Magnetocardiography (MCG) enables passive detection of weak magnetic fields generated by the heart with high sensitivity, which can offer valuable information for diagnosing and treating heart conditions. Due to the limitations of the geomagnetic field and unknown magnetic interference, the MCG signals are often overwhelmed by high levels of magnetic noise. In this paper, we propose the design of a high-resolution and movable MCG system comprised of an active-passive coupling magnetic control (AP-CMC) system and a wearable multi-channel signal detection array. The system realizes the MCG measurement at the same time as the AP-CMC system eliminates interference in real time, i.e., simultaneous control and simultaneous measurement. Dynamic MCG signal measurements were successfully conducted, obtaining typical characteristic features of MCG signals. Our method shows promise in enhancing the accuracy and expanding the scope of MCG measurement applications, thereby offering valuable insights for the early diagnosis and precise localization of heart diseases.

Long non-coding RNAs (lncRNAs) play a key role in the human brain, and genetic variants regulate their expression. Herein, expression quantitative trait loci of lncRNAs encompassing 10 brain regions from 134 individuals were analyzed, and novel variants influencing lncRNA expression (eSNPs) and respective affected lncRNAs (elncRNAs) were identified. The eSNPs showed proximity to their corresponding elncRNAs, enriched in the non-coding genome, and have a high minor allele frequency. The elncRNAs exhibit a high-level and complex pattern of expression. The genetic regulation is more tissue specific for lncRNAs than for protein-coding genes, with notable differences between cerebrum and cerebellum. Nonetheless, it shows relatively similar patterns across cortex regions. Furthermore, we observed a significant enrichment of eSNPs among variants associated with neurological disorders, especially insomnia, and identified insomnia-related lncRNAs involved in immune response functions. Moreover, the present study offers an improved tool for lncRNA quantification, a novel approach for lncRNA function analysis, and a database of lncRNA expression regulation in human brain. These findings and resources will advance the research on non-coding gene expression regulation in neuroscience.

Ferromagnetic boundary coupling is one key factor that limits the control accuracy of the residual magnetic field in the magnetic shielding room. Usually the image method is utilized to suppress the coupling, but the tolerance for door slit and holes of the image method is poor. Here, we propose the self-shielding method for suppressing the ferromagnetic boundary coupling with higher coupling suppression ability and better tolerance for door slit and holes. Two sets of biplane coils for one axis are designed for achieving uniform magnetic field in the target area and nearzero magnetic field in the shielding area near the passive metal shielding layer simultaneously. We have determined the analysis method and design process of the self-shielding method, and verified its feasibility by experimental test. The proposed method can provide new ideas for improving the control accuracy of the residual magnetic field in the magnetic shielding room, ensuring the smooth progress of magnetoencephalography measurement.

The massive scale of future wireless networks will cause computational bottlenecks in performance optimization. In this paper, we study the problem of connecting mobile traffic to Cloud RAN (C-RAN) stations. To balance station load, we steer the traffic by designing device association rules. The baseline association rule connects each device to the station with the strongest signal, which does not account for interference or traffic hot spots, and leads to load imbalances and performance deterioration. Instead, we can formulate an optimization problem to decide centrally the best association rule at each time instance. However, in practice this optimization has such high dimensions, that even linear programming solvers fail to solve. To address the challenge of massive connectivity, we propose an approach based on the theory of optimal transport, which studies the economical transfer of probability between two distributions. Our proposed methodology can further inspire scalable algorithms for massive optimization problems in wireless networks.

The X10 programming language is organized around the notion of places (an encapsulation of data and activities operating on the data), partitioned global address space (PGAS), and asynchronous computation and communication.

Solving large, irregular graph problems efficiently is challenging. Current software systems and commodity multiprocessors do not support fine-grained, irregular parallelism well. We present XWS, the X10 Work Stealing framework, an open-source runtime for the parallel programming language X10 and a library to be used directly by application writers. XWS extends the Cilk work-stealing framework with several features necessary to efficiently implement graph algorithms, viz., support for improperly nested procedures, global termination detection, and phased computation. We also present a strategy to adaptively control the granularity of parallel tasks in the work-stealing scheme, depending on the instantaneous size of the work queue. We compare the performance of the XWS implementations of spanning tree algorithms with that of the hand-written C and Cilk implementations using various graph inputs. We show that XWS programs (written in Java) scale and exhibit comparable or better performance.

We present an Adaptive Mesh Refinement benchmark for evaluating programmability and performance of modern parallel programming languages. Benchmarks employed today by language developing teams, originally designed for performance evaluation of computer architectures, do not fully capture the complexity of state-of-the-art computational software systems running on today’s parallel machines or to be run on the emerging ones from the multi-cores to the peta-scale High Productivity Computer Systems. This benchmark, extracted from a real application framework, presents challenges for a programming language in both expressiveness and performance. It consists of an infrastructure for finite difference calculations on block-structured adaptive meshes and a solver for elliptic Partial Differential Equations built on this infrastructure. Adaptive Mesh Refinement algorithms are challenging to implement due to the irregularity introduced by local mesh refinement. We describe those challenges posed by this benchmark through two reference implementations (C++/Fortran/MPI and Titanium) and in the context of three programming models. Categories and Subject Descriptors