Dynamics at the crystal-melt interface in a supercooled chalcogenide liquid near the glass transition.

Direct quantitative measurements of nanoscale dynamical processes associated with structural relaxation and crystallization near the glass transition are a major experimental challenge. These type of processes have been primarily treated as macroscopic phenomena within the framework of phenomenological models and bulk experiments. Here, we report x-ray photon correlation spectroscopy measurements of dynamics at the crystal-melt interface during the radiation induced formation of Se nano-crystallites in pure Se and in binary AsSe4 glass-forming liquids near their glass transition temperature. We observe a heterogeneous dynamical behaviour where the intensity correlation functions g2(q, t) exhibits either a compressed or a stretched exponential decay, depending on the size of the Se nano-crystallites. The corresponding relaxation timescale for the AsSe4 liquid increases as the temperature is raised, which can be attributed to changes in the chemical composition of the melt at the crystal-melt interface with the growth of the Se nano-crystallites

Node Injection for Class-specific Network Poisoning

Graph Neural Networks (GNNs) are powerful in learning rich network representations that aid the performance of downstream tasks. However, recent studies showed that GNNs are vulnerable to adversarial attacks involving node injection and network perturbation. Among these, node injection attacks are more practical as they don't require manipulation in the existing network and can be performed more realistically. In this paper, we propose a novel problem statement - a class-specific poison attack on graphs in which the attacker aims to misclassify specific nodes in the target class into a different class using node injection. Additionally, nodes are injected in such a way that they camouflage as benign nodes. We propose NICKI, a novel attacking strategy that utilizes an optimization-based approach to sabotage the performance of GNN-based node classifiers. NICKI works in two phases - it first learns the node representation and then generates the features and edges of the injected nodes. Extensive experiments and ablation studies on four benchmark networks show that NICKI is consistently better than four baseline attacking strategies for misclassifying nodes in the target class. We also show that the injected nodes are properly camouflaged as benign, thus making the poisoned graph indistinguishable from its clean version w.r.t various topological properties.Comment: 28 pages, 5 figure

BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations

Objective: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a single acceleration (or homogeneous) platform to effectively address the complete array of modeling requirements. Approach: In this paper we propose and build BrainFrame, a heterogeneous acceleration platform, incorporating three distinct acceleration technologies, a Dataflow Engine, a Xeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different instances of a state-of-the-art neuron model, modeling the Inferior- Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal- network dimensions but also different network-connectivity circumstances that can drastically change application workload characteristics. Main results: The synthetic approach of three HPC technologies demonstrated that BrainFrame is better able to cope with the modeling diversity encountered. Our performance analysis shows clearly that the model directly affect performance and all three technologies are required to cope with all the model use cases.Comment: 16 pages, 18 figures, 5 table

Domain fluctuations in a ferroelectric low-strain BaTiO3 thin film

A ferroelectric BaTiO3 thin film grown on a NdScO3 substrate was studied using x-ray photon correlation spectroscopy (XPCS) to characterize thermal fluctuations near the a/b to a/c domain structure transformation present in this low-strain material, which is absent in the bulk. XPCS studies provide a direct comparison of the role of domain fluctuations in first- and second-order phase transformations. The a/b to a/c domain transformation is accompanied by a decrease in fluctuation timescales, and an increase in intensity and correlation length. Surprisingly, domain fluctuations are observed up to 25 degrees C above the transformation, concomitant with the growth of a/c domains and coexistence of both domain types. After a small window of stability, as the Curie temperature is approached, a/c domain fluctuations are observed, albeit slower, potentially due to the structural transformation associated with the ferroelectric to paraelectric transformation. The observed time evolution and reconfiguration of domain patterns highlight the role played by phase coexistence and elastic boundary conditions in altering fluctuation timescales in ferroelectric thin films

BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations

Objective. The advent of high-performance computing (HPC) in recent years has led to its increasing use in brain studies through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. Approach. In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU

Evidence of extreme domain wall speeds under ultrafast optical excitation

Time-resolved ultrafast EUV magnetic scattering was used to test a recent prediction of >10 km/s domain wall speeds by optically exciting a magnetic sample with a nanoscale labyrinthine domain pattern. Ultrafast distortion of the diffraction pattern was observed at markedly different timescales compared to the magnetization quenching. The diffraction pattern distortion shows a threshold-dependence with laser fluence, not seen for magnetization quenching, consistent with a picture of domain wall motion with pinning sites. Supported by simulations, we show that a speed of $\approx$ 66 km/s for highly curved domain walls can explain the experimental data. While our data agree with the prediction of extreme, non-equilibrium wall speeds locally, it differs from the details of the theory, suggesting that additional mechanisms are required to fully understand these effects.Comment: 5 pages, 4 figures; Supplemental Material: 8 pages, 9 figure

Symmetry-dependent ultrafast manipulation of nanoscale magnetic domains

Femtosecond optical pumping of magnetic materials has been used to achieve ultrafast switching and recently to nucleate symmetry-broken magnetic states. However, when the magnetic order parameter already presents a broken-symmetry state, such as a domain pattern, the dynamics are poorly understood and consensus remains elusive. Here, we resolve the controversies in the literature by studying the ultrafast response of magnetic domain patterns with varying degrees of translation symmetry with ultrafast x-ray resonant scattering. A data analysis technique is introduced to disentangle the isotropic and anisotropic components of the x-ray scattering. We find that the scattered intensity exhibits a radial shift restricted to the isotropic component, indicating that the far-from-equilibrium magnetization dynamics are intrinsically related to the spatial features of the domain pattern. Our results suggest alternative pathways for the spatiotemporal manipulation of magnetism via far-from-equilibrium dynamics and by carefully tuning the ground-state magnetic textures

Chronic tendoachilles rupture

We report two cases of chronic tendoachilles (TA) rupture, which was treated with V-Y plasty and turned down flap from the proximal segment to cover the defect. Chronic TA ruptures can be challenging to treat. A number of operations have been described for the repair and augmentation of the chronic TA rupture

The bystander effect in rats

To investigate whether the classic bystander effect is unique to humans, the effect of bystanders on rat helping was studied. In the presence of rats rendered incompetent to help through pharmacological treatment, rats were less likely to help due to a reduction in reinforcement rather than to a lack of initial interest. Only incompetent helpers of a strain familiar to the helper rat exerted a detrimental effect on helping; rats helped at near control levels in the presence of incompetent helpers from an unfamiliar strain. Duos and trios of potential helper rats helped at superadditive rates, demonstrating that rats act nonindependently with helping facilitated by the presence of competent-to-help bystanders. Furthermore, helping was facilitated in rats that had previously observed other rats' helping and were then tested individually. In sum, the influence of bystanders on helping behavior in rats features characteristics that closely resemble those observed in humans