Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Anomalies in Software Execution Logs with Siamese Network

Logs are semi-structured text files that represent software's execution paths and states during its run-time. Therefore, detecting anomalies in software logs reflect anomalies in the software's execution path or state. So, it has become a notable concern in software engineering. We use LSTM like many prior works, and on top of LSTM, we propose a novel anomaly detection approach based on the Siamese network. This paper also provides an authentic validation of the approach on the Hadoop Distributed File System (HDFS) log dataset. To the best of our knowledge, the proposed approach outperforms other methods on the same dataset at the F1 score of 0.996, resulting in a new state-of-the-art performance on the dataset. Along with the primary method, we introduce a novel training pair generation algorithm that reduces generated training pairs by the factor of 3000 while maintaining the F1 score, merely a modest decay from 0.996 to 0.995. Additionally, we propose a hybrid model by combining the Siamese network with a traditional feedforward neural network to make end-to-end training possible, reducing engineering effort in setting up a deep-learning-based log anomaly detector. Furthermore, we examine our method's robustness to log evolutions by evaluating the model on synthetically evolved log sequences; we got the F1 score of 0.95 at the noise ratio of 20%. Finally, we dive deep into some of the side benefits of the Siamese network. Accordingly, we introduce a method of monitoring the evolutions of logs without label requirements at run-time. Additionally, we present a visualization technique that facilitates human administrations of log anomaly detection.
Probabilistically violating the first law of thermodynamics in a quantum heat engine

Fluctuations of thermodynamic observables, such as heat and work, contain relevant information on the underlying physical process. These fluctuations are however not taken into account in the traditional laws of thermodynamics. While the second law is extended to fluctuating systems by the celebrated fluctuation theorems, the first law is generally believed to hold even in the presence of fluctuations. Here we show that in the presence of quantum fluctuations, also the first law of thermodynamics may break down. This happens because quantum mechanics imposes constraints on the knowledge of heat and work. To illustrate our results, we provide a detailed case-study of work and heat fluctuations in a quantum heat engine based on a circuit QED architecture. We find probabilistic violations of the first law and show that they are closely connected to quantum signatures related to negative quasi-probabilities. Our results imply that in the presence of quantum fluctuations, the first law of thermodynamics may not be applicable to individual experimental runs.
Reinforcement Learning for Decision-Making and Control in Power Systems: Tutorial, Review, and Vision

—With large-scale integration of renewable generation and distributed energy resources (DERs), modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to the widespread deployment of smart meters, smart sensors, and upgraded communication networks. As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. In this paper
The sharp second order Caffareli-Kohn-Nirenberg inequality and stability estimates for the sharp second order uncertainty principle

In this paper we prove a class of second order Caffarelli-Kohn-Nirenberg inequalities which contains the sharp second order uncertainty principle recently established by Cazacu, Flynn and Lam [13] as a special case. We also show the sharpness of our inequalities for several classes of parameters. Finally, we prove a stability version of the sharp second order uncertainty principle of Cazacu, Flynn and Lam by showing that the difference of both sides of the inequality controls the distance to the set of extremal functions in L2 norm of gradient of functions.
Design and Performance Analysis of a Highly Efficient Polychromatic Full Stokes Polarization Modulator for the CRISP Imaging Spectrometer

We present the design and performance of a polychromatic polarization modulator for the CRisp Imaging SpectroPolarimeter (CRISP) Fabry–Perot tunable narrow-band imaging spectropolarimer at the Swedish 1 m Solar Telescope (SST). We discuss the design process in depth, compare two possible modulator designs through a tolerance analysis, and investigate thermal sensitivity of the selected design. The trade-offs and procedures described in this paper are generally applicable in the development of broadband polarization modulators. The modulator was built and has been operational since 2015. Its measured performance is close to optimal between 500 and 900 nm, and differences between the design and as-built modulator are largely understood. We show some example data, and briefly review scientific work that used data from SST/CRISP and this modulator.
Bayesian data-driven discovery of partial differential equations with variable coefficients

The discovery of Partial Differential Equations (PDEs) is an essential task for applied science and engineering. However, data-driven discovery of PDEs is generally challenging, primarily stemming from the sensitivity of the discovered equation to noise and the complexities of model selection. In this work, we propose an advanced Bayesian sparse learning algorithm for PDE discovery with variable coefficients, predominantly when the coefficients are spatially or temporally dependent. Specifically, we apply threshold Bayesian group Lasso regression with a spike-and-slab prior (tBGL-SS) and leverage a Gibbs sampler for Bayesian posterior estimation of PDE coefficients. This approach not only enhances the robustness of point estimation with valid uncertainty quantification but also relaxes the computational burden from Bayesian inference through the integration of coefficient thresholds as an approximate MCMC method. Moreover, from the quantified uncertainties, we propose a Bayesian total error bar criteria for model selection, which outperforms classic metrics including the root mean square and the Akaike information criterion. The capability of this method is illustrated by the discovery of several classical benchmark PDEs with spatially or temporally varying coefficients from solution data obtained from the reference simulations. In the experiments, we show that the tBGL-SS method is more robust than the baseline methods under noisy environments and provides better model selection criteria along the regularization path.
Ubiquitous Dynamical Time Asymmetry in Measurements on Materials and Biological Systems

Many measurements on soft condensed matter (e.g., biological and materials) systems track low-dimensional observables projected from the full system phase space as a function of time. Examples are dynamic structure factors, spectroscopic and rheological response functions, and time series of distances derived from optical tweezers, single-molecule spectroscopy and molecular dynamics simulations. In many such systems the projection renders the reduced dynamics non-Markovian and the observable is not prepared in, or initially sampled from and averaged over, a stationary distribution. We prove that such systems always exhibit non-equilibrium, time asymmetric dynamics. That is, they evolve in time with a broken time-translation invariance in a manner closely resembling aging dynamics. We identify the entropy associated with the breaking of time-translation symmetry that is a measure of the instantaneous thermodynamic displacement of latent, hidden degrees of freedom from their stationary state. Dynamical time asymmetry is a general phenomenon, independent of the underlying energy surface, and is frequently even visible in measurements on systems that have fully reached equilibrium. This finding has fundamental implications for the interpretation of many experiments on, and simulations of, biological and materials systems.
Fast Exploration of Weight Sharing Opportunities for CNN Compression

The computational workload involved in Convolutional Neural Networks (CNNs) is typically out of reach for low-power embedded devices. There are a large number of approximation techniques to address this problem. These methods have hyper-parameters that need to be optimized for each CNNs using design space exploration (DSE). The goal of this work is to demonstrate that the DSE phase time can easily explode for state of the art CNN. We thus propose the use of an optimized exploration process to drastically reduce the exploration time without sacrificing the quality of the output.
Visual Framing of Science Conspiracy Videos: Integrating Machine Learning with Communication Theories to Study the Use of Color and Brightness

Recent years have witnessed an explosion of science conspiracy videos on the Internet, challenging science epistemology and public understanding of science. Scholars have started to examine the persuasion techniques used in conspiracy messages such as uncertainty and fear yet, little is understood about the visual narratives, especially how visual narratives differ in videos that debunk conspiracies versus those that propagate conspiracies. This paper addresses this gap in understanding visual framing in conspiracy videos through analyzing millions of frames from conspiracy and counter-conspiracy YouTube videos using computational methods. We found that conspiracy videos tended to use lower color variance and brightness, especially in thumbnails and earlier parts of the videos. This paper also demonstrates how researchers can integrate textual and visual features in machine learning models to study conspiracies on social media and discusses the implications of computational modeling for scholars interested in studying visual manipulation in the digital era. The analysis of visual and textual features presented in this paper could be useful for future studies focused on designing systems to identify conspiracy content on the Internet.
PHASE DESCRIPTION IN POLAR LIQUID CRYSTALS USING MODIFIED WEIGHTED DENSITY APPROXIMATION (MWDA)

The compatibility of different approximations to the Helmholtz free energy F is analysed in order to provide the consistent definition of the order parameters for liquid crystalline phases. In particular the diagrammatic expansion theory is compared to the weighted, also modified weighted, density approximations. The weight function w is shown to be proportional to the Mayer function. The explicit form of the weight function for the system of non-uniaxial polar molecules is given and the interaction terms of the polar and orientational order parameters in the free energy expansion are discussed.
The motor status of a hemiplegic patient can be assessed by the analysis of the gait regularity. There are a several scales for gait assessment: Rivermead Visual Gait Assessment (RVGA), Salford Gait Tool (SGT), Observational Gait Scale

We developed a method for assessment of the gait regularity in hemiplegic patients. We used a fully wearable system comprising ground reaction force sensors and inertial measurement units to record the dynamics and array electrodes and multichannel amplifiers for electromyography (EMG) mapping of activity of tibialis anterior m. during the gait. The fuzzy logic was applied to ground reaction force signals for estimating the gait symmetry. This paper introduces a new parameter for estimation of symmetry of muscle activities in the ipsilateral and contralateral legs based on the entropy of EMG maps. The presented method forms a set of gait parameters for quantifying the regularity of the patient’s gait. The set of parameters are of interest for the assessment of the efficacy of the therapy.
Optimized Task Scheduling Algorithm for cloud computing environment

Cloud Computing is a rapidly growing technology and it offers online accessible resources to the users. These resources include applications, servers, networking, and storage etc. and provide security, elasticity, scalability and low cost. Most of the businesses are migrating from on-premise to the cloud and millions of users access these services daily via the internet. So it is very important to apply appropriate scheduling technique to process a large amount of data and to do resource utilization more efficiently with better performance. This research paper presents new proposed algorithm which is using benefits of both Enhanced Max-Min and Max-Min algorithms together. In this proposed algorithm, there are two sets of resources created by considering their MIPS speed where the first one contains the resources with maximum execution time and the second one contains the resources with minimum execution time. The large task is assigned to the resource if the available resource is from the second set and average length task is assigned to the resource if the available resource is from the first set. The simulation tool used for testing is WorkflowSim. Test results display that the new proposed algorithm represents enhanced resource utilization with better makespan.
Cyclic Adenosine 3 ' , 5 '-Monophosphate Regulation of the Bacteriophage T 6 / Colicin K Receptor in Escherichia colit

Mutant strains of Escherichia coli unable to synthesize cyclic adenosine 3',5'monophosphate (cAMP) or the cyclic adenosine monophosphate receptor protein (CRP) were more resistant than wild-type cells to infection by bacteriophage T6. This resistance was found to be associated with the decreased production of specific T6 receptor protein (also the colicin K receptor) located in the outer membrane protein fraction of these cells. Transcription of this particular outer membrane protein was regulated by the cAMP-CRP complex. A novel affinity technique coupled with sodium dodecyl sulfate-polyacrylamide gel electrophoresis was used in these investigations.
Forecasting Wind Ramps

The recent increase in the number of wind power producers has necessitated changes in the methods power system operators employ to balance load and generation. Furthermore, the number of renewable energy producers is also expected to increase in the US to 20% of total generation by the year 2030 [1]. Wind power, unlike conventional fossil-fuel based generation, is intermittent and uncertain. Therefore, it is crucial that this variability is quantified so that power system operators can schedule appropriate amounts of reserve generation to compensate for fluctuations in wind production.
Approximation of occupation time functionals

The strong L2-approximation of occupation time functionals is studied with respect to discrete observations of a d-dimensional cadlag process. Upper bounds on the error are obtained under weak assumptions, generalizing previous results in the literature considerably. The approach relies on regularity for the marginals of the process and applies also to non-Markovian processes, such as fractional Brownian motion. The results are used to approximate occupation times and local times. For Brownian motion, the upper bounds are shown to be sharp up to a log-factor.
Automated Extraction and Presentation of Data Practices in Privacy Policies

Abstract Privacy policies are documents required by law and regulations that notify users of the collection, use, and sharing of their personal information on services or applications. While the extraction of personal data objects and their usage thereon is one of the fundamental steps in their automated analysis, it remains challenging due to the complex policy statements written in legal (vague) language. Prior work is limited by small/generated datasets and manually created rules. We formulate the extraction of fine-grained personal data phrases and the corresponding data collection or sharing practices as a sequence-labeling problem that can be solved by an entity-recognition model. We create a large dataset with 4.1k sentences (97k tokens) and 2.6k annotated fine-grained data practices from 30 real-world privacy policies to train and evaluate neural networks. We present a fully automated system, called PI-Extract, which accurately extracts privacy practices by a neural model and outperforms, by a large margin, strong rule-based baselines. We conduct a user study on the effects of data practice annotation which highlights and describes the data practices extracted by PI-Extract to help users better understand privacy-policy documents. Our experimental evaluation results show that the annotation significantly improves the users’ reading comprehension of policy texts, as indicated by a 26.6% increase in the average total reading score.
Experimental study of anti-liver fibrosis of Ganlong capsules combined with silymarin in vitro*

Objective To observe the influence of Ganlong capsules combined with silymarin on expression of transforming growth factor beta 1 (TGF-β1) in human hepatic stellate cells, hepatic fibrosis indexes and the antifibrosis effect in vitro. Methods Human hepatic stellate cell line (LX-2) was cultured in vitro . Enzyme-linked immunosorbent assay was used to detect the levels of TGF-β1, hepatic fibrosis indexes type I collagen (Col-I), type III procollagen (PCIII ), type IV collagen (IV-C), laminin (LN) and hyaluronic acid (HA). RT-PCR was used to detect the expressions of Col-I and TIMP-1 mRNAs. Results Compared with the compound silymarin treatment group, the content of TGF-β1 significantly decreased (P < 0.05), the content of LN, HA and Col-II in the LX-2 cells decreased at the 24th and 48th h (P < 0.05), and the content of PCIII and IV-C decreased significantly at the 24th h (P < 0.05)
ReDMArk: Bypassing RDMA Security Mechanisms

State-of-the-art remote direct memory access (RDMA) technologies such as InﬁniBand (IB) or RDMA over Converged Ethernet (RoCE) are becoming widely used in data center applications and are gaining traction in cloud environments. Hence, the security of RDMA architectures is crucial, yet potential security implications of using RDMA communication remain largely unstudied. ReDMArk shows that current security mechanisms of IB-based architectures are insufﬁcient against both in-network attackers and attackers located on end hosts, thus affecting not only secrecy, but also integrity of RDMA applications. We demonstrate multiple vulnerabilities in the design of IB-based architectures and implementations of RDMA-capable network interface cards (RNICs) and exploit those vulnerabilities to enable powerful attacks such as packet injection using impersonation, unauthorized memory access, and Denial-of-Service (DoS) attacks. To thwart the discovered attacks we propose multiple mitigation mechanisms that are deployable in current RDMA networks.
Second-generation uncemented total hip arthroplasty: a minimum 20-year follow-up

Aims We present the clinical and radiological results at a minimum follow-up of 20 years using a second-generation uncemented total hip arthroplasty (THA). These results are compared to our previously published results using a first-generation hip arthroplasty followed for 20 years. Methods A total of 62 uncemented THAs in 60 patients were performed between 1993 and 1994. The titanium femoral component used in all cases was a Taperloc with a reduced distal stem. The acetabular component was a fully porous coated threaded hemispheric titanium shell (T-Tap ST). The outcome of every femoral and acetabular component with regard to retention or revision was determined for all 62 THAs. Complete clinical follow-up at a minimum of 20 years was obtained on every living patient. Radiological follow-up was obtained on all but one. Results Two femoral components (3.2%) required revision. One stem was revised secondary to a periprosthetic fracture one year postoperatively and one was revised for late sepsis. No femoral component was revised for aseptic loosening. Six acetabular components had required revision, five for aseptic loosening. One additional acetabular component was revised for sepsis. Radiologically, all femoral components remained well fixed. One acetabular was judged loose by radiological criteria. The mean Harris Hip Score improved from 46 points (30 to 67) preoperatively to 89 points (78 to 100) at final follow-up. With revision for aseptic loosening as the endpoint, survival of the acetabular component was 95% (95% confidence interval (CI) 90 to 98) at 25 years. Femoral component survival was 100%. Conclusion The most significant finding of this report was the low prevalence of aseptic loosening and revision of the femoral component at a mean follow-up of 22 years. A second important finding was the survival of over 90% of the hemispheric threaded ring acetabular components. While these shells remain controversial, in this series they performed well. Cite this article: Bone Jt Open 2021;2(1):33–39.
Mutation of SPINOPHILIN (PPP1R9B) found in human tumors promotes the tumorigenic and stemness properties of cells

Rationale: SPINOPHILIN (SPN, PPP1R9B) is an important tumor suppressor involved in the progression and malignancy of different tumors depending on its association with protein phosphatase 1 (PP1) and the ability of the PP1-SPN holoenzyme to dephosphorylate retinoblastoma (pRB). Methods: We performed a mutational analysis of SPN in human tumors, focusing on the region of interaction with PP1 and pRB. We explored the effect of the SPN-A566V mutation in an immortalized non-tumorigenic cell line of epithelial breast tissue, MCF10A, and in two different p53-mutated breast cancer cells lines, T47D and MDA-MB-468. Results: We characterized an oncogenic mutation of SPN found in human tumor samples, SPN-A566V, that affects both the SPN-PP1 interaction and its phosphatase activity. The SPN-A566V mutation does not affect the interaction of the PP1-SPN holoenzyme with pocket proteins pRB, p107 and p130, but it affects its ability to dephosphorylate them during G0/G1 and G1, indicating that the PP1-SPN holoenzyme regulates cell cycle progression. SPN-A566V also promoted stemness, establishing a connection between the cell cycle and stem cell biology via pocket proteins and PP1-SPN regulation. However, only cells with both SPN-A566V and mutant p53 have increased tumorigenic and stemness properties. Conclusions: SPN-A566V, or other equivalent mutations, could be late events that promote tumor progression by increasing the CSC pool and, eventually, the malignant behavior of the tumor.
A measure of centrality in cyclic diffusion processes: Walk-betweenness

Unlike many traditional measures of centrality based on paths that do not allow any repeated nodes or lines, we propose a new measure of centrality based on walks, walk-betweenness, that allows any number of repeated nodes or lines. To illustrate the value of walk-betweenness, we examine the transmission of syphilis in Chicago area and the diffusion of microfinance in 43 rural Indian villages. Walk-betweenness allows us to identify hidden bridging communities in Chicago that were essential in the transmission dynamics. We also find that village leaders with high walk-betweenness are more likely to accelerate the rate of microfinance take-up among their followers, outperforming other traditional centrality measures in regression analyses.