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Speculative Decoding (SD) enforces strict distributional equivalence to the target model when accepting candidate tokens. While it maintains the target model’s generation quality, this strict equivalence limits the speedup achievable by SD and prevents users from trading deviations from the target distribution in exchange for further inference speed gains. To address these limitations, we introduce Fuzzy Speculative Decoding (FSD) - a decoding algorithm that generalizes SD by accepting candidate tokens based on the divergences between the target and draft model distributions. By allowing for controlled divergence from the target model, FSD enables users to flexibly trade generation quality for inference speed. Across several benchmarks, our method is able to achieve significant runtime improvements of over 5 tokens per second faster than SD at only an approximate 2% absolute reduction in benchmark accuracy. In many cases, FSD is even able to match SD benchmark accuracy at over 2 tokens per second faster, demonstrating that distributional equivalence is not necessary to maintain target model performance. Furthermore, FSD can be seamlessly integrated into existing SD extensions; we demonstrate this by applying FSD to EAGLE-2, greatly enhancing this existing extension’s efficiency while allowing it to leverage FSD’s tunable quality-speed trade-off. 

Synthesis of Ag–Au nanoparticles of different sizes, compositions, element configurations (alloy- and core–shell-like) and electrocatalytic activities by using precursor-loaded PS–P2VP reverse micelles as nanoreactors. 

In everyday life, people routinely make decisions that involve irredeemable risks such as death (e.g., while driving). Even though these decisions under extinction risk are common, practically important, and have different properties compared to the types of decisions typically studied by decision scientists, they have received little research attention. The present work advances the formal understanding of decision making under extinction risk by introducing a novel experimental paradigm, the Extinction Gambling Task (EGT). We derive optimal strategies for three different types of extinction and near-extinction events, and compare them to participants’ choices in three experiments. Leveraging computational modelling to describe strategies at the individual level, we document strengths and shortcomings in participants’ decisions under extinction risk. Specifically, we find that, while participants are relatively good in terms of the qualitative strategies they employ, their decisions are nevertheless affected by loss chasing, scope insensitivity, and opportunity cost neglect. We hope that by formalising decisions under extinction risk and providing a task to study them, this work will facilitate future research on an important topic that has been largely ignored. 

We survey the opportunities offered by the detection of the forward muons that accompany the creation of neutral effective vector bosons at a muon collider, in different kinematic regimes. Vectors with relatively low energy produce the Higgs boson and the extended muon angular coverage enables studies of the Higgs properties, such as the measurement of the inclusive production cross section and the branching ratio to invisible final states. New heavy particles could be produced by vectors of higher energy, through Higgs portal interactions. If the new particles are invisible, the detection of the forward muons is essential in order to search for this scenario. The angular correlations of the forward muons are sensitive to the quantum interference between the vector-boson helicity amplitudes and can be exploited for the characterization of vector-boson scattering and fusion processes. This is illustrated by analyzing the $CP$properties of the Higgs coupling to the $Z$boson. Our findings provide a physics case and a set of benchmarks for the design of a dedicated forward muon detector. Published by the American Physical Society2025 

<h1 id="summary">Summary</h1> <p>Title: Data Release for A search for extremely-high-energy neutrinos and first constraints on the ultra-high-energy cosmic-ray proton fraction with IceCube</p> <p>The IceCube observatory analyzed 12.6 years of data in search of extremely-high-energy (EHE) neutrinos above 5 PeV. The resultant limit of the search (Fig 1), and the effective area of the event selection (Fig 7), are provided in this data release.</p> <h1 id="contents">Contents</h1> <ul> <li><p>README file: this file</p> </li> <li><p><code>differential_limit_and_sensitivity.csv</code>: a comma separated value file, giving the observed experimental differential limit, and sensitivity, of the search as a function of neutrino energy. This is the content of Fig 1 in the paper. The first column is the neutrino energy in GeV. The second column is the limit in units of GeV/cm2/s/sr. The third column is the sensitivity in units of GeV/cm2/s/sr.</p> </li> <li><p><code>effective_area.csv</code>: a comma separated value file, giving the effective area of the search as a function of energy. This is the content of Fig 7 in the paper. The first column is the neutrino energy in GeV. The second column is the total effective area of the search, summed across neutrino flavors, and averaged across neutrinos and antineutrinos, in meters-squared. The third column is the effective area of the search for the average of electron neutrino and electron antineutrinos in units of meters-squared. The fourth column is the same as the third, but for muon-flavor neutrinos. The fifth column is the same as the third and fourth, but for tau-flavor neutrinos.</p> </li> <li><p><code>demo.py</code>: a short python script to demonstrate how to read the files. Run like <code>python demo.py</code>. A standard base python installation is sufficient, as the only dependencies are numpy and matplotlib.</p> </li> </ul> <h1 id="contacts">Contacts</h1> <p>For any questions about this data release, please write to analysis@icecube.wisc.edu</p>