On Reliability of Underwater Magnetic Induction Communications with Tri-Axis Coils

Underwater magnetic induction communications (UWMICs) provide a low-power and high-throughput solution for autonomous underwater vehicles (AUVs), which are envisioned to explore and monitor the underwater environment. UWMIC with tri-axis coils increases the reliability of the wireless channel by exploring the coil orientation diversity. However, the UWMIC channel is different from typical fading channels and the mutual inductance information (MII) is not always available. It is not clear the performance of the tri-axis coil MIMO without MII. Also, its performances with multiple users have not been investigated. In this paper, we analyze the reliability and multiplexing gain of UWMICs with tri-axis coils by using coil selection. We optimally select the transmit and receive coils to reduce the computation complexity and power consumption and explore the diversity for multiple users. We find that without using all the coils and MII, we can still achieve reliability. Also, the multiplexing gain of UWMIC without MII is 5dB smaller than typical terrestrial fading channels. The results of this paper provide a more power-efficient way to use UWMICs with tri-axis coils

Efficient Subgraph Matching on Billion Node Graphs

The ability to handle large scale graph data is crucial to an increasing number of applications. Much work has been dedicated to supporting basic graph operations such as subgraph matching, reachability, regular expression matching, etc. In many cases, graph indices are employed to speed up query processing. Typically, most indices require either super-linear indexing time or super-linear indexing space. Unfortunately, for very large graphs, super-linear approaches are almost always infeasible. In this paper, we study the problem of subgraph matching on billion-node graphs. We present a novel algorithm that supports efficient subgraph matching for graphs deployed on a distributed memory store. Instead of relying on super-linear indices, we use efficient graph exploration and massive parallel computing for query processing. Our experimental results demonstrate the feasibility of performing subgraph matching on web-scale graph data.Comment: VLDB201

Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution

The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more efficient solution. As limited anomaly labels hinder traditional supervised models in TSAD, various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of having explicit anomalies that even random functions can detect. This problem is exacerbated by ill-posed evaluation metrics, known as point adjustment (PA), which can result in inflated model performance. In this context, we propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD), which addresses these challenges by modeling features across three data domains - temporal, frequency, and residual domains - without relying on anomaly labels. Unlike traditional contrastive learning methods, TriAD employs both inter-domain and intra-domain contrastive loss to learn common attributes among normal data and differentiate them from anomalies. Additionally, our approach can detect anomalies of varying lengths by integrating with a discord discovery algorithm. It is worth noting that this study is the first to reevaluate the deep learning potential in TSAD, utilizing both rigorously designed datasets (i.e., UCR Archive) and evaluation metrics (i.e., PA%K and affiliation). Through experimental results on the UCR dataset, TriAD achieves an impressive three-fold increase in PA%K based F1 scores over SOTA deep learning models, and 50% increase of accuracy as compared to SOTA discord discovery algorithms.Comment: This work is submitted to IEEE International Conference on Data Engineering (ICDE) 202

Rational selection of small molecules that increase transcription through the GAA repeats found in Friedreich’s ataxia

AbstractFriedreich’s ataxia (FRDA) is an autosomal recessive trinucleotide repeat disease with no effective therapy. Expanded GAA repeats in the first intron of the FRDA gene are thought to form unusual non-B DNA conformations that decrease transcription and subsequently reduce levels of the encoded protein, frataxin. Frataxin plays a crucial role in iron metabolism and detoxification. To discover small molecules that increase transcription through the GAA repeat region in FRDA, we have made stable cell lines containing a portion of expanded intron 1 fused to a GFP reporter. Small molecules identified using the competition dialysis method were found to increase FRDA-intron 1-reporter gene expression. One of these compounds, pentamidine, increases frataxin levels in patient cells. Thus our approach can be used to detect small molecules of potential therapeutic value in FRDA

Novel Refrigeration Cycle with Continuous Cooling Turbo Compressor and Condensing Ejector Using Water as Refrigerant

Turbo chiller using water as a refrigerant was developed. A refrigeration cycle using water (R718) as a refrigerant has a high theoretical efficiency, but the cycle pressure ratio is high, and the compressor discharge temperature is elevated. Although the conversion of the compressor to a multistage compressor and the addition of an intercooler between each stage are known to lower the discharge temperature and improve performance, the resulting increased size is problematic. To resolve this issue, this study develops a refrigeration cycle that lowers discharge temperature during the compression process without enlarging the device. Using a continuous cooling compressor that compresses refrigerant vapor while continuously cooling it in process and a condensing ejector that condenses the vapor while compressing it, we experimentally verify the performance of each element. Based on the results, we verify the basic principles of a continuous cooling compressor for continuously cooling compressed vapor with refrigerant droplets sprayed by nozzles installed on the impeller. In addition, the condensing ejector uses a high-speed fine refrigerant droplets to transfer momentum to the refrigerant vapor. And thus raises its pressure within a two-phase state and simultaneously condensing refrigerant vapor