic cascades in both cases. We find that the latter provides acceptable solutions, while the former is strongly disfavoured due to the insufficient neutrino production rate.
We present the dissection in space, time, and energy of the region around the IceCube-170922A neutrino alert. This study is motivated by: (1) the first association between a neutrino alert and a blazar in a flaring state, TXS 0506+056; (2) the evidence of a neutrino flaring activity during 2014 – 2015 from the same direction; (3) the lack of an accompanying simultaneous γ-ray enhancement from the same counterpart; (4) the contrasting flaring activity of a neighbouring bright γ-ray source, the blazar PKS 0502+049, during 2014 – 2015. Our study makes use of multi-wavelength archival data accessed through Open Universe tools and includes a new analysis of Fermi-LAT data. We find that PKS 0502+049 contaminates the γ-ray emission region at low energies but TXS 0506+056 dominates the sky above a few GeV. TXS 0506+056, which is a very strong (top percent) radio and γ-ray source, is in a high γ-ray state during the neutrino alert but in a low though hard γ-ray state in coincidence with the neutrino flare. Both states can be reconciled with the energy associated with the neutrino emission and, in particular during the low/hard state, there is evidence that TXS 0506+056 has undergone a hadronic flare with very important implications for blazar modelling. All multi-messenger diagnostics reported here support a single coherent picture in which TXS 0506+056, a very high energy γ-ray blazar, is the only counterpart of all the neutrino emissions in the region and therefore the most plausible first non-stellar neutrino and, hence, cosmic ray source.
Detection of the IceCube-170922A neutrino coincident with the flaring blazar TXS 0506+056, the first and only 3-sigma high-energy neutrino source association to date, offers a potential breakthrough in our understanding of high-energy cosmic particles and blazar physics. We present a comprehensive analysis of TXS 0506+056 during its flaring state, using newly collected Swift, NuSTAR, and X-shooter data with Fermi observations and numerical models to constrain the blazar’s particle acceleration processes and multimessenger (electromagnetic and high-energy neutrino) emissions. Accounting properly for electromagnetic cascades in the emission region, we find a physically-consistent picture only within a hybrid leptonic scenario, with gamma-rays produced by external inverse-Compton processes and high-energy neutrinos via a radiatively-subdominant hadronic component. We derive robust constraints on the blazar’s neutrino and cosmic-ray emissions and demonstrate that, because of cascade effects, the 0.1-100keV emissions of TXS 0506+056 serve as a better probe of its hadronic acceleration and high-energy neutrino production processes than its GeV-TeV emissions. If the IceCube neutrino association holds, physical conditions in the TXS 0506+056 jet must be close to optimal for high-energy neutrino production, and are not favorable for ultra-high-energy cosmic-ray acceleration. Alternatively, the challenges we identify in generating a significant rate of IceCube neutrino detections from TXS 0506+056 may disfavor single-zone models. In concert with continued operations of the high-energy neutrino observatories, we advocate regular X-ray monitoring of TXS 0506+056 and other blazars in order to test single-zone blazar emission models, clarify the nature and extent of their hadronic acceleration processes, and carry out the most sensitive possible search for additional multimessenger sources.