A Cost Effective Block Framing Scheme for Underwater Communication

In this paper, the Selective Multiple Acknowledgement (SMA) method, based on Multiple Acknowledgement (MA), is proposed to efficiently reduce the amount of data transmission by redesigning the transmission frame structure and taking into consideration underwater transmission characteristics. The method is suited to integrated underwater system models, as the proposed method can handle the same amount of data in a much more compact frame structure without any appreciable loss of reliability. Herein, the performance of the proposed SMA method was analyzed and compared to those of the conventional Automatic Repeat-reQuest (ARQ), Block Acknowledgement (BA), block response, and MA methods. The efficiency of the underwater sensor network, which forms a large cluster and mostly contains uplink data, is expected to be improved by the proposed method

Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification

Whole slide image (WSI) classification is a fundamental task for the diagnosis and treatment of diseases; but, curation of accurate labels is time-consuming and limits the application of fully-supervised methods. To address this, multiple instance learning (MIL) is a popular method that poses classification as a weakly supervised learning task with slide-level labels only. While current MIL methods apply variants of the attention mechanism to re-weight instance features with stronger models, scant attention is paid to the properties of the data distribution. In this work, we propose to re-calibrate the distribution of a WSI bag (instances) by using the statistics of the max-instance (critical) feature. We assume that in binary MIL, positive bags have larger feature magnitudes than negatives, thus we can enforce the model to maximize the discrepancy between bags with a metric feature loss that models positive bags as out-of-distribution. To achieve this, unlike existing MIL methods that use single-batch training modes, we propose balanced-batch sampling to effectively use the feature loss i.e., (+/-) bags simultaneously. Further, we employ a position encoding module (PEM) to model spatial/morphological information, and perform pooling by multi-head self-attention (PSMA) with a Transformer encoder. Experimental results on existing benchmark datasets show our approach is effective and improves over state-of-the-art MIL methods.Comment: MICCAI 202

Effects of Genetic and Pharmacologic Inhibition of COX-2 on Colitis-associated Carcinogenesis in Mice

COX-2 has been inappropriately overexpressed in various human malignancies, and is considered as one of the representative targets for the chemoprevention of inflammation-associated cancer. In order to assess the role of COX-2 in colitis-induced carcinogenesis, the selective COX-2 inhibitor celecoxib and COX-2 null mice were exploited in an azoxymethane (AOM)-initiated and dextran sulfate sodium (DSS)-promoted murine colon carcinogenesis model. The administration of 2% DSS in drinking water for 1 week after a single intraperitoneal injection of AOM produced colorectal adenomas in 83% of mice, whereas only 27% of mice given AOM alone developed tumors. Oral administration of celecoxib significantly lowered the incidence as well as the multiplicity of colon tumors. The expression of COX-2 and inducible nitric oxide synthase (iNOS) was upregulated in the colon tissues of mice treated with AOM and DSS, and this was inhibited by celecoxib administration. Likewise, celecoxib treatment abrogated the DNA binding of NF-kappa B, a key transcription factor responsible for regulating expression of aforementioned pro-inflammatory enzymes, which was associated with suppression of I kappa B alpha degradation. In the COX-2 null (COX-2(-/-)) mice, there was about 30% reduction in the incidence of colon tumors, and the tumor multiplicity was also markedly reduced (7.7 +/- 2.5 vs. 2.43 +/- 1.4, P < 0.01). As both pharmacologic inhibition and genetic ablation of COX- 2 gene could not completely suppress colon tumor formation following treatment with AOM and DSS, it is speculated that other pro-inflammatory mediators, including COX-1 and iNOS, should be additionally targeted to prevent inflammation-associated colon carcinogenesis.

Geomagnetic field influences probabilistic abstract decision-making in humans

To resolve disputes or determine the order of things, people commonly use binary choices such as tossing a coin, even though it is obscure whether the empirical probability equals to the theoretical probability. The geomagnetic field (GMF) is broadly applied as a sensory cue for various movements in many organisms including humans, although our understanding is limited. Here we reveal a GMF-modulated probabilistic abstract decision-making in humans and the underlying mechanism, exploiting the zero-sum binary stone choice of Go game as a proof-of-principle. The large-scale data analyses of professional Go matches and in situ stone choice games showed that the empirical probabilities of the stone selections were remarkably different from the theoretical probability. In laboratory experiments, experimental probability in the decision-making was significantly influenced by GMF conditions and specific magnetic resonance frequency. Time series and stepwise systematic analyses pinpointed the intentionally uncontrollable decision-making as a primary modulating target. Notably, the continuum of GMF lines and anisotropic magnetic interplay between players were crucial to influence the magnetic field resonance-mediated abstract decision-making. Our findings provide unique insights into the impact of sensing GMF in decision-makings at tipping points and the quantum mechanical mechanism for manifesting the gap between theoretical and empirical probability in 3-dimensional living space.Comment: 32 pages, 5 figures, 4 supplementary figures, 2 supplementary tables, and separate 15 ancillary file