Real-time Data Flow Control for CBM-TOF Super Module Quality Evaluation

Super module assembled with MRPC detectors is the component unit of TOF (Time of Flight) system for the Compressed Baryonic Matter (CBM) experiment. Quality of super modules needs to be evaluated before it is applied in CBM-TOF. Time signals exported from super module are digitalized at TDC (Time to Digital Converter) station. Data rate is up to 6 Gbps at each TDC station, which brings a tremendous pressure for data transmission in real time. In this paper, a real-time data flow control method is designed. In this control method, data flow is divided into 3 types: scientific data flow, status data flow and control data flow. In scientific data flow, data of each TDC station is divided into 4 sub-flows, and then is read out by a parallel and hierarchical network, which consists of multiple readout mother boards and daughter boards groups. In status data flow, status data is aggregated into a specific readout mother board. Then it is uploaded to DAQ via readout daughter board. In control data flow, control data is downloaded to all circuit modules in the opposite direction of status data flow. Preliminary test result indicated data of STS was correctly transmitted to DAQ with no error and three type data flows were control orderly in real time. This data flow control method can meet the quality evaluation requirement of supper module in CBM-TOF

Statistical inference of body representation in the macaque brain

The sense of one’s own body is a pillar of self-consciousness and could be investigated by inducing human illusions of artificial objects as part of the self. Here, we present a nonhuman primate version of a rubber-hand illusion that allowed us to determine its computational and neuronal mechanisms. We implemented a video-based system in a reaching task in monkeys and combined a casual inference model to establish an objective and quantitative signature for the monkey’s body representation. Similar to humans, monkeys were more likely to perceive an external object as part of the self when the dynamics (spatial disparity) and the features (shape and structure) of visual (V) input was closer to proprioceptive (P) signals. Neural signals in the monkey’s premotor cortex reflected the strength of illusion and the likelihood of misattributing the illusory hand to oneself, thus, revealing a cortical representation of body ownership.Fil: Fang, Wen. Chinese Academy of Sciences; República de ChinaFil: Li, Junru. Chinese Academy of Sciences; República de ChinaFil: Qi, Guangyao. Chinese Academy of Sciences; República de ChinaFil: Li, Shenghao. Chinese Academy of Sciences; República de ChinaFil: Sigman, Mariano. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Wang, Liping. Chinese Academy of Sciences; República de China. Shanghai Research Center For Brain Science And Brain-inspired Intelligence; Chin

Spin-Orbit Coupling and Spin Textures in Optical Superlattices

We proposed and demonstrated a new approach for realizing spin orbit coupling with ultracold atoms. We use orbital levels in a double well potential as pseudospin states. Two-photon Raman transitions between left and right wells induce spin-orbit coupling. This scheme does not require near resonant light, features adjustable interactions by shaping the double well potential, and does not depend on special properties of the atoms. A pseudospinor Bose-Einstein condensate spontaneously acquires an antiferromagnetic pseudospin texture which breaks the lattice symmetry similar to a supersolid

MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals

Brain signals are important quantitative data for understanding physiological activities and diseases of human brain. Most existing studies pay attention to supervised learning methods, which, however, require high-cost clinical labels. In addition, the huge difference in the clinical patterns of brain signals measured by invasive (e.g., SEEG) and non-invasive (e.g., EEG) methods leads to the lack of a unified method. To handle the above issues, we propose to study the self-supervised learning (SSL) framework for brain signals that can be applied to pre-train either SEEG or EEG data. Intuitively, brain signals, generated by the firing of neurons, are transmitted among different connecting structures in human brain. Inspired by this, we propose MBrain to learn implicit spatial and temporal correlations between different channels (i.e., contacts of the electrode, corresponding to different brain areas) as the cornerstone for uniformly modeling different types of brain signals. Specifically, we represent the spatial correlation by a graph structure, which is built with proposed multi-channel CPC. We theoretically prove that optimizing the goal of multi-channel CPC can lead to a better predictive representation and apply the instantaneou-time-shift prediction task based on it. Then we capture the temporal correlation by designing the delayed-time-shift prediction task. Finally, replace-discriminative-learning task is proposed to preserve the characteristics of each channel. Extensive experiments of seizure detection on both EEG and SEEG large-scale real-world datasets demonstrate that our model outperforms several state-of-the-art time series SSL and unsupervised models, and has the ability to be deployed to clinical practice

CHIME : Cross-passage hierarchical memory network for generative review question answering

We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation. It extends XLNet introducing an auxiliary memory module consisting of two components: the context memory collecting cross-passage evidences, and the answer memory working as a buffer continually refining the generated answers. Empirically, we show the efficacy of the proposed architecture in the multi-passage generative QA, outperforming the state-of-the-art baselines with better syntactically well-formed answers and increased precision in addressing the questions of the AmazonQA review dataset. An additional qualitative analysis revealed the interpretability introduced by the memory module