An Evolutionary Computation Based Feature Selection Method for Intrusion Detection

Data Availability: The data used to support the fndings of this study are available from the corresponding author upon request. This work was supported by the National Natural Science Foundation of China (61403206, 61771258, and 61876089), the Natural Science Foundation of Jiangsu Province (BK20141005 and BK20160910), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (14KJB520025), the Priority Academic Program Development of Jiangsu Higher Education Institutions, the Open Research Fund of Jiangsu Engineering Research Center of Communication and Network Technology, NJUPT (JSGCZX17001), and the Natural Science Foundation of Jiangsu Province of China under Grant BK20140883.Peer reviewedPublisher PD

Combining Machine Learning Models using combo Library

Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. To facilitate this process, we propose and implement an easy-to-use Python toolkit, combo, to aggregate models and scores under various scenarios, including classification, clustering, and anomaly detection. In a nutshell, combo provides a unified and consistent way to combine both raw and pretrained models from popular machine learning libraries, e.g., scikit-learn, XGBoost, and LightGBM. With accessibility and robustness in mind, combo is designed with detailed documentation, interactive examples, continuous integration, code coverage, and maintainability check; it can be installed easily through Python Package Index (PyPI) or https://github.com/yzhao062/combo.Comment: In Proceedings of Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020

Evolutionary Stages and Disk Properties of Young Stellar Objects in the Perseus Cloud

We investigated the evolutionary stages and disk properties of 211 Young stellar objects (YSOs) across the Perseus cloud by modeling the broadband optical to mid-infrared (IR) spectral energy distribution (SED). By exploring the relationships among the turnoff wave bands lambda_turnoff (longward of which significant IR excesses above the stellar photosphere are observed), the excess spectral index alpha_excess at lambda <~ 24 microns, and the disk inner radius R_in (from SED modeling) for YSOs of different evolutionary stages, we found that the median and standard deviation of alpha_excess of YSOs with optically thick disks tend to increase with lambda_turnoff, especially at lambda_turnoff >= 5.8 microns, whereas the median fractional dust luminosities L_dust/L_star tend to decrease with lambda_turnoff. This points to an inside-out disk clearing of small dust grains. Moreover, a positive correlation between alpha_excess and R_in was found at alpha_excess > ~0 and R_in > ~10 $\times$ the dust sublimation radius R_sub, irrespective of lambda_turnoff, L_dust/L_star and disk flaring. This suggests that the outer disk flaring either does not evolve synchronously with the inside-out disk clearing or has little influence on alpha_excess shortward of 24 microns. About 23% of our YSO disks are classified as transitional disks, which have lambda_turnoff >= 5.8 microns and L_dust/L_star >10^(-3). The transitional disks and full disks occupy distinctly different regions on the L_dust/L_star vs. alpha_excess diagram. Taking L_dust/L_star as an approximate discriminator of disks with (>0.1) and without (<0.1) considerable accretion activity, we found that 65% and 35% of the transitional disks may be consistent with being dominantly cleared by photoevaporation and dynamical interaction respectively. [abridged]Comment: 31 pages, 13 figures, 2 tables. To appear in a special issue of RAA on LAMOST science

The thesan project: public data release of radiation-hydrodynamic simulations matching reionization-era JWST observations

Cosmological simulations serve as invaluable tools for understanding the Universe. However, the technical complexity and substantial computational resources required to generate such simulations often limit their accessibility within the broader research community. Notable exceptions exist, but most are not suited for simultaneously studying the physics of galaxy formation and cosmic reionization during the first billion years of cosmic history. This is especially relevant now that a fleet of advanced observatories (e.g. James Webb Space Telescope, Nancy Grace Roman Space Telescope, SPHEREx, ELT, SKA) will soon provide an holistic picture of this defining epoch. To bridge this gap, we publicly release all simulation outputs and post-processing products generated within the THESAN simulation project at https://thesan-project.com. This project focuses on the $z \geq 5.5$ Universe, combining a radiation-hydrodynamics solver (AREPO-RT), a well-tested galaxy formation model (IllustrisTNG) and cosmic dust physics to provide a comprehensive view of the Epoch of Reionization. The THESAN suite includes 16 distinct simulations, each varying in volume, resolution, and underlying physical models. This paper outlines the unique features of these new simulations, the production and detailed format of the wide range of derived data products, and the process for data retrieval. Finally, as a case study, we compare our simulation data with a number of recent observations from the James Webb Space Telescope, affirming the accuracy and applicability of THESAN. The examples also serve as prototypes for how to utilise the released dataset to perform comparisons between predictions and observations.Comment: Data and documentation at https://www.thesan-project.com, comments and requests welcome, paper submitted to MNRA

UT-B-deficient mice develop renal dysfunction and structural damage

<p>Abstract</p> <p>Background</p> <p>Urea transporter UT-B is the major urea transporter in erythrocytes and the descending vasa recta in the kidney. In this study, we investigated the effects of long-term UT-B deficiency on functional and structural defect in the kidney of 16-and 52-week-old UT-B-null mice.</p> <p>Methods</p> <p>UT-B-knockout mice were generated by targeted gene disruption and lacked UT-B protein expression in all organs. The urinary concentrating ability of mice was studied in terms of daily urine output, urine osmolality, and urine and plasma chemistries. Changes in renal morphology were evaluated by hematoxylin and eosin staining.</p> <p>Results</p> <p>The UT-B-null mice showed defective urine concentrating ability. The daily urine output in UT-B-null mice (2.5 ± 0.1 ml) was 60% higher and urine osmolality (985 ± 151 mosm) was significantly lower than that in wild-type mice (1463 ± 227 mosm). The 52-week-old UT-B-null mice exhibited polyuria after water deprivation, although urine osmolality was increased. At 52 weeks of age, over 31% of UT-B-null mice exhibited renal medullary atrophy because of severe polyuria and hydronephrosis.</p> <p>Conclusions</p> <p>Long-term UT-B deficiency causes severe renal dysfunction and structural damage. These results demonstrate the important role of UT-B in countercurrent exchange and urine concentration.</p

Survivin gene silencing sensitizes prostate cancer cells to selenium growth inhibition

<p>Abstract</p> <p>Background</p> <p>Prostate cancer is a leading cause of cancer-related death in men worldwide. Survivin is a member of the inhibitor of apoptosis (IAP) protein family that is expressed in the majority of human tumors including prostate cancer, but is barely detectable in terminally differentiated normal cells. Downregulation of survivin could sensitize prostate cancer cells to chemotherapeutic agents <it>in vitro </it>and <it>in vivo</it>. Selenium is an essential trace element. Several studies have shown that selenium compounds inhibit the growth of prostate cancer cells. The objective of this study is to investigate whether survivin gene silencing in conjunction with selenium treatment could enhance the therapeutic efficacy for prostate cancer and to elucidate the underlying mechanisms.</p> <p>Methods</p> <p>Expression of survivin was analyzed in a collection of normal and malignant prostatic tissues by immunohistochemical staining. <it>In vitro </it>studies were conducted in PC-3M, C4-2B, and 22Rv1 prostate cancer cells. The effect of selenium on survivin expression was analyzed by Western blotting and semi-quantitative RT-PCR. Survivin gene knockdown was carried out by transfecting cells with a short hairpin RNA (shRNA) designed against survivin. Cell proliferation was quantitated by the 3-(4,5-Dimethylthiazol-2-yl)- 2,5-Diphenyltetrazolium Bromide (MTT) assay and apoptosis by propidium iodide staining followed by flow cytometry analysis. Finally, <it>in vivo </it>tumor growth assay was performed by establishing PC-3M xenograft in nude mice and monitoring tumor growth following transfection and treatment.</p> <p>Results</p> <p>We found that survivin was undetectable in normal prostatic tissues but was highly expressed in prostate cancers. Survivin knockdown or selenium treatment inhibited the growth of prostate cancer cells, but the selenium effect was modest. In contrast to what have been observed in other cell lines, selenium treatment had little or no effect on survivin expression in several androgen-independent prostate cancer cell lines. Survivin knockdown sensitized these cells to selenium growth inhibition and apoptosis induction. In nude mice bearing PC-3M xenografts, survivin knockdown synergizes with selenium in inhibiting tumor growth.</p> <p>Conclusions</p> <p>Selenium could inhibit the growth of hormone-refractory prostate cancer cells both <it>in vitro </it>and <it>in vivo</it>, but the effects were modest. The growth inhibition was not mediated by downregulating survivin expression. Survivin silencing greatly enhanced the growth inhibitory effects of selenium.</p

An Intrusion Detection System Based on Genetic Algorithm for Software-Defined Networks

A SDN (Software-Defined Network) separates the control layer from the data layer to realize centralized network control and improve the scalability and the programmability. SDN also faces a series of security threats. An intrusion detection system (IDS) is an effective means of protecting communication networks against traffic attacks. In this paper, a novel IDS model for SDN is proposed to collect and analyze the traffic which is generally at the control plane. Moreover, network congestion will occur when the amount of data transferred reaches the data processing capacity of the IDS. The suggested IDS model addresses this problem with a probability-based traffic sampling method in which the genetic algorithm (GA) is used to approach the sampling probability of each sampling point. According to the simulation results, the suggested IDS model based on GA is capable of enhancing the detection efficiency in SDNs

Visual Browse and Exploration in Motion Capture Data with Phylogenetic Tree of Context-Aware Poses

Visual browse and exploration in motion capture data take resource acquisition as a human&ndash;computer interaction problem, and it is an essential approach for target motion search. This paper presents a progressive schema which starts from pose browse, then locates the interesting region and then switches to online relevant motion exploration. It mainly addresses three core issues. First, to alleviate the contradiction between the limited visual space and ever-increasing size of real-world database, it applies affinity propagation to numerical similarity measure of pose to perform data abstraction and obtains representative poses of clusters. Second, to construct a meaningful neighborhood for user browsing, it further merges logical similarity measures of pose with the weight quartets and casts the isolated representative poses into a structure of phylogenetic tree. Third, to support online motion exploration including motion ranking and clustering, a biLSTM-based auto-encoder is proposed to encode the high-dimensional pose context into compact latent space. Experimental results on CMU&rsquo;s motion capture data verify the effectiveness of the proposed method