2,183 research outputs found
An XMM-Newton View of the Radio Galaxy 3C 411
We present the first high signal-to-noise XMM-Newton observations of the
broad-line radio galaxy 3C 411. After fitting various spectral models, an
absorbed double power-law continuum and a blurred relativistic disk reflection
model (kdblur) are found to be equally plausible descriptions of the data.
While the softer power-law component (=2.11) of the double power-law
model is entirely consistent with that found in Seyfert galaxies (and hence
likely originates from a disk corona), the additional power law component is
very hard (=1.05); amongst the AGN zoo, only flat-spectrum radio
quasars have such hard spectra. Together with the very flat radio-spectrum
displayed by this source, we suggest that it should instead be classified as a
FSRQ. This leads to potential discrepancies regarding the jet inclination
angle, with the radio morphology suggesting a large jet inclination but the
FSRQ classification suggesting small inclinations. The kdblur model predicts an
inner disk radius of at most 20 r and relativistic reflection
The taxonomic position of some teratocephalid nematodes : a scanning electron microscope study
Radioiodinated rose bengal kinetics in extrahepatic biliary obstruction and hepatic homograft rejection in the dog.
Prediction and explanation in the multiverse
Probabilities in the multiverse can be calculated by assuming that we are
typical representatives in a given reference class. But is this class well
defined? What should be included in the ensemble in which we are supposed to be
typical? There is a widespread belief that this question is inherently vague,
and that there are various possible choices for the types of reference objects
which should be counted in. Here we argue that the ``ideal'' reference class
(for the purpose of making predictions) can be defined unambiguously in a
rather precise way, as the set of all observers with identical information
content. When the observers in a given class perform an experiment, the class
branches into subclasses who learn different information from the outcome of
that experiment. The probabilities for the different outcomes are defined as
the relative numbers of observers in each subclass. For practical purposes,
wider reference classes can be used, where we trace over all information which
is uncorrelated to the outcome of the experiment, or whose correlation with it
is beyond our current understanding. We argue that, once we have gathered all
practically available evidence, the optimal strategy for making predictions is
to consider ourselves typical in any reference class we belong to, unless we
have evidence to the contrary. In the latter case, the class must be
correspondingly narrowed.Comment: Minor clarifications adde
Spatial Regulation of Air Toxics Hot Spots
This paper analyzes the potential implications, in terms of net social costs and distribution of risks and abatement costs, of a policy to address the problem of air toxics “hot spots.” The policy we analyze involves regulation of air toxics sources at increasingly finer spatial resolutions. We develop a model of a decisionmaker choosing emission standards within a net cost minimization framework. Empirical application of the model to two counties in Florida demonstrates that regulation at finer resolutions could involve trade-offs between net social costs and equitable distribution of risks and, in some settings, between individual and population risks
Hot Spots Regulation and Environmental Justice
This paper analyzes whether regulating “hot spots” of toxic air pollution by increasing the spatial resolution of regulation could address environmental justice (EJ) concerns. To examine this question, this paper develops a decision model of a regulator choosing emission controls within a net cost minimizing framework. An empirical application of the model using air toxic emission data for Escambia and Santa Rosa Counties in Florida estimates the emission standards and spatial distribution of risks at a coarse and a finer spatial resolutions. Implications for EJ are analyzed by combining the simulated spatial risk distributions at the two resolutions with the demographic data. Results indicate that different measures of EJ point to different conclusions regarding the question of whether finer resolution regulation alleviates EJ concerns. The paper concludes with a discussion of the implications for EJ policy
Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways
We present Lil-Bevo, our submission to the BabyLM Challenge. We pretrained
our masked language models with three ingredients: an initial pretraining with
music data, training on shorter sequences before training on longer ones, and
masking specific tokens to target some of the BLiMP subtasks. Overall, our
baseline models performed above chance, but far below the performance levels of
larger LLMs trained on more data. We found that training on short sequences
performed better than training on longer sequences.Pretraining on music may
help performance marginally, but, if so, the effect seems small. Our targeted
Masked Language Modeling augmentation did not seem to improve model performance
in general, but did seem to help on some of the specific BLiMP tasks that we
were targeting (e.g., Negative Polarity Items). Training performant LLMs on
small amounts of data is a difficult but potentially informative task. While
some of our techniques showed some promise, more work is needed to explore
whether they can improve performance more than the modest gains here. Our code
is available at https://github.com/venkatasg/Lil-Bevo and out models at
https://huggingface.co/collections/venkatasg/babylm-653591cdb66f4bf68922873aComment: Proceedings of the BabyLM Challeng
Anthropic reasoning in multiverse cosmology and string theory
Anthropic arguments in multiverse cosmology and string theory rely on the
weak anthropic principle (WAP). We show that the principle, though ultimately a
tautology, is nevertheless ambiguous. It can be reformulated in one of two
unambiguous ways, which we refer to as WAP_1 and WAP_2. We show that WAP_2, the
version most commonly used in anthropic reasoning, makes no physical
predictions unless supplemented by a further assumption of "typicality", and we
argue that this assumption is both misguided and unjustified. WAP_1, however,
requires no such supplementation; it directly implies that any theory that
assigns a non-zero probability to our universe predicts that we will observe
our universe with probability one. We argue, therefore, that WAP_1 is
preferable, and note that it has the benefit of avoiding the inductive
overreach characteristic of much anthropic reasoning.Comment: 7 pages. Expanded discussion of selection effects and some minor
clarifications, as publishe
An Infrared Divergence Problem in the cosmological measure theory and the anthropic reasoning
An anthropic principle has made it possible to answer the difficult question
of why the observable value of cosmological constant (
GeV) is so disconcertingly tiny compared to predicted value of vacuum
energy density GeV. Unfortunately, there is a
darker side to this argument, as it consequently leads to another absurd
prediction: that the probability to observe the value for randomly
selected observer exactly equals to 1. We'll call this controversy an infrared
divergence problem. It is shown that the IRD prediction can be avoided with the
help of a Linde-Vanchurin {\em singular runaway measure} coupled with the
calculation of relative Bayesian probabilities by the means of the {\em
doomsday argument}. Moreover, it is shown that while the IRD problem occurs for
the {\em prediction stage} of value of , it disappears at the {\em
explanatory stage} when has already been measured by the observer.Comment: 9 pages, RevTe
Self-Modification of Policy and Utility Function in Rational Agents
Any agent that is part of the environment it interacts with and has versatile
actuators (such as arms and fingers), will in principle have the ability to
self-modify -- for example by changing its own source code. As we continue to
create more and more intelligent agents, chances increase that they will learn
about this ability. The question is: will they want to use it? For example,
highly intelligent systems may find ways to change their goals to something
more easily achievable, thereby `escaping' the control of their designers. In
an important paper, Omohundro (2008) argued that goal preservation is a
fundamental drive of any intelligent system, since a goal is more likely to be
achieved if future versions of the agent strive towards the same goal. In this
paper, we formalise this argument in general reinforcement learning, and
explore situations where it fails. Our conclusion is that the self-modification
possibility is harmless if and only if the value function of the agent
anticipates the consequences of self-modifications and use the current utility
function when evaluating the future.Comment: Artificial General Intelligence (AGI) 201
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