An automatic input-sensitive approach for heterogeneous task partitioning

Unleashing the full potential of heterogeneous systems, consisting of multi-core CPUs and GPUs, is a challenging task due to the difference in processing capabilities, memory availability, and communication latencies of different computational resources. In this paper we propose a novel approach that automatically optimizes task partitioning for different (input) problem sizes and different heterogeneous multi-core architectures. We use the Insieme source-to-source compiler to translate a single-device OpenCL program into a multi-device OpenCL program. The Insieme Runtime System then performs dynamic task partitioning based on an offline-generated prediction model. In order to derive the prediction model, we use a machine learning approach based on Artificial Neural Networks (ANN) that incorporates static program features as well as dynamic, input sensitive features. Principal component analysis have been used to further improve the task partitioning. Our approach has been evaluated over a suite of 23 programs and respectively achieves a performance improvement of 22% and 25% compared to an execution of the benchmarks on a single CPU and a single GPU which is equal to 87.5% of the optimal performance

Automatic Data Layout Optimizations for GPUs

Memory optimizations have became increasingly important in order to fully exploit the computational power of modern GPUs. The data arrangement has a big impact on the performance, and it is very hard for GPU programmers to identify a well-suited data layout. Classical data layout transformations include grouping together data fields that have similar access patterns, or transforming Array-of-Structures (AoS) to Structure-of-Arrays (SoA). This paper presents an optimization infrastructure to automatically determine an improved data layout for OpenCL programs written in AoS layout. Our framework consists of two separate algorithms: The first one constructs a graph-based model, which is used to split the AoS input struct into several clusters of fields, based on hardware dependent parameters. The second algorithm selects a good per-cluster data layout (e.g., SoA, AoS or an intermediate layout) using a decision tree. Results show that the combination of both algorithms is able to deliver higher performance than the individual algorithms. The layouts proposed by our framework result in speedups of up to 2.22, 1.89 and 2.83 on an AMD FirePro S9000, NVIDIA GeForce GTX 480 and NVIDIA Tesla k20m, respectively, over different AoS sample programs, and up to 1.18 over a manually optimized program

Experimental quantum teleportation over a high-loss free-space channel

We present a high-fidelity quantum teleportation experiment over a high-loss free-space channel between two laboratories. We teleported six states of three mutually unbiased bases and obtained an average state fidelity of 0.82(1), well beyond the classical limit of 2/3. With the obtained data, we tomographically reconstructed the process matrices of quantum teleportation. The free-space channel attenuation of 31 dB corresponds to the estimated attenuation regime for a down-link from a low-earth-orbit satellite to a ground station. We also discussed various important technical issues for future experiments, including the dark counts of single-photon detectors, coincidence-window width etc. Our experiment tested the limit of performing quantum teleportation with state-of-the-art resources. It is an important step towards future satellite-based quantum teleportation and paves the way for establishing a worldwide quantum communication network

Blip10000: a social video dataset containing SPUG content for tagging and retrieval

The increasing amount of digital multimedia content available is inspiring potential new types of user interaction with video data. Users want to easilyfind the content by searching and browsing. For this reason, techniques are needed that allow automatic categorisation, searching the content and linking to related information. In this work, we present a dataset that contains comprehensive semi-professional user generated (SPUG) content, including audiovisual content, user-contributed metadata, automatic speech recognition transcripts, automatic shot boundary les, and social information for multiple `social levels'. We describe the principal characteristics of this dataset and present results that have been achieved on different tasks

Quantum erasure with causally disconnected choice

The counterintuitive features of quantum physics challenge many common-sense assumptions. In an interferometric quantum eraser experiment, one can actively choose whether or not to erase which-path information, a particle feature, of one quantum system and thus observe its wave feature via interference or not by performing a suitable measurement on a distant quantum system entangled with it. In all experiments performed to date, this choice took place either in the past or, in some delayed-choice arrangements, in the future of the interference. Thus in principle, physical communications between choice and interference were not excluded. Here we report a quantum eraser experiment, in which by enforcing Einstein locality no such communication is possible. This is achieved by independent active choices, which are space-like separated from the interference. Our setup employs hybrid path-polarization entangled photon pairs which are distributed over an optical fiber link of 55 m in one experiment, or over a free-space link of 144 km in another. No naive realistic picture is compatible with our results because whether a quantum could be seen as showing particle- or wave-like behavior would depend on a causally disconnected choice. It is therefore suggestive to abandon such pictures altogether

Quantum Optical Experiments Modeled by Long Short-Term Memory

We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states. In this work, we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search but is also an essential step towards automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.Comment: 9 page

Bell violation with entangled photons, free of the fair-sampling assumption

The violation of a Bell inequality is an experimental observation that forces one to abandon a local realistic worldview, namely, one in which physical properties are (probabilistically) defined prior to and independent of measurement and no physical influence can propagate faster than the speed of light. All such experimental violations require additional assumptions depending on their specific construction making them vulnerable to so-called "loopholes." Here, we use photons and high-efficiency superconducting detectors to violate a Bell inequality closing the fair-sampling loophole, i.e. without assuming that the sample of measured photons accurately represents the entire ensemble. Additionally, we demonstrate that our setup can realize one-sided device-independent quantum key distribution on both sides. This represents a significant advance relevant to both fundamental tests and promising quantum applications

An Extremely Rare Congenital Muscle Bundle Crossing the Right Atrial Cavity

Muscle bundles in the right atrium are an extremely rare congenital anomaly. We report the case of a patient with 2 atrial septal defects and a large muscle bundle crossing the right atrium. Only 3 comparable cases have previously been published. (Level of Difficulty: Intermediate.)

A magnet attached to the forehead disrupts magnetic compass orientation in a migratory songbird

For studies on magnetic compass orientation and navigation performance in small bird species, controlled experiments with orientation cages inside an electromagnetic coil system are the most prominent methodological paradigm. These are, however, not applicable when studying larger bird species and/or orientation behaviour during free flight. For this, researchers have followed a very different approach. By attaching small magnets to birds, they intended to deprive them of access to meaningful magnetic information. Unfortunately, results from studies using this approach appear rather inconsistent. As these are based on experiments with birds under free flight conditions, which usually do not allow exclusion of other potential orientation cues, an assessment of the overall efficacy of this approach is difficult to conduct. Here, we directly test the efficacy of small magnets for temporarily disrupting magnetic compass orientation in small migratory songbirds using orientation cages under controlled experimental conditions. We found that birds which have access to the Earth’s magnetic field as their sole orientation cue show a general orientation towards their seasonally appropriate migratory direction. When carrying magnets on their forehead under these conditions, the same birds become disoriented. However, under changed conditions that allow birds access to other (i.e. celestial) orientation cues, any disruptive effect of the magnets they carry appears obscured. Our results provide clear evidence for the efficacy of the magnet approach for temporarily disrupting magnetic compass orientation in birds, but also reveal its limitations for application in experiments under free flight conditions

Protein Kinase C θ Affects Ca2+ Mobilization and NFAT Activation in Primary Mouse T Cells

Protein kinase C (PKC)θ is an established component of the immunological synapse and has been implicated in the control of AP-1 and NF-κB. To study the physiological function of PKCθ, we used gene targeting to generate a PKCθ null allele in mice. Consistently, interleukin 2 production and T cell proliferative responses were strongly reduced in PKCθ-deficient T cells. Surprisingly, however, we demonstrate that after CD3/CD28 engagement, deficiency of PKCθ primarily abrogates NFAT transactivation. In contrast, NF-κB activation was only partially reduced. This NFAT transactivation defect appears to be secondary to reduced inositol 1,4,5-trisphosphate generation and intracellular Ca2+ mobilization. Our finding suggests that PKCθ plays a critical and nonredundant role in T cell receptor–induced NFAT activation