Lifetime and latency aware data collection in wireless sensor networks

A Wireless Sensor Network (WSN) consists of a set of sensor nodes deployed in the environment where we intend to collect physical information such as temperatures. All the senor nodes are connected wirelessly, and work cooperatively to fulfill some specified tasks. Sensor nodes are typically battery powered. As a result, the network lifetime becomes a major optimization objective in the design of a WSN. Another important optimisation objective is to minimize the maximum latency of data collection for time-critical applications. In this thesis, we study the problem of lifetime and latency aware data collection in a static WSN with only one base station. We propose two novel routing structures, namely, k-tree and k-DAG, to balance the loads of the neighbouring sensor nodes of the base station to prolong the lifetime of the network while providing the maximum latency guarantee. Firstly, we investigate the lifetime aware data collection problem by using ktree. A k-tree is a spanning tree with the base station as the root such that the path from each sensor node to the base station is at most k hops longer than the shortest path from this sensor node to the base station. We propose a distributed algorithm for constructing a k-tree such that the loads of the base station s children are balanced. Secondly, we study the lifetime aware data collection problem by using k-DAG. A k-DAG is a spanning Directed Acyclic Graph (DAG) with the base station as the only source node such that the path length of any path from each sensor node to the base station is not k hops longer than its shortest path length to the base station. We present a distributed algorithm for constructing a k-DAG such that the loads of the base station s children are balanced. In addition, we propose an efficient distributed naming scheme to assign a unique ID to each sensor node for efficient point-to-point communication. We have implemented all of our algorithms by Cooja simulator. The simulation results show that our approaches significantly increase the network lifetime by up to 82%

Aberrant Protein Turn-Over Associated With Myofibrillar Disorganization in FHL1 Knockout Mice

Mutations in the FHL1 gene, and FHL1 protein deletion, are associated with rare hereditary myopathies and cardiomyopathies. FHL1-null mice develop age-dependent myopathy and increased autophagic activity. However, the molecular pathway involved in contractile function and increased autophagic activity in the FHL1-null mouse has not yet been fully elucidated. In this study, FHL1 protein was knocked out in mice using Transcription Activator-like Effector Nucleases (TALENs) and the IRS1-FOXO1/mTOR signaling pathway was investigated in skeletal muscles and heart. TALEN constructs caused targeted mutations in 30% of newborn mice; these mutations caused a deletion of 1–13 base pairs which blocked synthesis of the full-length FHL1 protein. Furthermore, 2.5-month old FHL1-null male mice were not prone to global muscular fatigue when compared with WT littermates, but histological analysis and ultrastructural analysis by transmission electron microscopy confirmed the presence of myofibrillar disorganization and the accumulation of autophagosome or autolysosome-like structures in FHL1-null mice. Moreover, autophagy and mitophagy were both activated in FHL1 KO mice and the degradation of autophagic lysosomes was impeded. Enhanced autophagic activity in FHL1 KO mice was induced by FOXO1 up-regulation and protein synthesis was increased via mTOR. The cytoskeletal proteins, MYBPC2 and LDB3, were involved in the formation of pathological changes in FHL1 KO mice. Markers of early differentiation (MEF2C and MYOD1) and terminal differentiation (total MYH) were both up-regulated in tibialis anterior (TA) muscles in FHL1 KO mice. The number of type I and type II fibers increased in FHL1-null TA muscles, but the number of type| | b, and type | | d fibers were both reduced in FHL1-null TA muscles. The results obtained from the heart were consistent with those from the skeletal muscle and indicated autophagic activation by FOXO1 and an increase in protein synthesis via mTOR also occurred in the heart tissue of FHL1 knockout mice. In conclusion, aberrant protein turn-over associated with myofibrillar disorganization in FHL1 knockout mice. the up-regulation of FOXO1 was associated with enhanced autophagic activity and pathological changes in the muscle fibers of FHL1 KO mice. These results indicated that autophagy activated by FOXO1 is a promising therapeutic target for hereditary myopathies and cardiomyopathies induced by FHL1

Lego-MT: Towards Detachable Models in Massively Multilingual Machine Translation

Multilingual neural machine translation (MNMT) aims to build a unified model for many language directions. Existing monolithic models for MNMT encounter two challenges: parameter interference among languages and inefficient inference for large models. In this paper, we revisit the classic multi-way structures and develop a detachable model by assigning each language (or group of languages) to an individual branch that supports plug-and-play training and inference. To address the needs of learning representations for all languages in a unified space, we propose a novel efficient training recipe, upon which we build an effective detachable model, Lego-MT. For a fair comparison, we collect data from OPUS and build a translation benchmark covering 433 languages and 1.3B parallel data. Experiments show that Lego-MT with 1.2B parameters brings an average gain of 3.2 spBLEU. It even outperforms M2M-100 with 12B parameters. The proposed training recipe brings a 28.2$\times$ speedup over the conventional multi-way training method.\footnote{ \url{https://github.com/CONE-MT/Lego-MT}.}Comment: ACL 2023 Finding

Nociception and hypersensitivity involve distinct neurons and molecular transducers in Drosophila

Significance: Functional plasticity of the nociceptive circuit is a remarkable feature and is of clinical relevance. As an example, nociceptors lower their threshold upon tissue injury, a process known as allodynia that would facilitate healing by guarding the injured areas. However, long-lasting hypersensitivity could lead to chronic pain, a debilitating disease not effectively treated. Therefore, it is crucial to dissect the mechanisms underlying basal nociception and nociceptive hypersensitivity. In both vertebrate and invertebrate species, conserved transient receptor potential (Trp) channels are the primary transducers of noxious stimuli. Here, we provide a precedent that in Drosophila larvae, heat sensing in the nociception and hypersensitivity states is mediated by distinct heat-sensitive neurons and TrpA1 alternative isoforms

Balancing Logit Variation for Long-tailed Semantic Segmentation

Semantic segmentation usually suffers from a long-tail data distribution. Due to the imbalanced number of samples across categories, the features of those tail classes may get squeezed into a narrow area in the feature space. Towards a balanced feature distribution, we introduce category-wise variation into the network predictions in the training phase such that an instance is no longer projected to a feature point, but a small region instead. Such a perturbation is highly dependent on the category scale, which appears as assigning smaller variation to head classes and larger variation to tail classes. In this way, we manage to close the gap between the feature areas of different categories, resulting in a more balanced representation. It is noteworthy that the introduced variation is discarded at the inference stage to facilitate a confident prediction. Although with an embarrassingly simple implementation, our method manifests itself in strong generalizability to various datasets and task settings. Extensive experiments suggest that our plug-in design lends itself well to a range of state-of-the-art approaches and boosts the performance on top of them