Hands Are Hard: Unlearning How We Talk About Machine Learning in the Arts

Evoking the spirits of Fluxus scores and art education drawing books from the early 1900s, this instructional art object guides the reader through a series of poetic prompts, designed to investigate the phenomenon of “bad hands” created by generative artificial intelligence. By now everyone has seen the AI-generated images of hands from the uncanny valley. A body of discourse has emerged to discuss “bad hands” in generated images, including their usefulness as a media literacy tool for identifying synthetic images and as evidence of the continued necessity for humans’ artistic abilities. However, these applications may be short-lived as it is only a matter of time before these systems are capable of rendering human hands that are indistinguishable from our own. Before these “bad hands” disappear from view, they might lend artists/educators a hand – a chance to unlearn how we think about machine learning. By learning to create intentional “bad hands,” these creative prompts not only reveal the ways these systems have defined “bad” and “hands,” but also equip us to interrogate the definition of human hiding inside of its mysterious black box

Gluon polarisation from high transverse momentum hadron pairs production (COMPASS)

A new preliminary result of a gluon polarisation \Delta G/G obtained selecting high transverse momentum hadron pairs in DIS events with Q^2>1 (GeV/c)^2 is presented. Data has been collected by COMPASS at CERN during the 2002-2004 years. In the extraction of $\Delta G/G$ contributions coming from the leading order $\gamma q$ and QCD processes are taken into account. A new weighting method based on a neural network approach is used. Also a preliminary result of \Delta G/G for events with Q^2<1 (GeV/c)^2 is presented.Comment: Advanced Studies Institute On Symmetries And Spin (SPIN-Praha-2008) 20-26 Jul 2008, Prague, Czech Republic. The proceedings of the ASI will be published in The European Physical Journal Special Topics (EJP ST) Serie

Revisiting Actor Programming in C++

The actor model of computation has gained significant popularity over the last decade. Its high level of abstraction makes it appealing for concurrent applications in parallel and distributed systems. However, designing a real-world actor framework that subsumes full scalability, strong reliability, and high resource efficiency requires many conceptual and algorithmic additives to the original model. In this paper, we report on designing and building CAF, the "C++ Actor Framework". CAF targets at providing a concurrent and distributed native environment for scaling up to very large, high-performance applications, and equally well down to small constrained systems. We present the key specifications and design concepts---in particular a message-transparent architecture, type-safe message interfaces, and pattern matching facilities---that make native actors a viable approach for many robust, elastic, and highly distributed developments. We demonstrate the feasibility of CAF in three scenarios: first for elastic, upscaling environments, second for including heterogeneous hardware like GPGPUs, and third for distributed runtime systems. Extensive performance evaluations indicate ideal runtime behaviour for up to 64 cores at very low memory footprint, or in the presence of GPUs. In these tests, CAF continuously outperforms the competing actor environments Erlang, Charm++, SalsaLite, Scala, ActorFoundry, and even the OpenMPI.Comment: 33 page

Pattern Recognition Software and Techniques for Biological Image Analysis

The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays

Nuclear Modification Factor for Production of Open Heavy Flavor at Forward Rapidity in Cu+Cu Collisions

The PHENIX experiment at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory with its muon spectrometer has the ability to detect muons over the range of pseudorapidity 1.1 \u3c |eta| \u3c 2.25. Single muon production is an important tool for studying heavy flavor production via semi-leptonic decays of open heavy flavor mesons. Because of their large mass, heavy quarks are produced in earlier stages of heavy ion collisions. Therefore, heavy flavor production can serve as an important probe of the Quark Gluon Plasma, a novel state of matter predicted to be created at RHIC. The measurement of the nuclear modification factor of open heavy flavor at forward rapidity in Cu+Cu collisions at sqrt{s_{NN}}=200 GeV is presented. Measurements of heavy flavor production in p+p collisions at sqrt{s_{NN}}=200 GeV will be also presented

CheckMATE 2: From the model to the limit

We present the latest developments to the CheckMATE program that allows models of new physics to be easily tested against the recent LHC data. To achieve this goal, the core of CheckMATE now contains over 60 LHC analyses of which 12 are from the 13 TeV run. The main new feature is that CheckMATE 2 now integrates the Monte Carlo event generation via Madgraph and Pythia 8. This allows users to go directly from a SLHA file or UFO model to the result of whether a model is allowed or not. In addition, the integration of the event generation leads to a significant increase in the speed of the program. Many other improvements have also been made, including the possibility to now combine signal regions to give a total likelihood for a model.Comment: 53 pages, 6 figures; references updated, instructions slightly change

e+e--pair production in Pb-Au collisions at 158 GeV per nucleon

We present the combined results on electron-pair production in 158 GeV/n {Pb-Au} ($\sqrt{s}$= 17.2 GeV) collisions taken at the CERN SPS in 1995 and 1996, and give a detailed account of the data analysis. The enhancement over the reference of neutral meson decays amounts to a factor of 2.31$\pm0.19 (stat.)\pm0.55 (syst.)\pm0.69 (decays)$ for semi-central collisions (28% $\sigma/\sigma_{geo}$) when yields are integrated over $m>$ 200 MeV/$c^2$ in invariant mass. The measured yield, its stronger-than-linear scaling with $N_{ch}$, and the dominance of low pair $p_t$ strongly suggest an interpretation as {\it thermal radiation} from pion annihilation in the hadronic fireball. The shape of the excess centring at $m\approx$ 500 MeV/$c^2$, however, cannot be described without strong medium modifications of the $\rho$ meson. The results are put into perspective by comparison to predictions from Brown-Rho scaling governed by chiral symmetry restoration, and from the spectral-function many-body treatment in which the approach to the phase boundary is less explicit.Comment: 39 pages, 40 figures, to appear in Eur.Phys.J.C. (2005