Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Detecting Anomalies in Log-Event Sequences with Deep Learning: Open Research Challenges

Anomaly Detection (AD) is an important area to reliably detect malicious behavior and attacks on computer systems. Log data is a rich source of information about systems and thus provides a suitable input for AD. With the sheer amount of log data available today, Machine Learning (ML) and its further development Deep Learning (DL) have been applied for years to create models for AD. Especially when processing complex log data, DL is often able to achieve better performance than ML. To detect anomalous patterns that span over multiple log lines, it is necessary to group these log lines into log-event sequences. This work uses a Long Short-Term Memory (LSTM) model for AD which is one of the most important approaches to represent long-range temporal dependencies in log-event sequences of arbitrary length. This means that we use past information to predict whether future events are normal or anomalous. For the LSTM model we adapt a state of the art open source implementation called LogDeep. For the evaluation, we use a Hadoop Distributed File System (HDFS) data set, which is well studied in current research, and an open source Audit data set provided by the Austrian Institute of Technology (AIT). In this paper we show that without padding, a common preprocessing step used that strongly influences the AD process and artificially improves detection results and thus accuracy in lab testing, it is not possible to achieve the same high quality of results shown in literature. Furthermore, we analyze limitations of DL approaches applied for AD and list future research priorities and design challenges.
A higher number of SARS-COV-2 infections in quilombola communities than in the local population in Brazil

The historical and social vulnerability of quilombola communities in Brazil can make them especially fragile in the face of COVID-19, considering that several individuals have precarious health systems and inadequate access to water. This work aimed to characterize the frequency of SARS-COV-2 infections and the presence of IgM and IgG SARS-CoV-2 antibodies in quilombola populations and their relationship with the presence of risk factors or preexisting chronic diseases in the quilombola communities. We analyzed the sociodemographic and clinical characteristics, serological status, comorbidities, and symptoms of 1,994 individuals (478 males and 1,536 females) from 18 Brazilian municipalities in the State of Sergipe of quilombola communities, which were evaluated at different epidemiological weeks, starting at the 32nd (August 6th) and ending at the 40th (October 3rd) epidemiological week. More than 70% of studied families live in rural areas and they have an extreme poverty social status. Although we found a higher number of SARS-COV-2 infections in quilombola communities than in the local population, their SARS-CoV-2 reactivity and IgM and IgG positivity varied across the communities investigated. Arterial hypertension was the most risk factor, being found in 27.8% of the individuals (9.5% in stage 1, 10.8% in stage 2, and 7.5% in stage 3). The most common COVID-19 symptoms and comorbidities were headache, runny nose, flu, and dyslipidemia. However, most individuals were asymptomatic (79.9%). Our data indicate that mass testing must be incorporated into public policy to improve the health care system available to quilombola populations during a future pandemic or epidemic.
Monosomes buffer translational stress to allow for active ribosome elongation

Introduction: The synthesis of proteins is a fundamental process in the life-span of all cells. The activation of ribosomes on transcripts is the starting signal for elongation and, in turn, the translation of an mRNA. Thereby, most mRNAs circulate between single (monosomes) and multi ribosomal particles (polysomes), a process that defines their translational activity. The interplay between monosomes and polysomes is thought to crucially impact translation rate. How monosomes and polysomes are balanced during stress remains, however, elusive. Methods: Here, we set out to investigate the monosome and polysome levels as well as their kinetics under different translational stress conditions including mTOR inhibition, downregulation of the eukaryotic elongation factor 2 (eEF2) and amino acid depletion. Results: By using a timed ribosome runoff approach in combination with polysome profiling, we found that the used translational stressors show very distinct effects on translation. However, they all had in common that the activity of monosomes was preferentially affected. This adaptation seems to be needed for sufficient translation elongation. Even under harsh conditions such as amino acid starvation, we detected active polysomes while monosomes were mostly inactive. Hence, it is plausible that cells compensate the reduced availability of essential factors during stress by adapting the levels of active monosomes to favor sufficient elongation. Discussion: These results suggest that monosome and polysome levels are balanced under stress conditions. Together, our data argue for the existence of translational plasticity that ensure sufficient protein synthesis under stress conditions, a process that is necessary for cell survival and recovery.
Superhumps in AM Canum Venaticorum stars

The AM Canum Venaticorum stars are an in homogeneous group of six stars that are char­ acterised by the absence of hydrogen lines in their spectra. The structure of these stars has been the subject of much debate, and single star models were considered as recently as 1992. The prevailing opinion, however, is that the A. \Jf eVn stars are semi-detached binaries which consist of a low-mass, degenerate helium secondary which is transferring mass via an accretion disc to a DB white dwarf. Under this model, the orbital periods of the AM CVn systems are in the range 1000 3000 s. The AM CVn systems show many of the accretion disc-related phenomena that are observed in non-magnetic (hydrogen-rich) cataclysmic variables. Five of the six systems show low-amplitude optical oscillations with periods between 500 and 2000 s. The oscillations have a complex and variable harmonic structure. Since they are consistent with the predictions of the double-degenerate binary model, the oscillations were assumed to correspond to the orbital period in each system. However, the funda­ mental periods and their harmonics are unstable, showing amplitude and phase drifts on a time scale of weeks to months. The instability of the photometric periods has therefore been an obstacle to accepting that the AM CVn stars are binary systems. An alternative explanation was proposed by O'Donoghue & Kilkenny (1989): the photometric variations in the AM CVn stars are not their orbital periods, but are caused by the same mechanism that produces superhumps in SU UMa dwarf novae. Superhumps are optical variations observed during the superoutbursts of SU UMa stars with periods a few percent in ex­ cess of the orbital period. The superhumps usually disappear as the star declines to quiescence after a superoutburst. Superhumps are thought to be the result of periodic enhancements of tidal dissipation in the outer regions of the accretion disc. In addition
Frequency–wavenumber mapping in turbulent shear flows

Spatial turbulence spectra for high-Reynolds-number shear flows are usually obtained by mapping experimental frequency spectra into wavenumber space using Taylor’s hypothesis, but this is known to be less than ideal. In this paper, we propose a cross-spectral approach that allows us to determine the entire wavenumber–frequency spectrum using two-point measurements. The method uses cross-spectral phase differences between two points – equivalent to wave velocities – to reconstruct the wavenumber–frequency plane, which can then be integrated to obtain the spatial spectrum. We verify the technique on a particle image velocimetry (PIV) data set of a turbulent boundary layer. To show the potential influence of the different mappings, the transfer functions that we obtained from our PIV data are applied to hot-wire data at approximately the same Reynolds number. Comparison of the newly proposed technique with the classic approach based on Taylor’s hypothesis shows that – as expected – Taylor’s hypothesis holds for larger wavenumbers (small spatial scales), but there are significant differences for smaller wavenumbers (large spatial scales). In the range of Reynolds number examined in this study, double-peaked spectra in the outer region of a turbulent wall flow are thought to be the result of using Taylor’s hypothesis. This is consistent with previous studies that focused on examining the limitations of Taylor’s hypothesis (del Álamo & Jiménez, J. Fluid Mech., vol. 640, 2009, pp. 5–26). The newly proposed mapping method provides a data-driven approach to map frequency spectra into wavenumber spectra from two-point measurements and will allow the experimental exploration of spatial spectra in high-Reynolds-number turbulent shear flows.
Research on intelligent measurement system of workpiece outer dimension

In the field of machinery manufacturing, due to the backward detection methods of the outer dimensions of the workpiece, the high labor intensity of the detection and the high production cost, the problems of poor detection accuracy and low efficiency are caused. Therefore, this paper designs an industrial camera-based intelligent detection system for workpiece outer dimensions. The system uses industrial cameras to simulate human vision functions to accurately extract information from workpiece images and convert them into a form that can be understood and calculated by computers. Compared with the traditional manual workpiece external size detection method, the system has the advantages of good positioning flexibility, short sampling period, large amount of information, low cost, good stability, high precision and intelligence, etc., and the system realizes online non-contact detection, which has the characteristics of high accuracy, fast speed, and easy operation; at the same time, the system realizes the real-time measurement of the outer dimensions of the workpiece and the result output, and the detection accuracy is less than 0.01mm. Through the actual production environment test, the system can meet the requirements of rapid and accurate detection of the outer dimensions of the workpiece, and effectively enhance the ability of automatic manufacturing and detection of products.
Efficient multi-dimensional dynamic aggregation scheme for location security

The statistics on demand of customer is very important for business, such as collecting location information of customers to provide them with various services. In this paper, we present a new method for customers to authenticate their identity and position without revealing them and statistics the distribution of passenger flow in different periods, where the purpose of privacy-preserving could be achieved. Due to the identify of customers changes rapidly, in order to ensure that the identify of customers can be updated in time, this paper use dynamic group signature method to add and delete identify frequently. In the meantime, customers need to be divided into different types to meet the needs of different business. Therefore, we will use customers' age and occupation as their identify marks. In order to understand the needs of customers of different ID, age and occupations that we statistics the identity to help business find the suitable development areas.
Two-stage attentional temporal convolution and LSTM model for financial data forecasting

Financial time series usually consist of multiple time series, and financial time series data forecasting models use the historical data plays of multiple driving series to predict the future values of the target series. In recent years, attention-based Long and Short-Term Memory (LSTM) neural networks and Temporal Convolutional Networks (TCN) have been widely used in time series forecasting. In this paper, we propose a two-stage attention-based TCN and LSTM hybrid forecasting model, in order to better obtain the spatial correlation of driving sequences, we used causal self-attention to obtain the spatial attention weights of driving sequences, then use TCN to extract the short-term features of the series in the first stage, in the second stage, adding the temporal attention module computes the sequence adaptively assigning weights to the input sequence for the current and historical moments, and finally use LSTM to capture the long-term dependence of the time-series data. We used the NASDAQ 100 stock dataset and the financial time series of CSI 300 companies to measure the performance of the proposed model in financial data forecasting.
Four-channel convolutional Chinese handwriting recognition based on MobileNetV2

Handwriting Chinese Character Recognition (HCCR) is the foundation of document digitization. It is a challenging subject in the field of image classification and recognition for a series of reasons such as the large number of Chinese characters, the diversification of writing style and numerous similar characters. To solve the above problems, this paper designs a four-channel convolution recognition model based on MobileNetV2. First, the input image is sent to four-channel convolution with different receptive fields, and feature maps of different scales are extracted respectively to improve the accuracy of the model. Then the feature maps are combined to enrich the diversity of features. Afterwards, the combined features are weighted by SE Block, and more useful feature maps are screened by this means to accelerate the model convergence. Finally, the lightweight network mobilenetv2 is used to classify the weighted features. The experimental results show that the recognition accuracy of the four-channel convolution recognition model based on mobilenetv2 on the offline handwritten Chinese character set CASIA-HWDB1.1 has reached 96.05%, and the convergence speed of the model is extremely fast. Also, the memory occupation and parameter quantity are far lower than other Chinese handwriting character recognition models.
Eliminating nonrenormalizability helps prove scaled affine quantization of φ44 is nontrivial

Following a modest comparison between canonical and affine quantization, which points to positive features in the affine procedures. We prove through Monte Carlo analysis that the covariant Euclidean scalar field theory, [Formula: see text], where [Formula: see text] denotes the power of the interaction term and [Formula: see text] where [Formula: see text] is the spatial dimension and 1 adds imaginary time, such that [Formula: see text] can be acceptably quantized using scaled affine quantization and the resulting theory is nontrivial, unlike what happens using canonical quantization.
A custom-made weight-drop impactor to produce consistent spinal cord injury outcomes in a rat model

Abstract Objective The main objective of this study is to design a custom-made weight-drop impactor device to produce a consistent spinal cord contusion model in rats in order to examine the efficacy of potential therapies for post-traumatic spinal cord injuries (SCIs). Methods Adult female Sprague-Dawley rats (n = 24, 11 weeks old) were randomly divided equally into two groups: sham and injured. The consistent injury pattern was produced by a 10 g stainless steel rod dropped from a height of 30 mm to cause (0.75 mm) intended displacement to the dorsal surface of spinal cord. The neurological functional outcomes were assessed at different time intervals using the following standardized neurobehavioral tests: Basso, Beattie, and Bresnahan (BBB) scores, BBB open-field locomotion test, Louisville Swim Scale (LSS), and CatWalk gait analysis system. Results Hind limb functional parameters between the two groups using BBB scores and LSS were significantly different (p < 0.05). There were significant differences (p < 0.05) between the SCI group and the sham group for the hind limb functional parameters using the CatWalk gait analysis. Conclusion We developed an inexpensive custom-made SCI device that yields a precise adjustment of the height and displacement of the impact relative to the spinal cord surface.
Equations aux dérivées partielles et aléas

Dans cette these, on a d’abord considere une equation d'onde. On a premierement montre que l’equation est bien-posee presque sure par la methode de decomposition de frequence de Bourgain sous l’hypothese de regularite que s > 2(p−3)/(p-1). Ensuite, nous avons reduit de cette exigence de regulation a (p-3)/(p−1) en appelant une estimation probabiliste a priori. Nous considerons egalement l’approximation des solutions obtenues ci-dessus par des solutions lisses et la stabilite de cette procedure d’approximation. Et nous avons conclu que l’equation est partout mal-posee dans le regime de super-critique. Nous avons considere ensuite l’equation du faisceau quintique sur le tore 3D. Et nous avons montre que cette equation est presque sur bien-posee globalement dans certain regimes de super-critique. Enfin, nous avons prouve que la mesure de l’image de la mesure gaussienne sous l’application de flot de l’equation BBM generalise satisfait une inegalite de type log-Sobolev avec une petit peu de perte de l’integrabilite.
Activity Recommendation for Business Process Modeling with Pre-trained Language Models

Activity recommendation in business process modeling is concerned with suggesting suitable labels for a new activity inserted by a modeler in a process model under development. Recently, it has been proposed to represent process model repositories as knowledge graphs, which makes it possible to address the activity-recommendation problem as a knowledge graph completion task. However, existing recommendation approaches are entirely dependent on the knowledge contained in the model repository used for training. This makes them rigid in general and even inapplicable in situations where a process model consists of un-seen activities, which were not part of the repository used for training. In this paper, we avoid these issues by recognizing that the semantics contained in process models can be used to instead pose the activity-recommendation problem as a set of textual sequence-to-sequence tasks. This enables the application of transfer-learning techniques from natural language processing, which allows for recommendations that go beyond the activities contained in an available repository. We operationalize this with an activity-recommendation approach that employs a pre-trained language model at its core, and uses the representations of process knowledge as structured graphs combined with the natural-language-based semantics of process models. In an experimental evaluation, we show that our approach considerably outperforms the state of the art in terms of semantic accuracy of the recommendations and that it is able to recommend and handle activity labels that go beyond the vocabulary of the model repository used during training.
An image is worth a thousand species: mapping plant biodiversity with citizen science, remote sensing, and deep learning

Anthropogenic habitat destruction and climate change are altering the composition of plant communities worldwide1,2. However, traditional species distribution models cannot detect rapid, local plant species changes due to their low spatial and temporal resolution3,4, and remote sensing models can only identify changes in coarse vegetation categories5,6. Here we combine open-access remote sensing imagery, citizen science observations, and deep learning to create a multi-species prediction model at high spatial and temporal resolution. We train a novel deep convolutional neural network using ∼half a million observations within California to simultaneously predict the presence of over 2,000 plant species at meter-level resolution. This model—deepbiosphere—accurately performs many key biodiversity monitoring tasks, from fine-mapping geographic distributions of individual species and communities, to detecting rapid plant community changes in space and time. Deepbiosphere shifts the paradigm for species distribution modeling, providing a roadmap for inexpensive, automatic, and scalable detection of anthropogenic impacts on species worldwide.
Morpho-physiobiochemical dissection reveals insight into salt-induced differential responses in genetically modified Solanum melongena L. (Bt Brinjal) varieties using an indigenous hydroponic system

Salinity is a major abiotic constraint of crop production in many countries, including Bangladesh, where a significant amount of cultivable areas are diversely affected by rising salt concentrations. Therefore, it is of paramount importance to figure out the possible solutions to cope with this abiotic stress. So, the development of tolerant genotypes of various crop species can be the best alternative to enhance crop production as well as to improve the livelihoods of poor marginal farmers. With this in mind, the impact of different NaCl levels (50 mM, 100 mM, and 150 mM) on four different Bt Brinjal (Bacillus thuringiensis) genotypes (BARI Bt Begun-1, BARI Bt Begun-2, BARI Bt Begun-3, and BARI Bt Begun-4) was evaluated using morpho-physicochemical analyses at growth, harvesting, and postharvest stages by establishing a new indigenous cost-effective hydroponic system. Our results show that excess salt (> 100 mM) has a detrimental effect on plant growth and development and most of the traits measured across different growth stages. Based on the different measured traits, BARI Bt Begun-1 and BARI Bt Begun-2 varieties outperformed in terms of better morpho-physiological, biochemical, photosynthetic, and antioxidant capacity under salt stress when compared to BARI Bt Begun-3 and BARI Bt Begun-4. Therefore, we conclude that BARI Bt Begun-1 and BARI Bt Begun-2 are moderately tolerant varieties, while BARI Bt Begun-3 and Begun-4 were susceptible varieties to salinity stress. The identified salt-responsive contrasting varieties can serve as valuable genetic materials in comparative genomics for breeding salt-tolerant Brinjal varieties and the newly established hydroponic system could be utilized in translational research programs.
Setdb1-loss induces type-I interferons and immune clearance of melanoma

Despite recent advances in the treatment of melanoma, many patients with metastatic disease still succumb to their disease. To identify tumor-intrinsic modulators of immunity to melanoma, we performed a whole-genome CRISPR screen in melanoma and identified multiple components of the HUSH complex, including Setdb1, as hits. We found that loss of Setdb1 leads to increased immunogenicity and complete tumor clearance in a CD8+ T-cell dependent manner. Mechanistically, loss of Setdb1 causes de-repression of endogenous retroviruses (ERVs) in melanoma cells and triggers tumor-cell intrinsic type-I interferon signaling, upregulation of MHC-I expression, and increased CD8+ T-cell infiltration. Furthermore, spontaneous immune clearance observed in Setdb1-/- tumors results in subsequent protection from other ERV-expressing tumor lines, supporting the functional anti-tumor role of ERV-specific CD8+ T-cells found in the Setdb1-/- microenvironment. Blocking the type-I interferon receptor in mice grafted with Setdb1-/- tumors decreases immunogenicity by decreasing MHC-I expression, leading to decreased T-cell infiltration and increased melanoma growth comparable to Setdb1wt tumors. Together, these results indicate a critical role for Setdb1 and type-I interferons in generating an inflamed tumor microenvironment, and potentiating tumor-cell intrinsic immunogenicity in melanoma. This study further emphasizes regulators of ERV expression and type-I interferon expression as potential therapeutic targets for augmenting anti-cancer immune responses.