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| 2. |
- Menkveld, Albert J., et al.
(författare)
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Nonstandard Errors
- 2024
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Ingår i: JOURNAL OF FINANCE. - : Wiley-Blackwell. - 0022-1082 .- 1540-6261. ; 79:3, s. 2339-2390
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Tidskriftsartikel (refereegranskat)abstract
- In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
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| 3. |
- Cheng, Peirui, et al.
(författare)
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Highly Efficient Ruddlesden–Popper Halide Perovskite PA2MA4Pb5I16 Solar Cells
- 2018
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Ingår i: ACS Energy Letters. - : American Chemical Society (ACS). - 2380-8195. ; 3:8, s. 1975-1982
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Tidskriftsartikel (refereegranskat)abstract
- Two-dimensional (2D) Ruddlesden-Popper (RP) organic-inorganic perovskites have emerged as promising candidates for solar cells with technologically relevant stability. Herein, a new RP perovskite, the fifth member («n» = 5) of the (CH3(CH2)2NH3)2(CH3NH3)n-1PbnI3n+1 family (abbreviated as PA2MA4Pb5I16), was synthesized and systematically investigated in terms of photovoltaic application. The obtained pure PA2MA4Pb5I16 crystal exhibits a direct band gap of Eg = 1.85 eV. Systematic analysis on the solid film highlights the key role of the precursor-solvent interaction in the quantum well orientation, phase purity, grain size, surface quality, and optoelectronic properties, which can be well-tuned with addition of dimethyl sulfoxide (DMSO) into the N,N-dimethylformamide (DMF) precursor solution. These findings present opportunities for designing a high-quality RP film with well-controlled quantum well orientation, micrometer-sized grains, and optoelectronic properties. As a result, we achieved power conversion efficiency (PCE) up to 10.41%.
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| 4. |
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| 5. |
- Feng, Zhuo, et al.
(författare)
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Nurse logs: A common seedling strategy in the Permian Cathaysian flora
- 2022
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Ingår i: iScience. - Cambridge, Massachusetts : Elsevier BV. - 2589-0042. ; 25, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Nurse logs are common in modern forests from boreal to temperate and tropical ecosystems. However, the evolution of the nurse-log strategy remains elusive because of their rare occurrence in the fossil record. We report seven coniferous nurse logs from lowermost to uppermost Permian strata of northern China that have been colonized by conifer and sphenophyllalean roots. These roots are associated with two types of arthropod coprolites and fungal remains. Our study provides the first glimpse into plant—plant facilitative relationships between late Paleozoic gymnosperms and sphenopsids. Detritivorous arthropods and fungi appear to have been crucial for the utilization of nurse logs in Permian forests. The phylogenetically distant roots demonstrate that nurse-log interaction wasa sophisticated seedling strategy in late Paleozoic humid tropical forests, and this approach may have been adopted and developed by a succession of plant groups leading to its wide representation in modern forest ecosystems.
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| 6. |
- Huan, Yuxiang, et al.
(författare)
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A 3D Tiled Low Power Accelerator for Convolutional Neural Network
- 2018
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Ingår i: 2018 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS). - : IEEE. - 9781538648810
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Konferensbidrag (refereegranskat)abstract
- It remains a challenge to run Deep Learning in devices with stringent power budget in the Internet-of-Things. This paper presents a low-power accelerator for processing Convolutional Neural Networks on the embedded devices. The power reduction is realized by exploring data reuse in three different aspects, with regards to convolution, filter and input features. A systolic-like data flow is proposed and applied to rows of Processing Elements (PEs), which facilitate reusing the data during convolution. Reuse of input features and filters is achieved by arranging the PE array in a 3D tiled architecture, whose dimension is 3 x 14 x 4. Local storage within PEs is therefore reduced and only cost 17.75 kB, which is 20% of the state-of-the-art. With dedicated delay chains in each PE, this accelerator is reconfigurable to suit various parameter settings of convolutional layers. Evaluated in UMC 65 nm low leakage process, the accelerator can reach a peak performance of 84 GOPS and consume only 136 mW at 250 Mhz.
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| 7. |
- Huang, Boming, et al.
(författare)
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Automated trading systems statistical and machine learning methods and hardware implementation : a survey
- 2019
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Ingår i: Enterprise Information Systems. - : Taylor & Francis. - 1751-7575 .- 1751-7583. ; 13:1, s. 132-144
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Tidskriftsartikel (refereegranskat)abstract
- Automated trading, which is also known as algorithmic trading, is a method of using a predesigned computer program to submit a large number of trading orders to an exchange. It is substantially a real-time decision-making system which is under the scope of Enterprise Information System (EIS). With the rapid development of telecommunication and computer technology, the mechanisms underlying automated trading systems have become increasingly diversified. Considerable effort has been exerted by both academia and trading firms towards mining potential factors that may generate significantly higher profits. In this paper, we review studies on trading systems built using various methods and empirically evaluate the methods by grouping them into three types: technical analyses, textual analyses and high-frequency trading. Then, we evaluate the advantages and disadvantages of each method and assess their future prospects.
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| 8. |
- Huang, Boming, et al.
(författare)
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IECA : An In-Execution Configuration CNN Accelerator With 30.55 GOPS/mm(2) Area Efficiency
- 2021
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Ingår i: IEEE Transactions on Circuits and Systems Part 1. - : Institute of Electrical and Electronics Engineers (IEEE). - 1549-8328 .- 1558-0806. ; 68:11, s. 4672-4685
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Tidskriftsartikel (refereegranskat)abstract
- It remains challenging for a Convolutional Neural Network (CNN) accelerator to maintain high hardware utilization and low processing latency with restricted on-chip memory. This paper presents an In-Execution Configuration Accelerator (IECA) that realizes an efficient control scheme, exploring architectural data reuse, unified in-execution controlling, and pipelined latency hiding to minimize configuration overhead out of the computation scope. The proposed IECA achieves row-wise convolution with tiny distributed buffers and reduces the size of total on-chip memory by removing 40% of redundant memory storage with shared delay chains. By exploiting a reconfigurable Sequence Mapping Table (SMT) and Finite State Machine (FSM) control, the chip realizes cycle-accurate Processing Element (PE) control, automatic loop tiling and latency hiding without extra time slots for pre-configuration. Evaluated on AlexNet and VGG-16, the IECA retains over 97.3% PE utilization and over 95.6% memory access time hiding on average. The chip is designed and fabricated in a UMC 55-nm process running at a frequency of 250 MHz and achieves an area efficiency of 30.55 GOPS/mm(2) and 0.244 GOPS/KGE (kilo-gate-equivalent), which makes an over 2.0x and 2.1x improvement, respectively, compared with that of previous related works. Implementation of the IEC control scheme uses only a 0.55% area of the 2.75 mm(2) core.
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| 9. |
- Jin, Yi, et al.
(författare)
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Self-aware distributed deep learning framework for heterogeneous IoT edge devices
- 2021
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Ingår i: Future generations computer systems. - : Elsevier BV. - 0167-739X .- 1872-7115. ; 125, s. 908-920
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Tidskriftsartikel (refereegranskat)abstract
- Implementing artificial intelligence (AI) in the Internet of Things (IoT) involves a move from the cloud to the heterogeneous and low-power edge, following an urgent demand for deploying complex training tasks in a distributed and reliable manner. This work proposes a self-aware distributed deep learning (DDL) framework for IoT applications, which is applicable to heterogeneous edge devices aiming to improve adaptivity and amortize the training cost. The self-aware design including the dynamic self-organizing approach and the self-healing method enhances the system reliability and resilience. Three typical edge devices are adopted with cross-platform Docker deployment: Personal Computers (PC) for general computing devices, Raspberry Pi 4Bs (Rpi) for resource-constrained edge devices, and Jetson Nanos (Jts) for AI-enabled edge devices. Benchmarked with ResNet-32 on CIFAR-10, the training efficiency of tested distributed clusters is increased by 8.44x compared to the standalone Rpi. The cluster with 11 heterogeneous edge devices achieves a training efficiency of 200.4 images/s and an accuracy of 92.45%. Results prove that the self-organizing approach functions well with dynamic changes like devices being removed or added. The self-healing method is evaluated with various stabilities, cluster scales, and breakdown cases, testifying that the reliability can be largely enhanced for extensively distributed deployments. The proposed DDL framework shows excellent performance for training implementation with heterogeneous edge devices in IoT applications with high-degree scalability and reliability.
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| 10. |
- Li, Biao, et al.
(författare)
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Decoupling the roles of Ni and Co in anionic redox activity of Li-rich NMC cathodes
- 2023
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Ingår i: Nature Materials. - : Springer Nature. - 1476-1122 .- 1476-4660. ; 22:11, s. 1370-1379
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Tidskriftsartikel (refereegranskat)abstract
- Li[LixNiyMnzCo1−x−y−z]O2 (lithium-rich NMCs) are benchmark cathode materials receiving considerable attention due to the abnormally high capacities resulting from their anionic redox chemistry. Although their anionic redox mechanisms have been much investigated, the roles of cationic redox processes remain underexplored, hindering further performance improvement. Here we decoupled the effects of nickel and cobalt in lithium-rich NMCs via a comprehensive study of two typical compounds, Li1.2Ni0.2Mn0.6O2 and Li1.2Co0.4Mn0.4O2. We discovered that both Ni3+/4+ and Co4+, generated during cationic redox processes, are actually intermediate species for triggering oxygen redox through a ligand-to-metal charge-transfer process. However, cobalt is better than nickel in mediating the kinetics of ligand-to-metal charge transfer by favouring more transition metal migration, leading to less cationic redox but more oxygen redox, more O2 release, poorer cycling performance and more severe voltage decay. Our work highlights a compositional optimization pathway for lithium-rich NMCs by deviating from using cobalt to using nickel, providing valuable guidelines for future high-capacity cathode design.
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