Write-limited sorts and joins for persistent memory

To mitigate the impact of the widening gap between the memory needs of CPUs and what standard memory technology can deliver, system architects have introduced a new class of memory technology termed persistent memory. Persistent memory is byteaddressable, but exhibits asymmetric I/O: writes are typically one order of magnitude more expensive than reads. Byte addressability combined with I/O asymmetry render the performance profile of persistent memory unique. Thus, it becomes imperative to find new ways to seamlessly incorporate it into database systems. We do so in the context of query processing. We focus on the fundamental operations of sort and join processing. We introduce the notion of write-limited algorithms that effectively minimize the I/O cost. We give a high-level API that enables the system to dynamically optimize the workflow of the algorithms; or, alternatively, allows the developer to tune the write profile of the algorithms. We present four different techniques to incorporate persistent memory into the database processing stack in light of this API. We have implemented and extensively evaluated all our proposals. Our results show that the algorithms deliver on their promise of I/O-minimality and tunable performance. We showcase the merits and deficiencies of each implementation technique, thus taking a solid first step towards incorporating persistent memory into query processing. 1

Universality of dispersive spin-resonance mode in superconducting BaFe2As2

Spin fluctuations in superconducting BaFe2(As1-xPx)2 (x=0.34, Tc = 29.5 K) are studied using inelastic neutron scattering. Well-defined commensurate magnetic signals are observed at ({\pi},0), which is consistent with the nesting vector of the Fermi surface. Antiferromagnetic (AFM) spin fluctuations in the normal state exhibit a three-dimensional character reminiscent of the AFM order in nondoped BaFe2As2. A clear spin gap is observed in the superconducting phase forming a peak whose energy is significantly dispersed along the c-axis. The bandwidth of dispersion becomes larger with approaching the AFM ordered phase universally in all superconducting BaFe2As2, indicating that the dispersive feature is attributed to three-dimensional AFM correlations. The results suggest a strong relationship between the magnetism and superconductivity.Comment: 5 pages, 5 figure

Faradaic Rectification Studies on Tl+-TP+ Redox Couple at the Platinum Electrode Interface

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AnyPose: Anytime 3D Human Pose Forecasting via Neural Ordinary Differential Equations

Anytime 3D human pose forecasting is crucial to synchronous real-world human-machine interaction, where the term ``anytime" corresponds to predicting human pose at any real-valued time step. However, to the best of our knowledge, all the existing methods in human pose forecasting perform predictions at preset, discrete time intervals. Therefore, we introduce AnyPose, a lightweight continuous-time neural architecture that models human behavior dynamics with neural ordinary differential equations. We validate our framework on the Human3.6M, AMASS, and 3DPW dataset and conduct a series of comprehensive analyses towards comparison with existing methods and the intersection of human pose and neural ordinary differential equations. Our results demonstrate that AnyPose exhibits high-performance accuracy in predicting future poses and takes significantly lower computational time than traditional methods in solving anytime prediction tasks