Search

We present BitConduite, a visual analytics approach for explorative analysis of financial activity within the Bitcoin network, offering a view on transactions aggregated by entities, i.e., by individuals, companies, or other groups actively using Bitcoin. BitConduite makes Bitcoin data accessible to nontechnical experts through a guided workflow around entities analyzed according to several activity metrics. Analyses can be conducted at different scales, from large groups of entities down to single entities. BitConduite also enables analysts to cluster entities to identify groups of similar activities as well as to explore characteristics and temporal patterns of transactions. To assess the value of our approach, we collected feedback from domain experts. [ABSTRACT FROM AUTHOR]

A primary objective of a distributed storage system is to reliably store huge amounts of source data for long periods of time using a large number of high storage capacity nodes. Storage nodes are prone to permanent failures, where such node failures cause all stored data to be permanently lost, and failed nodes are replaced with nodes that initially store no data. To maintain recoverability of the source data as storage nodes fail and are replaced, a total amount of storage capacity that is larger than the source data size is allocated to store the source data, and a repairer continually reads data from and writes data to the storage nodes as they fail and are replaced. We prove information-theoretic lower bounds on the rate at which a repairer reads data from the storage system as a function of the rate at which nodes fail and the amount by which the allocated storage capacity exceeds the source data size. These lower bounds hold for any repairer, i.e., any repairer that does not read data at or above the lower bound rate will provably not maintain recoverability of the source data. The bounds are provably tight asymptotically as the number of storage nodes grows and the allocated storage capacity approaches the source data size. [ABSTRACT FROM AUTHOR]

The charging/discharging delays in superconducting coils wound without insulation (NI coils) are a major drawback of the technique. While removing the insulation improves safety margins, the increase in the characteristic time constant τc can make a coil unfit for a particular purpose. It is widely accepted for instance that NI coils will not be used in ac applications where τc ~ 1/f. To decrease τc of the NI coils, the same length of superconductor can be wound/connected in parallel rather than in series — decreasing the inductance L, and hence the time constant τc, while maintaining the number of amp-turns IopN. Here we investigate the effect of parallel connecting coils in a magnet using a 2D axially symmetric model which captures all the necessary electromagnetic properties of the HTS NI coils. These properties include: critical current anisotropy Jc(B,θ), turn-to-turn conductivity, as well as winding parallelism. Our modeling results show that the parallel connected magnet experiences magnet-wide shielding current effects. Whilst these shielding currents affect field homogeneity — the model enables this effect to be quantified. Furthermore, shielding currents are not an issue when running NI coils in saturated mode. The modeling work presented here provides a simple initial example of how magnet designers may approach designing, optimizing, and operating high current, HTS NI coils. [ABSTRACT FROM AUTHOR]

With increasingly complex neural network architectures and heterogeneous device characteristics, finding a reasonable graph partitioning and device placement strategy is challenging. There have been prior attempts at learned approaches for solving device placement, these approaches are computationally expensive, unable to handle large graphs consisting over 50000 nodes, and do not generalize well to unseen graphs. To address all these limitations, we propose an efficient single-shot, generalized deep RL method (SGDP) based on a scalable sequential attention mechanism over a graph neural network that is transferable to new graphs. On a diverse set of representative deep learning models, our method on average achieves 20% improvement over human placement and 18% improvement over the prior art with 15× faster convergence. We are the first to demonstrate super human performance on 8-layer recurrent neural network language model and 8-layer GNMT consisting of over 50000 nodes, on 8-GPUs. We provide rationales and sensitivity study on model architecture selections. [ABSTRACT FROM AUTHOR]

Let $\mathbb {F}_{q}$ be the finite field of $q$ elements. In this paper we obtain bounds on the following counting problem: given a polynomial $f(x)\in \mathbb {F}_{q} [x]$ of degree $k+m$ and a non-negative integer $r$ , count the number of polynomials $g(x)\in \mathbb {F}_{q} [x]$ of degree at most $k-1$ such that $f(x)+g(x)$ has exactly $r$ roots in $\mathbb {F}_{q}$. Previously, explicit formulas were known only for the cases $m=0, 1, 2$. As an application, we obtain an asymptotic formula on the list size of the standard Reed-Solomon code $[q, k, q-k+1]_{q}$. [ABSTRACT FROM AUTHOR]

Interactive model steering helps people incrementally build machine learning models that are tailored to their domain and task. Existing visual analytic tools allow people to steer a single model (e.g., assignment attribute weights used by a dimension reduction model). However, the choice of model is critical in such situations. What if the model chosen is suboptimal for the task, dataset, or question being asked? What if instead of parameterizing and steering this model, a different model provides a better fit? This paper presents a technique to allow users to inspect and steer multiple machine learning models. The technique steers and samples models from a broader set of learning algorithms and model types. We incorporate this technique into a visual analytic prototype, BEAMES, that allows users to perform regression tasks via multimodel steering. This paper demonstrates the effectiveness of BEAMES via a use case, and discusses broader implications for multimodel steering. [ABSTRACT FROM AUTHOR]

We experimentally study the pulse generation and the pulse train timing and amplitude stability of a monolithic passively mode-locked multisection quantum-well semiconductor laser. The laser emits a stable optical pulse train at a wavelength of 1070 nm and at a fundamental repetition rate of 20 GHz. Timing jitter and amplitude jitter analysis allows identifying distinct laser operating regimes of stable picosecond optical pulses. We obtain an ultra-low pulse-to-pulse timing jitter of 22 fs and find emission regimes which are unaffected by amplitude jitter. The lowest pulse width amounts to 2.6 ps, and the highest pulse peak power is estimated to be 556 mW at a low timing jitter of 170 fs and a negligible relative amplitude jitter of 0.05. [ABSTRACT FROM AUTHOR]