High Performance Reconfigurable Computing for Linear Algebra: Design and Performance Analysis

Field Programmable Gate Arrays (FPGAs) enable powerful performance acceleration for scientific computations because of their intrinsic parallelism, pipeline ability, and flexible architecture. This dissertation explores the computational power of FPGAs for an important scientific application: linear algebra. First of all, optimized linear algebra subroutines are presented based on enhancements to both algorithms and hardware architectures. Compared to microprocessors, these routines achieve significant speedup. Second, computing with mixed-precision data on FPGAs is proposed for higher performance. Experimental analysis shows that mixed-precision algorithms on FPGAs can achieve the high performance of using lower-precision data while keeping higher-precision accuracy for finding solutions of linear equations. Third, an execution time model is built for reconfigurable computers (RC), which plays an important role in performance analysis and optimal resource utilization of FPGAs. The accuracy and efficiency of parallel computing performance models often depend on mean maximum computations. Despite significant prior work, there have been no sufficient mathematical tools for this important calculation. This work presents an Effective Mean Maximum Approximation method, which is more general, accurate, and efficient than previous methods. Together, these research results help address how to make linear algebra applications perform better on high performance reconfigurable computing architectures

Edge Element Approximation for the Spherical Interface Dynamo System

Exploring the origin and properties of magnetic fields is crucial to the development of many fields such as physics, astronomy and meteorology. We focus on the edge element approximation and theoretical analysis of celestial dynamo system with quasi-vacuum boundary conditions. The system not only ensures that the magnetic field on the spherical shell is generated from the dynamo model, but also provides convenience for the application of the edge element method. We demonstrate the existence, uniqueness and stability of the solution to the system by the fixed point theorem. Then, we approximate the system using the edge element method, which is more efficient in dealing with electromagnetic field problems. Moreover, we also discuss the stability of the corresponding discrete scheme. And the convergence is demonstrated by later numerical tests. Finally, we simulate the three-dimensional time evolution of the spherical interface dynamo model, and the characteristics of the simulated magnetic field are consistent with existing work

Application of Model-Based Time Series Prediction of Infrared Long-Wave Radiation Data for Exploring the Precursory Patterns Associated with the 2021 Madoi Earthquake

Taking the Madoi MS 7.4 earthquake of 21 May 2021 as an example, this paper proposes using time series prediction models to predict the outgoing long-wave radiation (OLR) anomalies and study short-term pre-earthquake signals. Five time series prediction models, including autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM), were trained with the OLR time series data of the aseismic moments in the 5° × 5° spatial range around the epicenter. The model with the highest prediction accuracy was selected to retrospectively predict the OLR values during the aseismic period and before the earthquake in the area. It was found, by comparing the predicted time series values with the actual time series value, that the similarity indexes of the two time series before the earthquake were lower than the index of the aseismic period, indicating that the predicted time series before the earthquake significantly differed from the actual time series. Meanwhile, the temporal and spatial distribution characteristics of the anomalies in the 90 days before the earthquake were analyzed with a 95% confidence interval as the criterion of the anomalies, and the following was found: out of 25 grids, 18 grids showed anomalies—the anomalies of the different grids appeared on similar dates, and the anomalies of high values appeared centrally at the time of the earthquake, which supports the hypothesis that pre-earthquake signals may be associated with the earthquake

Preparation of 3D spherical Ni/Al LDHs with significantly enhanced electrochemical performance as a superior cathode material for Ni/MH batteries.

Nickel-based hydroxides with excellent electrochemical performance have been considered as cathode materials for Ni/MH batteries. In this paper, a Ni/Al layered double hydroxides (Ni/Al LDHs) material with three-dimensional (3D) spherical structure is synthesized by a facile stable dual complexation-precipitation method. SEM images show that the obtained Ni/Al LDHs possess 3D spherical structure composed of nanosheets. XRD and CV tests indicate that doping of Al increases the distance between Ni-Al layers, greatly improving the specific capacity of the obtained materials. The electrochemical tests show that the specific capacity of the obtained material with 18% Al is up to 383.4 mAh g-1 at a current density of 1 A g-1. In addition, when the current density is further increased to 10 and 20 A g-1, the specific capacity of this material still maintains 345.0 mAh g-1 and 307.9 mAh g-1, respectively, which implies that this cathode material can provide remarkable power densities. Moreover, the material composed of Ni/Al LDHs keeps 97.6% initial capacity after 5000 cycles at a current density of 10 A g-1, showing an excellent cycling stability and durability

Aberrant Calcium Signaling in Astrocytes Inhibits Neuronal Excitability in a Human Down Syndrome Stem Cell Model.

Down syndrome (DS) is a genetic disorder that causes cognitive impairment. The staggering effects associated with an extra copy of human chromosome 21 (HSA21) complicates mechanistic understanding of DS pathophysiology. We examined the neuron-astrocyte interplay in a fully recapitulated HSA21 trisomy cellular model differentiated from DS-patient-derived induced pluripotent stem cells (iPSCs). By combining calcium imaging with genetic approaches, we discovered the functional defects of DS astroglia and their effects on neuronal excitability. Compared with control isogenic astroglia, DS astroglia exhibited more-frequent spontaneous calcium fluctuations, which reduced the excitability of co-cultured neurons. Furthermore, suppressed neuronal activity could be rescued by abolishing astrocytic spontaneous calcium activity either chemically by blocking adenosine-mediated signaling or genetically by knockdown of inositol triphosphate (IP3) receptors or S100B, a calcium binding protein coded on HSA21. Our results suggest a mechanism by which DS alters the function of astrocytes, which subsequently disturbs neuronal excitability

Physical Layer Security for the Internet of Things: Authentication and Key Generation

A low-complexity, yet secure framework is proposed for protecting the Internet of Things (IoT) and for achieving both authentication and secure communication. In particular, the slight random difference among transceivers is extracted for creating a unique radio frequency fingerprint and for ascertaining the unique user identity. The wireless channel between any two users is a perfect source of randomness and can be exploited as cryptographic keys. This can be applied to the physical layer of the communications protocol stack. This article reviews these protocols and shows how they can be integrated to provide a complete IoT security framework. We conclude by outlining the future challenges in applying these compelling physical layer security techniques to the IoT.<br/

A green and template-free synthesis process of superior carbon material with ellipsoidal structure as enhanced material for supercapacitors.

Metal Organic Frameworks or related carbon materials are the ideal materials for supercapacitors due to their high surface area and unique porous structure. Here, we propose a new green and recyclable synthesis method of porous carbon. Aluminum hydroxide (Al(OH)₃) and trimesic acid (BTC) are employed as raw materials to obtain aluminium trimesic (denoted as Al-BTC) via their covalent reaction. Then, the porous carbon is obtained through carbonization and dissolving process to remove the aluminum oxide (Al₂O₃). Al(OH)₃ is recovered by the Bayer method for the next batch. The SEM images show that the porous carbon has rugby-like morphology with the same of 400 nm wide and 1000 nm long which indicates the porous carbon with c/a ratio of 2.5 providing the largest specific volume surface area. The sample offers 306.4 F gˉ¹at 1 A gˉ¹, and it can retain 72.2% even at the current density of 50 A gˉ¹. In addition, the porous carbon provides excellent durability of 50,000 cycles at 50 A gˉ¹ with only 5.05% decline of capacitance. Moreover, the porous carbon has an ultrafast charge acceptance, and only 4.4 s is required for one single process, which is promising for application in electric vehicles

Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering

While large language models (LLMs) are equipped with longer text input capabilities than before, they are struggling to seek correct information in long contexts. The "lost in the middle" problem challenges most LLMs, referring to the dramatic decline in accuracy when correct information is located in the middle. To overcome this crucial issue, this paper proposes to enhance the information searching and reflection ability of LLMs in long contexts via specially designed tasks called Attention Strengthening Multi-doc QA (ASM QA). Following these tasks, our model excels in focusing more precisely on the desired information. Experimental results show substantial improvement in Multi-doc QA and other benchmarks, superior to state-of-the-art models by 13.7% absolute gain in shuffled settings, by 21.5% in passage retrieval task. We release our model, Ziya-Reader to promote related research in the community