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Automatic modulation classification (AMC) is a technology that identifies a modulation scheme without prior signal information and plays a vital role in various applications, including cognitive radio and link adaptation. With the development of deep learning (DL), DL-based AMC methods have emerged, while most of them focus on reducing computational complexity in a centralized structure. This centralized learning-based AMC (CentAMC) violates data privacy in the aspect of direct transmission of client-side raw data. Federated learning-based AMC (FedeAMC) can bypass this issue by exchanging model parameters, but causes large resultant latency and client-side computational load. Moreover, both CentAMC and FedeAMC are vulnerable to large-scale noise occured in the wireless channel between the client and the server. To this end, we develop a novel AMC method based on a split learning (SL) framework, coined SplitAMC, that can achieve high accuracy even in poor channel conditions, while guaranteeing data privacy and low latency. In SplitAMC, each client can benefit from data privacy leakage by exchanging smashed data and its gradient instead of raw data, and has robustness to noise with the help of high scale of smashed data. Numerical evaluations validate that SplitAMC outperforms CentAMC and FedeAMC in terms of accuracy for all SNRs as well as latency., to be presented at IEEE VTC2023-Spring

PurposePresence of a long remnant sigmoid colon after left hemicolectomy with inferior mesenteric vein (IMV) ligation for distal transverse and descending colon cancers may be a risk factor for venous ischemia. This study aimed to evaluate the clinical impact of IMV preservation in patients who underwent left hemicolectomy with inferior mesenteric artery (IMA) preservation.MethodsWe included 155 patients who underwent left hemicolectomy with IMA preservation for distal transverse and descending colon cancers from 2003 to 2020. Technical success of IMV preservation was determined by assessing pre- and post-operative patency of the IMV on computed tomography (CT) by an abdominal radiologist. Intestinal complications comprising ulceration, stricture, venous engorgement, and colitis in remnant colon were compared between the IMV preservation and ligation groups.ResultsIMV was preserved in 22 (14.2%) and ligated in 133 (85.8%) patients. Surgical time, postoperative recovery outcomes, and number of harvested lymph nodes were similar in both groups. The technical success of IMV preservation was 81.8%. Intestinal complications were less common in the preservation group than in the IMV ligation group (4.5% vs. 23.3%, P=0.048). The complications in the IMV ligation group were anastomotic ulcer (n=2), anastomotic stricture (n=4), venous engorgement of the remnant distal colon (n=4), and colitis in the distal colon (n=21).ConclusionsIMV preservation may be beneficial after left hemicolectomy with IMA preservation for distal transverse and descending colon cancers. We suggest that IMV preservation might be considered when long remnant sigmoid colon is expected during left hemicolectomy with low ligation of IMA.

We will give a necessary condition for local connectedness of the space of Kleinian punctured torus group using Bromgerg's local coordinate system and provide a sufficient condition for local connectedness on a dense subset of the necessary condition. That is, the collection of the points where the boundary of the space of punctured torus group is not locally connected is a dense subset of the points satisfying the necessary condition.

Integrated gradients are widely employed to evaluate the contribution of input features in classification models because it satisfies the axioms for attribution of prediction. This method, however, requires an appropriate baseline for reliable determination of the contributions. We propose a compensated integrated gradients method that does not require a baseline. In fact, the method compensates the attributions calculated by integrated gradients at an arbitrary baseline using Shapley sampling. We prove that the method retrieves reliable attributions if the processes of input features in a classifier are mutually independent, and they are identical like shared weights in convolutional neural networks. Using three electroencephalogram datasets, we experimentally demonstrate that the attributions of the proposed method are more reliable than those of the original integrated gradients, and its computational complexity is much lower than that of Shapley sampling., Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216

This thesis investigates the Park Chung Hee model (PCHM). This term refers to a South Korean variant of the East Asian model of capitalism—particularly, the historical model that guided the rapid and sustained growth of the economy since the mid-1960s. This historical investigation is theoretically informed by a cultural political economy of variegated capitalism (VarCap-CPE) that enables a differential and integral exploration of both historical and contemporary capitalism. In this context, my contribution is twofold. The first is theoretical. While a theoretically informed historical investigation into East Asian capitalism requires an approach to (post-)colonialism, imperialism and hegemony as a prerequisite, VarCap-CPE has still not fully integrated such an approach into its analytical framework. So, my first aim is to improve this paradigm by drawing on Marx’s insights into colonialism, the world market, and international hegemony and propose how they might be put in their place, provisionally, in a VarCap-CPE analysis. My second goal is empirical. Based on the enhanced version of the VarCap-CPE, I aim to give a better account of the PCHM than previous literature in political economy. Specifically, I show how the model was informed by two contradictory state strategies: (1) the fascist and autarkic state strategies of Imperial Japan; and (2) the liberal and free trade-oriented developmentalism, based on W.W. Rostow’s modernization theory. I thereby demonstrate that the PCHM was self-contradictory and, in this context, present it as a “chimerical” model that combines in a contradictory manner the DNA of two rival species. On this basis, I provide an integral account of its seemingly miraculous performance as well as the dilemmas, contradictions and crisis-proneness that beset it. In addition, unlike much of the extant literature on the Park model, my analysis permits theoretically consistent further research into its crisis and subsequent neoliberalisation.