Fine motor control in using pen for writing and copying: in the impaired and healthy brain

The central issue of the dissertation is to investigate the neural-cognitive basis of writing and copying figures focusing on fine motor abilities. The neuronal recycling hypothesis is used as the theoretical framework, assuming that the ability to use pen emerged from other closely related cognitive abilities. The thesis contained four independent studies with either ischemic stroke patients or healthy participants. Chapter 2 describe the general methods used in our study. Chapter 3 is a neuropsychological study that utilizes principle component analysis and voxel-based morphometry. It explores the neural-cognitive basis underlying complex figure copying (CFC). It demonstrates the involvement of different processing stages that supports figure copying along the dorsal pathway, from visual through eye-hand coordination to the motor associative cortex. Chapters 4-6 focus on writing abilities, across two different systems: phonological and logographic. Chapter 4, is a neuropsychological study that utilized machine learning to explore the latent relationship between writing with other cognitive tasks in English and Chinese. Across the two-writing systems impairment in writing skills could be reliably classified using the same features. These cognitive features were related to CFC, attention, reading, memory and age. Chapter 5 presents two neuropsychological studies that examine the neuro-cognitive makeup of the ability to write words (phonological) and numbers (logographic). The first study is a detail comorbidity analysis of writing deficits of words, numbers, language and motor deficits. It demonstrates that pure writing deficits are very rare, with the majority of writing deficits overlapping with motor (CFC) or language impairments. The second study in this chapter is a VBM study focus on writing numbers and words. We identified two dissociable networks that have been specifically evolved to support writing: a visual-manual motor ability to use pen mediated by right angular and middle frontal gyri; and an ability to transform symbolic representations grapheme to manual programs for use with the pen. Chapter 6 is an fMRI study with healthy participants investigating the neural substrates associated with writing English, Chinese and Pinyin. The study identifies different brain networks that support writing abilities across writing systems: visual information perception and visual motor transformation, semantic component. Chapter 7, summarize and compare the main finding of the four studies. Overall, the studies demonstrate the close relations between the sue of pen and other more basic cognitive functions, such as control of hand movement, language, attention. As predicted by the neuronal recycling hypothesis there were minimal pure deficits of writing or copying; and for proficient writers, the same neural structures supported different writing systems

SGLD-Based Information Criteria and the Over-Parameterized Regime

Double-descent refers to the unexpected drop in test loss of a learning algorithm beyond an interpolating threshold with over-parameterization, which is not predicted by information criteria in their classical forms due to the limitations in the standard asymptotic approach. We update these analyses using the information risk minimization framework and provide Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for models learned by stochastic gradient Langevin dynamics (SGLD). Notably, the AIC and BIC penalty terms for SGLD correspond to specific information measures, i.e., symmetrized KL information and KL divergence. We extend this information-theoretic analysis to over-parameterized models by characterizing the SGLD-based BIC for the random feature model in the regime where the number of parameters $p$ and the number of samples $n$ tend to infinity, with $p/n$ fixed. Our experiments demonstrate that the refined SGLD-based BIC can track the double-descent curve, providing meaningful guidance for model selection and revealing new insights into the behavior of SGLD learning algorithms in the over-parameterized regime

Histogram-Based Flash Channel Estimation

Current generation Flash devices experience significant read-channel degradation from damage to the oxide layer during program and erase operations. Information about the read-channel degradation drives advanced signal processing methods in Flash to mitigate its effect. In this context, channel estimation must be ongoing since channel degradation evolves over time and as a function of the number of program/erase (P/E) cycles. This paper proposes a framework for ongoing model-based channel estimation using limited channel measurements (reads). This paper uses a channel model characterizing degradation resulting from retention time and the amount of charge programmed and erased. For channel histogram measurements, bin selection to achieve approximately equal-probability bins yields a good approximation to the original distribution using only ten bins (i.e. nine reads). With the channel model and binning strategy in place, this paper explores candidate numerical least squares algorithms and ultimately demonstrates the effectiveness of the Levenberg-Marquardt algorithm which provides both speed and accuracy.Comment: 6 pages, 8 figures, Submitted to the IEEE International Communications Conference (ICC) 201

Synthesis, Characterization, and Tribological Behavior of Oleic Acid Capped Graphene Oxide

Graphene oxide (GO) nanosheets were prepared by modified Hummers and Offeman methods. Furthermore, oleic acid (OA) capped graphene oxide (OACGO) nanosheets were prepared and characterized by means of Fourier transform-infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), and X-ray diffraction (XRD). At the same time, the friction and wear properties of OA capped graphite powder (OACG), OACGO, and oleic acid capped precipitate of graphite (OACPG) as additives in poly-alpha-olefin (PAO) were compared using four-ball tester and SRV-1 reciprocating ball-on-disc friction and wear tester. By the addition of OACGO to PAO, the antiwear ability was improved and the friction coefficient was decreased. Also, the tribological mechanism of the GO was investigated

New Technology and Experimental Study on Snow-Melting Heated Pavement System in Tunnel Portal

In recent years, with the rapid growth of economy and sharp rise of motor vehicles in China, the pavement skid resistance in tunnel portals has become increasingly important in cold region. However, the deicing salt, snow removal with machine, and other antiskid measures adopted by highway maintenance division have many limitations. To improve the treatment effect, we proposed a new snow-melting approach employing electric heat tracing, in which heating cables are installed in the structural layer of road. Through the field experiment, laboratory experiment, and numerical investigation, structure type, heating power, and preheating time of the flexible pavement heating system in tunnel portal were systematically analyzed, and advantages of electric heat tracing technology in improving the pavement skid resistance in tunnel portal were also presented. Therefore, such new technology, which offers new snow-melting methods for tunnel portal, bridge, mountainous area, and large longitudinal slope in cold region, has promising prospect for extensive application

Global-Local Stepwise Generative Network for Ultra High-Resolution Image Restoration

While the research on image background restoration from regular size of degraded images has achieved remarkable progress, restoring ultra high-resolution (e.g., 4K) images remains an extremely challenging task due to the explosion of computational complexity and memory usage, as well as the deficiency of annotated data. In this paper we present a novel model for ultra high-resolution image restoration, referred to as the Global-Local Stepwise Generative Network (GLSGN), which employs a stepwise restoring strategy involving four restoring pathways: three local pathways and one global pathway. The local pathways focus on conducting image restoration in a fine-grained manner over local but high-resolution image patches, while the global pathway performs image restoration coarsely on the scale-down but intact image to provide cues for the local pathways in a global view including semantics and noise patterns. To smooth the mutual collaboration between these four pathways, our GLSGN is designed to ensure the inter-pathway consistency in four aspects in terms of low-level content, perceptual attention, restoring intensity and high-level semantics, respectively. As another major contribution of this work, we also introduce the first ultra high-resolution dataset to date for both reflection removal and rain streak removal, comprising 4,670 real-world and synthetic images. Extensive experiments across three typical tasks for image background restoration, including image reflection removal, image rain streak removal and image dehazing, show that our GLSGN consistently outperforms state-of-the-art methods.Comment: submmitted to Transactions on Image Processin

Determination of grounding line on the Amery Ice Shelf using Sentinel-1 radar interferometry data

Delineation of the grounding line (GL) is necessary for calculating the mass balance of Antarctica, but GL measurements for most of the continent remain at a relatively coarse level. We used Sentinel-1 constellation data to map the GL of the Amery Ice Shelf (AIS) using double-differential synthetic aperture radar interferometry. The ice thickness anomaly deduced from hydrostatic equilibrium and existing Antarctic GL products is compared with our result. With this new and very accurate GL, we detected new ice rises in the north of the AIS. Our new measurement shows no major change of the AIS GL, particularly in the southernmost part

Investigation Progresses and Applications of Fractional Derivative Model in Geotechnical Engineering

Over the past couple of decades, as a new mathematical tool for addressing a number of tough problems, fractional calculus has been gaining a continually increasing interest in diverse scientific fields, including geotechnical engineering due primarily to geotechnical rheology phenomenon. Unlike the classical constitutive models in which simulation analysis gradually fails to meet the reasonable accuracy of requirement, the fractional derivative models have shown the merits of hereditary phenomena with long memory. Additionally, it is traced that the fractional derivative model is one of the most effective and accurate approaches to describe the rheology phenomenon. In relation to this, an overview aimed first at model structure and parameter determination in combination with application cases based on fractional calculus was provided. Furthermore, this review paper shed light on the practical application aspects of deformation analysis of circular tunnel, rheological settlement of subgrade, and relevant loess researches subjected to the achievements acquired in geotechnical engineering. Finally, concluding remarks and important future investigation directions were pointed out

SPA: A Graph Spectral Alignment Perspective for Domain Adaptation

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces; (ii)-we further develop a fine-grained message propagation module -- upon a novel neighbor-aware self-training mechanism -- in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://github.com/CrownX/SPA.Comment: NeurIPS 2023 camera read