Miracle-dz/UniFAR-FTUs huggingface dataset:
Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection

Automated analysis methods are crucial aids for monitoring and defending a network to protect the sensitive or confidential data it hosts. This work introduces a flexible, powerful, and unsupervised approach to detecting anomalous behavior in computer and network logs, one that largely eliminates domain-dependent feature engineering employed by existing methods. By treating system logs as threads of interleaved "sentences" (event log lines) to train online unsupervised neural network language models, our approach provides an adaptive model of normal network behavior. We compare the effectiveness of both standard and bidirectional recurrent neural network language models at detecting malicious activity within network log data. Extending these models, we introduce a tiered recurrent architecture, which provides context by modeling sequences of users' actions over time. Compared to Isolation Forest and Principal Components Analysis, two popular anomaly detection algorithms, we observe superior performance on the Los Alamos National Laboratory Cyber Security dataset. For log-line-level red team detection, our best performing character-based model provides test set area under the receiver operator characteristic curve of 0.98, demonstrating the strong fine-grained anomaly detection performance of this approach on open vocabulary logging sources.
Association between SET expression and glioblastoma cell apoptosis and proliferation

Glioblastoma multiforme (GBM) was one of the first cancer types systematically studied at a genomic and transcriptomic level due to its high incidence and aggressivity; however, the detailed mechanism remains unclear, even though it is known that numerous cytokines are involved in the occurrence and development of GBM. The present study aimed to determine whether the SET gene has a role in human glioblastoma carcinogenesis. A total of 32 samples, including 18 cases of glioma, 2 cases of meningioma and 12 normal brain tissue samples, were detected using the streptavidin-peroxidase method through immunohistochemistry. To reduce SET gene expression in U251 and U87MG cell lines, the RNA interference technique was used and transfection with small interfering (si)RNA of the SET gene was performed. Cell apoptosis was detected by flow cytometry, cell migration was examined by Transwell migration assay and cell proliferation was determined by Cell Counting Kit-8. SET, Bcl-2, Bax and caspase-3 mRNA and protein expression levels were detected by reverse transcription-quantitative polymerase chain reaction and western blot analysis, respectively. Positive protein expression of SET was observed in the cell nucleus, with the expression level of SET significantly higher in glioma tissues compared with normal brain tissue (P=0.001). Elevated expression of SET was significantly associated with gender (P=0.002), tumors classified as World Health Organization grade II (P=0.031), III (P=0.003) or IV (P=0.001), and moderately (P=0.031) or poorly differentiated (P=0.001) tumors. Compared with the negative and non-treatment (blank) control cells, SET gene expression was significantly inhibited (P=0.006 and P<0.001), cell apoptosis was significantly increased (P=0.001 and P<0.001), cell proliferation was significantly inhibited (P=0.002 and P=0.015), and cell migration was significantly decreased (P=0.001 and P=0.001) in siRNA-transfected U87MG−SET and U251−SET cells, respectively. In addition, mRNA and protein expression levels of Bcl-2 were significantly inhibited in U87MG−SET and U251−SET cells, while mRNA and protein expression levels of Bax and caspase-3 were significantly increased, compared with the two control groups. Thus, the current data suggests that SET may regulate the proliferation and apoptosis of glioblastoma cells by upregulating Bcl-2, and downregulating Bax and caspase-3.
Action sequence reproduction based on automatic segmentation and Object-Action Complexes

Teaching robots object manipulation skills is a complex task that involves multimodal perception and knowledge about processing the sensor data. In this paper, we show a concept for humanoid robots in household environments with a variety of related objects and actions. Following the paradigms of Programming by Demonstration (PbD), we provide a flexible approach that enables a robot to adaptively reproduce an action sequence demonstrated by a human. The obtained human motion data with involved objects is segmented into semantic conclusive sub-actions by the detection of relations between the objects and the human actor. Matching actions are chosen from a library of Object-Action Complexes (OACs) using the preconditions and effects of each sub-action. The resulting sequence of OACs is parameterized for the execution on a humanoid robot depending on the observed action sequence and on the state of the environment during execution. The feasibility of this approach is shown in an exemplary kitchen scenario, where the robot has to prepare a dough.
Aspects of design and statistical analysis in the Comet assay.

Some aspects of the statistical design and analysis of the Comet (single cell gel electrophoresis) assay have been evaluated by means of a simulation study. The tail length and tail moment were selected for the quantification of DNA migration. Results from the simulation study showed that the choice of measure to summarize the cells on each slide is extremely important in order to facilitate an efficient analysis. For tail moment, the mean of log transformed data is clearly superior to the other evaluated measures, whereas using the mean of raw data without transformation can lead to very inefficient analyses. The 90th percentile, capturing the upper tail of the distribution, performs well for the tail length, with a slight improvement obtained by applying a log transformation prior to calculations. Furthermore, the simulation study has been used to assess the appropriateness of some models for statistical analysis and to address the issue of design (i.e. number of cultures or animals in each group, number of slides per animal/culture and number of cells scored per slide). Combining the results from the simulations with practical experience from the pharmaceutical industry, we conclude the paper by providing concise recommendations regarding the design and statistical analysis in the Comet assay.
Weighted informative inverse active class selection for motor imagery brain computer interface

EEG signal is used to establish a communication channel between brain and control device. A common scenario of machine learning based brain computer interface (BCI) is class wise accuracy is different from overall accuracy. Accuracy for one class is worse than overall accuracy. Because, error rates of all classes are not converging in same rate during training of learner. Active class selection (ACS) can be a solution to non-uniform converging of error rates for different classes. ACS determines the class-wise proportion of samples in training set based on empirical error. One of the ACS methods is inverse proportion. It implies that class proportion is inverse of class accuracy. Entropy of unlabeled samples is an active learning (AL) approach of finding the most informative samples. The intuition of this work is to feed the learner with most informative samples maintaining the class proportion given by inverse ACS method. Following this idea, an improved weighted inverse ACS method is developed and combined with AL query concept for BCI. This proposed weighted informative inverse active class selection method has been applied on BCI competition IV motor imagery (MI) binary class data set 2B. It shows better or similar performance on most of the subjects with less amount of training samples. So, this method gives us a way of performance improvement as well as reduction of training samples in BCI.
Reconstruction of cone-beam projections from Compton scattered data

The problem of reconstructing a 3D source distribution from Compton scattered data can be separated into two tasks. First, the angular distribution of line projections at different observation points within the detector volume are reconstructed. Then, reconstruction techniques are applied to the resulting cone-beam projections to synthesize the 3D source distribution. This paper describes an analytic solution for the first, yet unsolved, task. Building on the convolution theorem in spherical coordinates, a backprojection and inverse filtering technique in terms of spherical harmonics is formulated. The rotation invariance of the point response of the backprojection in spherical coordinates is proved; and the corresponding inverse filter function is derived. The resulting filtered backprojection algorithm then consists of a summation over all detected events of fixed and known event response functions. Measurement errors, which for Compton scatter detectors are typically different for each detected event, can easily be accounted for in the proposed algorithm. The computational cost of the algorithm is O(NT/sup 2/), where N is the number of detected events and /spl pi//T is the desired angular resolution.
Model‐based uncertainty in species range prediction

Aim Many attempts to predict the potential range of species rely on environmental niche (or ‘bioclimate envelope’) modelling, yet the effects of using different niche‐based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy‐guiding applications.
Video Quality-Based Spectral and Energy Efficient Mobile Association in Heterogeneous Wireless Networks

The staggering mobile growth is shaping to be the biggest shift in technology since the advent of the Internet, paving the way for unprecedented and yet to be thought of video services and applications. The proliferation of these mobile devices and applications, however, requires new paradigms to satisfy the increasing demands for capacity and energy, and achieve a good video quality. In this paper, we propose a video quality-based framework for spectrum and energy efficient mobile association and resource allocation in heterogeneous wireless networks. The basic tenets of the framework are (1) two novel performance metrics, namely QSE and QEE, to capture spectrum usage and energy consumption from video quality's perspective; and (2) a computationally efficient optimization model to derive mobile association and resource allocation for video connections in heterogeneous wireless networks. To this end, we first study the fundamental tradeoff between QSE and QEE in a PtP Rayleigh fading wireless channel. We then study QSE and QEE at the system level and develop a mobile association and resource allocation scheme that aims to jointly optimize system level QSE and QEE. The problem is formulated as a mixed-integer nonlinear optimization problem. Nonlinear fractional programming approach and dual decomposition method are applied to search the optimal solutions in a computationally efficient way. The simulation results evaluate the performance tradeoff between QSE and QEE, and show that the system performance, including PSNR distribution and maximum QSE/QEE values, greatly depends on bandwidth and power decaying factors.
Sites of [3H]taurine Uptake in the Rat Substantia Nigra in Relation to the Release of Taurine from the Striatonigral Pathway

The autoradiographic localization of radiolabelled taurine taken up in the rat substantia nigra in vivo together with conditions of release of the [3H]taurine taken up into brain slices were studied to determine whether they are consistent with the hypothesis that taurine may act as a neurotransmitter in the striatonigral pathway.
Engaging Stakeholders in Globally Distributed Software Development Processes

The engagement of stakeholders in the project lifecycle is a key factor in the success of any project. Yet getting stakeholders engaged is a difficult task. The problem is further exacerbated when the software development project is globally distributed, mainly because of communication barriers between different stakeholders. In this paper we describe multiple case studies where a wiki platform, together with specific working practices, enabled us to track the development process and the effectiveness of stakeholder engagement. The case studies were conducted by a consultancy team working with globally distributed teams. The wiki platform was used to ensure timely responses from stakeholders through their ongoing active involvement in the process. Engagement was automatically measured using metrics based on wiki usage data. In the case studies, we quantitatively analyzed these metrics. These metrics serve as an indication of the engagement effectiveness and thereby also the likelihood of projects' success, based on stakeholders' behavior rather than attitude. We add qualitative examples to this analysis for illustrating the quality of stakeholders' engagement. As opposed to the traditional approach that uses interviews and questionnaires, this automatic method measures stakeholder behavior rather than attitude. By analyzing wiki usage data we are able to identify different work practices according to the users— consultants or stakeholders—who performed changes in the wiki, and we identify practices that generate effective stakeholder engagement and practices that do not. In this paper we (1) define work practices for implementing effective stakeholder engagement and (2) develop metrics that automatically indicate the effectiveness of ongoing stakeholder engagement using the number of reads after a write.
Immunohistochemistry and molecular diagnostics of pleural malignant mesothelioma.

CONTEXT The pathologic approach to pleural-based lesions is stepwise and uses morphologic assessment, correlated with clinical and imaging data supplemented by immunohistochemistry (IHC), and more recently, molecular tests, as an aid for 2 main diagnostic problems: malignant mesothelioma (MM) versus other malignant tumors and malignant versus reactive mesothelial proliferations. OBJECTIVE To present the current knowledge regarding IHC and molecular tests with respect to MM diagnosis, and in particular, the differentiation of the epithelioid type of MM from carcinoma metastatic to the pleural cavity. DATA SOURCES A review of immunohistochemical features of 286 consecutive MMs from 459 cases of pleural pathology, diagnosed during routine practice from 2003 to 2009. A survey of biomedical journal literature from MedLine/PubMed (US National Library of Medicine) focused on MM and associated tissue-based diagnostic IHC markers and molecular tests. CONCLUSIONS The search for a single diagnostic marker of MM has so far been discouraging, given the biologic and phenotypic tumor heterogeneity of MM. The use of antibody panels has gained unanimous acceptance especially in the differential diagnosis between MM and metastatic carcinoma, whereas the usefulness of IHC is more limited when dealing with spindle cell malignancies or distinguishing malignant from reactive mesothelium. A great degree of interlaboratory variability in antibody combinations and clone selection within diagnostic panels still exists. Current investigations aim at selecting the most suitable and cost-effective combination of antibodies by using novel statistical approaches for assessing diagnostic performance beyond the traditional measures of sensitivity and specificity.
Properties of African Cassava Mosaic Virus Capsid Protein Expressed in Fission Yeast

The capsid proteins (CPs) of geminiviruses combine multiple functions for packaging the single-stranded viral genome, insect transmission and shuttling between the nucleus and the cytoplasm. African cassava mosaic virus (ACMV) CP was expressed in fission yeast, and purified by SDS gel electrophoresis. After tryptic digestion of this protein, mass spectrometry covered 85% of the amino acid sequence and detected three N-terminal phosphorylation sites (threonine 12, serines 25 and 62). Differential centrifugation of cell extracts separated the CP into two fractions, the supernatant and pellet. Upon isopycnic centrifugation of the supernatant, most of the CP accumulated at densities typical for free proteins, whereas the CP in the pellet fraction showed a partial binding to nucleic acids. Size-exclusion chromatography of the supernatant CP indicated high order complexes. In DNA binding assays, supernatant CP accelerated the migration of ssDNA in agarose gels, which is a first hint for particle formation. Correspondingly, CP shifted ssDNA to the expected densities of virus particles upon isopycnic centrifugation. Nevertheless, electron microscopy did not reveal any twin particles, which are characteristic for geminiviruses.
Theoretical estimates of HVAC duct channel capacity for high-speed Internet access

We report theoretical channel capacity estimates of heating, ventilation, and air-conditioning (HVAC) ducts based on multi-carrier transmission that uses M-QAM modulation and measured channel responses at 2.4 GHz Industrial, Scientific, and Medical (ISM) band. It is shown that, data rates in excess of 1 Gbps are possible over distances up to 500 m in "matched" ducts (one can think of "matched" ducts as user-friendly ducts, since "matching" can, in principle, eliminate all the multipath reflections in HVAC ducts). Our work also shows that data rates in excess of 300 Mbps are possible over distances up to 500 m even in the presence of significant multipath reflections.
The Effect of Cooling on the Density Profile of Hot Gas in Clusters of Galaxies: Is Additional Physics Needed?

We use high-resolution hydrodynamic simulations to investigate the density profile of hot gas in clusters of galaxies, adopting a variant of cold dark matter cosmologies and employing a cosmological N-body/smoothed particle hydrodynamics code to follow the evolution of dark matter and gas. In addition to gravitational interactions, gas pressure, and shock heating, we include bremsstrahlung cooling in the computation. Dynamical time, two-body relaxation time, and cooling time in the simulations are examined to demonstrate that the results are free from artificial relaxation effects and that the time step is short enough to accurately follow the evolution of the system. In the simulation with nominal resolution of 66 h-1 kpc the computed cluster appears normal, but in a higher (by a factor 2) resolution run, cooling is so efficient that the final gas density profile shows a steep rise toward the cluster center that is not observed in real clusters. Also, the X-ray luminosity of 7 × 1045 ergs s-1 far exceeds that for any cluster of the computed temperature. The most reasonable explanation for this discrepancy is that there are some physical processes still missing in the simulations that actually mitigate the cooling effect and play a crucial role in the thermal and dynamical evolution of the gas near the center. Among the promising candidate processes are heat conduction and heat input from supernovae. We discuss the extent to which these processes can alter the evolution of gas.
An evolutionary platform for developing next-generation electronic circuits

In this paper, a new method for evolving simple electronic circuits is discussed, with the aim of improving the reliability and performance of basic circuit blocks. Next-generation CMOS device models will be used in the simulation of circuits. Circuits are mapped to a grid layout which reflects the appearance of conventional schematic blocks. The performance of the system at designing passive low-pass filters is discussed, with an outline given of the intended future steps, towards the goal of integrating sub 100 nm MOSFET models into the circuits.
Climate and landscape controls on water balance model complexity over changing timescales

A systematic approach is described for determining the minimum level of model complexity required to predict runoff in New Zealand catchments, with minimal calibration, at decreasing timescales. Starting with a lumped conceptual model representing the most basic hydrological processes needed to capture water balance, model complexity is systematically increased in response to demonstrated deficiencies in model predictions until acceptable accuracy is achieved. Sensitivity and error analyses are performed to determine the dominant physical controls on streamflow variability. It is found that dry catchments are sensitive to a threshold storage parameter, producing inaccurate results with little confidence, while wet catchments are relatively insensitive, producing more accurate results with more confidence. Sensitivity to the threshold parameter is well correlated with climate and timescale, and in combination with the results of two previous studies, this allowed the postulation of a qualitative relationship between model complexity, timescale, and the climatic dryness index (DI). This relationship can provide an a priori understanding of the model complexity required to accurately predict streamflow with confidence in small catchments under given climate and timescales and a conceptual framework for model selection. The objective of the paper is therefore not to present a perfect model for any of the catchments studied but rather to present a systematic approach to modeling based on making inferences from data that can be applied with respect to different model designs, catchments and timescales.
Median-based generation of synthetic speech durations using a non-parametric approach

This paper proposes a new approach to duration modelling for statistical parametric speech synthesis in which a recurrent statistical model is trained to output a phone transition probability at each timestep (acoustic frame). Unlike conventional approaches to duration modelling - which assume that duration distributions have a particular form (e.g., a Gaussian) and use the mean of that distribution for synthesis - our approach can in principle model any distribution supported on the non-negative integers. Generation from this model can be performed in many ways; here we consider output generation based on the median predicted duration. The median is more typical (more probable) than the conventional mean duration, is robust to training-data irregularities, and enables incremental generation. Furthermore, a frame-level approach to duration prediction is consistent with a longer-term goal of modelling durations and acoustic features together. Results indicate that the proposed method is competitive with baseline approaches in approximating the median duration of held-out natural speech.