Routing for Intermittently-Powered Sensing Systems

Recently, intermittent computing (IC) has received tremendous attention due to its high potential in perpetual sensing for Internet-of-Things (IoT). By harvesting ambient energy, battery-free devices can perform sensing intermittently without maintenance, thus significantly improving IoT sustainability. To build a practical intermittently-powered sensing system, efficient routing across battery-free devices for data delivery is essential. However, the intermittency of these devices brings new challenges, rendering existing routing protocols inapplicable. In this paper, we propose RICS, the first-of-its-kind routing scheme tailored for intermittently-powered sensing systems. RICS features two major designs, with the goal of achieving low-latency data delivery on a network built with battery-free devices. First, RICS incorporates a fast topology construction protocol for each IC node to establish a path towards the sink node with the least hop count. Second, RICS employs a low-latency message forwarding protocol, which incorporates an efficient synchronization mechanism and a novel technique called pendulum-sync to avoid the time-consuming repeated node synchronization. Our evaluation based on an implementation in OMNeT++ and comprehensive experiments with varying system settings show that RICS can achieve orders of magnitude latency reduction in data delivery compared with the baselines

Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain

Linking computational natural language processing (NLP) models and neural responses to language in the human brain on the one hand facilitates the effort towards disentangling the neural representations underpinning language perception, on the other hand provides neurolinguistics evidence to evaluate and improve NLP models. Mappings of an NLP model's representations of and the brain activities evoked by linguistic input are typically deployed to reveal this symbiosis. However, two critical problems limit its advancement: 1) The model's representations (artificial neurons, ANs) rely on layer-level embeddings and thus lack fine-granularity; 2) The brain activities (biological neurons, BNs) are limited to neural recordings of isolated cortical unit (i.e., voxel/region) and thus lack integrations and interactions among brain functions. To address those problems, in this study, we 1) define ANs with fine-granularity in transformer-based NLP models (BERT in this study) and measure their temporal activations to input text sequences; 2) define BNs as functional brain networks (FBNs) extracted from functional magnetic resonance imaging (fMRI) data to capture functional interactions in the brain; 3) couple ANs and BNs by maximizing the synchronization of their temporal activations. Our experimental results demonstrate 1) The activations of ANs and BNs are significantly synchronized; 2) the ANs carry meaningful linguistic/semantic information and anchor to their BN signatures; 3) the anchored BNs are interpretable in a neurolinguistic context. Overall, our study introduces a novel, general, and effective framework to link transformer-based NLP models and neural activities in response to language and may provide novel insights for future studies such as brain-inspired evaluation and development of NLP models

CD133: a potential indicator for differentiation and prognosis of human cholangiocarcinoma.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: CD133 is known to be a cancer stem cell (CSC) marker. However, recent studies have revealed that CD133 is not restricted to CSC but to be expressed not only in human normal tissues but also in some cancers and could serve as a prognostic factor for the patients. Nevertheless, the expression of CD133 in human cholangiocarcinoma (CC) is rare and our study is to detect the expression and explore the potential functions of CD133 in human CC. METHODS: Fifty-nine cases, comprised of 5 normal liver tissues and 54 consecutive CC specimens (21 well-differentiated, 12 moderately-differentiated and 21 poorly-differentiated), were included in the study. Immunohistochemical stainning with CD133 protein was carried out, and statistical analyses were performed. RESULTS: CD133 was found to express in all 5 normal livers and 40 out of 54 (74%) CC tissues with different subcellular localization. In the well, moderately and poorly differentiated cases, the numbers of CD133 positive cases were 19 (19 of 21, 90%), 10 (10 of 12, 83%) and 11 (11 of 21, 52%) respectively. Further statistical analyses indicated that the expression and different subcellular localization of CD133 were significantly correlated with the differentiation status of tumors (P = 0.004, P = 0.009). Among 23 patients followed up for survival, the median survival was 4 months for fourteen CD133 negative patients but 14 months for nine CD133 positive ones. In univariate survival analysis, CD133 negative expression correlated with poor prognosis while CD133 positive expression predicted a favorable outcome of CC patients (P = 0.001). CONCLUSIONS: Our study demonstrates that CD133 expression correlates with the differentiation of CC and indicates that CD133 is a potential indicator for differentiation and prognosis of human CC.Published versio

Self-Sustainable Cyber-Physical Systems with Collaborative Intermittent Computing

Cyber-physical systems have become a main technology driver for our intelligent society. However, almost all cyber-physical systems rely on battery-powered devices to function, which suffer from high maintenance cost for recharging/replacing the batteries and bring in negative environmental impacts due to the hazardous chemicals used in the batteries. To address this challenge, a new computing paradigm called intermittent computing (IC) was proposed which advocates a battery-free design where cyber-physical devices can be completely powered by energy scavenged from ambient sources such as sunlight, radio waves, and vibrations. Since its advent, many efforts have been made on addressing the challenges in IC, from the hardware to the software stack. In this vision paper, we make a brief summary of existing works on IC and discuss a more realistic setup where, instead of focusing on one IC node as done in most existing works, we propose to build a self-sustainable cyber-physical system through the collaboration of distributed IC devices-collaborative intermittent computing (CIC). We discuss the challenges in CIC and provide a vision for the future cyber-physical systems