Streaming Temporal Graphs: Subgraph Matching

We investigate solutions to subgraph matching within a temporal stream of data. We present a high-level language for describing temporal subgraphs of interest, the Streaming Analytics Language (SAL). SAL programs are translated into C++ code that is run in parallel on a cluster. We call this implementation of SAL the Streaming Analytics Machine (SAM). SAL programs are succinct, requiring about 20 times fewer lines of code than using the SAM library directly, or writing an implementation using Apache Flink. To benchmark SAM we calculate finding temporal triangles within streaming netflow data. Also, we compare SAM to an implementation written for Flink. We find that SAM is able to scale to 128 nodes or 2560 cores, while Apache Flink has max throughput with 32 nodes and degrades thereafter. Apache Flink has an advantage when triangles are rare, with max aggregate throughput for Flink at 32 nodes greater than the max achievable rate of SAM. In our experiments, when triangle occurrence was faster than five per second per node, SAM performed better. Both frameworks may miss results due to latencies in network communication. SAM consistently reported an average of 93.7% of expected results while Flink decreases from 83.7% to 52.1% as we increase to the maximum size of the cluster. Overall, SAM can obtain rates of 91.8 billion netflows per day.Comment: Big Data 201

Functional renormalization group for non-Hermitian and PT-symmetric systems

We generalize the vertex expansion approach of the functional renormalization group to non-Hermitian systems. As certain anomalous expectation values might not vanish, additional terms as compared to the Hermitian case can appear in the flow equations. We investigate the merits and shortcomings of the vertex expansion for non-Hermitian systems by considering an exactly solvable PT-symmetric non-linear toy-model and reveal, that in this model, the fidelity of the vertex expansion in a perturbatively motivated truncation schema is comparable with that of the Hermitian case. The vertex expansion appears to be a viable method for studying correlation effects in non-Hermitian systems

Balancing engagement and neutrality in technology assessment

Should technology assessment take a stance – when, on what, and how? How to deal with its neutrality paradigm in times of anti-democratic tendencies

Can neural quantum states learn volume-law ground states?

We study whether neural quantum states based on multi-layer feed-forward networks can find ground states which exhibit volume-law entanglement entropy. As a testbed, we employ the paradigmatic Sachdev-Ye-Kitaev model. We find that both shallow and deep feed-forward networks require an exponential number of parameters in order to represent the ground state of this model. This demonstrates that sufficiently complicated quantum states, although being physical solutions to relevant models and not pathological cases, can still be difficult to learn to the point of intractability at larger system sizes. This highlights the importance of further investigations into the physical properties of quantum states amenable to an efficient neural representation

The Deformable Mirror Demonstration Mission (DeMi) CubeSat: optomechanical design validation and laboratory calibration

Coronagraphs on future space telescopes will require precise wavefront correction to detect Earth-like exoplanets near their host stars. High-actuator count microelectromechanical system (MEMS) deformable mirrors provide wavefront control with low size, weight, and power. The Deformable Mirror Demonstration Mission (DeMi) payload will demonstrate a 140 actuator MEMS deformable mirror (DM) with \SI{5.5}{\micro\meter} maximum stroke. We present the flight optomechanical design, lab tests of the flight wavefront sensor and wavefront reconstructor, and simulations of closed-loop control of wavefront aberrations. We also present the compact flight DM controller, capable of driving up to 192 actuator channels at 0-250V with 14-bit resolution. Two embedded Raspberry Pi 3 compute modules are used for task management and wavefront reconstruction. The spacecraft is a 6U CubeSat (30 cm x 20 cm x 10 cm) and launch is planned for 2019.Comment: 15 pages, 10 figues. Presented at SPIE Astronomical Telescopes + Instrumentation, Austin, Texas, US