Miracle-dz/UniFAR-FTUs huggingface dataset:
A Lightweight Anomaly Detection Approach in Large Logs Using Generalizable Automata

As software systems become complex, there is a need for monitoring techniques that can detect anomalies and other undesired behaviours that may occur during system operations. Performance issues, design bugs, or security attacks can cause anomalies. Their detection can not only improve the overall quality of a system, but also enhance users’ confidence in the system. In this thesis, we focus on the problem of detecting anomalies in large log data. Logs are generated at runtime and contain a wealth of information, useful for various software engineering tasks, including debugging, performance analysis, and fault diagnosis. Our anomaly detection approach is based on the multiresolution abnormal trace detection algorithm proposed in the literature. The algorithm exploits the causal relationship of events in large execution traces to build a model that represents the normal behaviour of a system using varying length n-grams and a generalizable automaton. The resulting model is later used to detect deviations from normalcy. In this thesis, we investigate the application of this algorithm in detecting anomalies in log data. Logs and execution traces are different. Unlike traces, logs do not exhibit a causal relationship among their events, raising questions as to the effectiveness of automata to model log data for anomaly detection. Logs are unstructured data and hence require the use of parsing and abstraction techniques. We propose a process, called LogAutomata, which uses the multiresolution abnormal trace detection algorithm as its primary mechanism. When applying LogAutomata to a large log file generated from the execution of Hadoop Distributed File System (HDFS), we show that the multiresolution algorithm can be a very effective way to detect anomalies in log data.
He star evolutionary channel to intermediate-mass binary pulsar PSR J1802-2124

The intermediate-mass binary pulsars (IMBPs) are characterized by relatively long spin periods (10 - 200 ms) and massive ($\ga 0.4 M_{\odot}$) white dwarf (WD) companions. Recently, precise mass measurements have been performed for the pulsar and the WD in the IMBP PSR J1802-2124. Some observed properties, such as the low mass of the pulsar, the high mass of the WD, the moderately long spin period, and the tight orbit, imply that this system has undergone a peculiar formation mechanism. In this work, we attempt to simulate the detailed evolutionary history of PSR J1802-2124. We propose that a binary system consisting of a neutron star (NS, of mass $1.3 M_{\odot}$) and an He star (of mass $1.0 M_{\odot}$), and with an initial orbital period of 0.5 d, may have been the progenitor of PSR J1802-2124. Once the He star overflows its Roche lobe, He-rich material is transferred onto the NS at a relatively high rate of $\sim 10^{-7}-10^{-6} M_{\odot}\,\rm yr^{-1}$, which is significantly higher than the Eddington accretion rate. A large amount of the transferred material is ejected from the vicinity of the NS by radiation pressure and results in the birth of a mildly recycled pulsar. Our simulated results are consistent with the observed parameters of PSR J1802-2124. Therefore, we argue that the NS + He star evolutionary channel may be responsible for the formation of most IMBPs with orbital periods $\la 3 \rm d$.
Excitation of heavy hydrogenlike ions by light atoms in relativistic collisions with large momentum transfers

We present a theory for excitation of heavy hydrogen-like projectile-ions by light target-atoms in collisions where the momentum transfers to the atom are very large on the atomic scale. It is shown that in this process the electrons and the nucleus of the atom behave as (quasi-) free particles with respect to each other and that their motion is governed by the field of the nucleus of the ion. The effect of this field on the atomic particles can be crucial for the contribution to the excitation of the ion caused by the electrons of the atom. Due to comparatively very large nuclear mass, however, this field can be neglected in the calculation of the contribution to the excitation due to the nucleus of the atom.
Effect of in-Mold Electromagnetic Stirring on Solidification and Inclusion Behavior

A three-dimensional transient model of in-mold electromagnetic stirring in bloom continuous casting mold has been developed. Time-varying electromagnetic field has been coupled with fluid flow, solidification and inclusion tracking model to investigate the effect of electromagnetic stirring on solidification and non-metallic inclusion behavior. The realizable k- ε turbulence model and the enthalpy-porosity approach have been used to investigate the fluid flow and the solidification of liquid steel in the mold. The behavior of non-metallic inclusions is analyzed using the stochastic tracking model. Electromagnetic stirring inhibits initial solid shell formation at the mold wall and turns the liquid core at the strand's center into a liquid-solid interface mush. Analysis has also been done on the impact of inclusion size and density on the removal rate of non-metallic inclusions. The findings indicate that inclusion and solidification behavior is significantly influenced by in-mold EMS.
Deep CO Observations and the CO-to-H2 Conversion Factor in DDO 154, a Low Metallicity Dwarf Irregular Galaxy

We present a deep spectroscopic search for CO emission in the dwarf irregular galaxy DDO 154, which has an Oxygen abundance of only 1=20 of the solar value. The observations were conducted in order to constrain the CO-to-H2 conversion factor at low metallicity. No CO was detected, however, despite being one of the sensitive observations done towards galaxies of this type. We succeed in putting a strong lower limit on the conversion factor, at least 10-times the Galactic value. Our result supports previous studies that argue for a high conversion factor at low metallicity.
Racial Difference in Retail Prices Paid * Preliminary and incomplete. Please do not cite or circulate

We document racial differences in retail prices paid for physically identical products. Black households pay 2.4 percent higher prices than white households, and Hispanic households pay 0.8 percent higher prices. This difference suggest that conventional measures of racial income differences understate real racial income inequality. The racial price gap is not explained by differences in household income, composition, or education. Instead three factors explain the entirety of the racial price gap: black and Hispanic households buy smaller packages with higher unit prices, benefit less from coupons, and live in high price zip codes. We find suggestive evidence that carrying and transportation costs, not supermarket access, are important underlying factors.
An Intelligent Approach to Develop, Assess and Optimize Energy Consumption Models for Air-Cooled Chillers using Machine Learning Algorithms

: The building sector accounts for more than 70% of the total electricity use. Chillers consume more than 50% of electrical energy during seasonal periods of building use. With the growth of the building sector and climate change, it's essential to develop energy-efficient HVAC systems that optimize the ever-increasing energy demand. This study aims to develop an energy consumption prediction model for air-cooled chillers using machine learning algorithms. This is done by developing different static and dynamic data-driven regressive and neural network models and comparing the accuracy of their prediction to identify the most accurate modeling algorithm using 3 main inputs chilled water return temperature, outside drybulb temperature, and cooling load. The proposed model structure was then optimized in terms of the number of neurons, epochs, time delays as well as the number of input variables using a genetic algorithm. Training and testing were done using real data obtained from a fully instrumented 4-ton air-cooled chiller. Results of the study show that the optimized artificial neural network model can predict energy consumption with a high level of accuracy compared to conventional modeling techniques. The development of highly accurate self-tuning models can be a powerful tool to use for other applications such as fault detection and diagnosis, assessment, and system optimization. Further studies are necessary to evaluate the effectiveness of using deep learning algorithms with more hidden layers and cross-validation techniques.
Biological damage due to photospheric, chromospheric and flare radiation in the environments of main-sequence stars

Abstract We explore the biological damage initiated in the environments of F, G, K, and M-type main-sequence stars due to photospheric, chromospheric and flare radiation. The amount of chromospheric radiation is, in a statistical sense, directly coupled to the stellar age as well as the presence of significant stellar magnetic fields and dynamo activity. With respect to photospheric radiation, we also consider detailed synthetic models, taking into account millions or hundred of millions of lines for atoms and molecules. Chromospheric UV radiation is increased in young stars in regard to all stellar spectral types. Flare activity is most pronounced in K and M-type stars, which also has the potential of stripping the planetary atmospheres of close-in planets, including planets located in the stellar habitable zone. For our studies, we take DNA as a proxy for carbon-based macromolecules, guided by the paradigm that carbon might constitute the biochemical centerpiece of extraterrestrial life forms. Planetary atmospheric attenuation is considered in an approximate manner.
Multivariate lookups based on logarithmic derivatives

Logarithmic derivatives translate products of linear factors into sums of their reciprocals, turning zeroes into simple poles of same multiplicity. Based on this simple fact, we construct an interactive oracle proof for lookups over the boolean hypercube, which makes use of a single multiplicity function instead of working with a rearranged union of table and witnesses, as Plookup [GW20] does. For single-column lookups the performance is comparable to Plookup, taken to the multivariate setting by Hyperplonk+ [CBBZ22]. However, the real power of our argument unfolds in the case of “batch-column” lookups, where multiple columns are subject to the same table lookup: While the number of ﬁeld operations is comparable to the Plookup strategy, the oracles provided by our prover are signiﬁcantly fewer. For example, given M = 20 columns of length between 2 12 and 2 18 , and assuming a commitment scheme over an elliptic curve with a 256 bit large base ﬁeld, our paper-pencil operation counts indicate that the logarithmic derivative lookup is more than 3 . 9 times faster.
Bayesian Graduation of Mortality Rates: an application to mathematical reserve evaluation.

: This paper presents Bayesian graduation models of mortality rates, using Markov chain Monte Carlo (MCMC) techniques. Graduated annual death probabilities are estimated through the predictive distribution of the number of deaths, which is assumed to follow a Poisson process, considering that all individuals in the same age class die independently and with the same probability. The resulting mortality tables are formulated through dynamic Bayesian models. The best such mortality table is then compared with known tables from abroad used by the market in Brazil. Calculation of adequate reserve levels is exemplified, via MCMC, making use of the value at risk concept, demonstrating the importance of using “true” observed mortality figures for the population exposed to risk in determining the survival coverage rate.
Comments on: "Echoes from the abyss: Evidence for Planck-scale structure at black hole horizons"

Recently, Abedi, Dykaar and Afshordi claimed evidence for a repeating damped echo signal following the binary black hole merger gravitational-wave events recorded in the first observational period of the Advanced LIGO interferometers. We discuss the methods of data analysis and significance estimation leading to this claim, and identify several important shortcomings. We conclude that their analysis does not provide significant observational evidence for the existence of Planck-scale structure at black hole horizons, and suggest renewed analysis correcting for these shortcomings.
Crop Recognition Using Deep Learning Techniques

Crop recognition from images or videos is a demanding and challenging task due to manifoldness in the plant’s diversity, background noises, variation in inclination of plants etc. Recognition of crops is the most effective solution to overcome botanical taxonomy gap, it also helps the government in identifying crops and their predicted cultivated amount at the end of season. This paper suggests deep learning based methods for crop recognition using Deep Convolutional Neural Networks. Transfer learning is implemented to achieve better results as it is more effective in real world scenarios because it uses pre-trained CNNs model which is trained on large datasets such as Image Net dataset and thus achieves top accuracy. Three pre-trained models and one custom model are implemented and evaluated on different metrics in order to get the best optimal model to correctly recognise crops from images which can be used in developed web-apps to provide solutions at end user level.
A 24.9~34.5 GHz Injection-Locked Frequency Tripler Based on Top Differential Injection of Resonator

This paper proposes a broadband injection locking scheme to solve the problem of narrow locking bandwidth of traditional injection-locked frequency Tripler (ILFT). The scheme utilizes the differential injection technology to improve the even-order harmonics rejection ratio and injection signal current. The top injection technology of resonant tank based on transformer coupling is adopted to avoid the loop gain deterioration with using traditional source nodes injection and extend the locking bandwidth. The circuit is designed in a 55-nm CMOS process. The simulation results show that a 24.9~ 34.5 GHz (32.3% bandwidth) locking range has been achieved with the condition of lv supply voltage and 5dBm injected power. The DC power consumption of the core circuit of ILFT is 5. 07mW. The first, second, and fourth harmonic rejection ratio in the lock center is greater than 40 dBc.
Rotation in NGC 2264: a study based on CoRoT photometric observations

Rotation is one of the key stellar parameters which undergo substantial evolution during the stellar lifetime, in particular during the early stages. Stellar rotational periods can be determined on the basis of the periodic modulation of starlight produced by non-uniformities on the surface of the stars, due to manifestation of stellar activity. We present the results of an extensive search for rotational periods among NGC 2264 cluster members, based on photometric monitoring using the CoRoT satellite, with a particular attention to the distribution of classical and weak-line T-Tauri stars. NGC 2264 is one of the nearest and best studied star forming region in the solar neighbourhood, with an estimated age of 3 Myr, and is the object of a recent simultaneous multiband campaign including a new CoRoT observation with the aim to assess the physical origin of the observed variability. We find that the rotational distributions of classical and weak-line T-Tauri star are different, suggesting a difference in the rotational properties of accreting and non-accreting stars.
New brown dwarf candidates in the Pleiades

We have performed deep, wide-field imaging on a ∼0.4 deg2 field in the Pleiades (Melotte 22). The selected field was not yet target of a deep search for low mass stars and brown dwarfs. Our limiting magnitudes are R ∼ 22 mag and I ∼ 20 mag, sufficient to detect brown dwarf candidates down to 40 MJ. We found 197 objects, whose location in the (I, R – I) color magnitude diagram is consistent with the age and the distance of the Pleiades. Using CTK R and I as well as JHK photometry from our data and the 2MASS survey we were able to identify 7 new brown dwarf candidates. We present our data reduction technique, which enables us to resample, calibrate, and co-add many images by just two steps. We estimate the interstellar extinction and the spectral type from our optical and the NIR data using a two-dimensional χ2 fitting (© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Probing the Mass and Structure of the Ring Nebula in Lyra with SOFIA/GREAT Observations of the [CII] 158 micron line

We have obtained new velocity-resolved spectra of the [CII] 158 micron line towards the Ring Nebula in Lyra (NGC 6720), one of the best-studied planetary nebulae, in order to probe its controversial 3-dimensional structure and to estimate the mass of circumstellar material in this object. We used the Terahertz receiver GREAT aboard the SOFIA airborne telescope to obtain the [CII] spectra at eight locations within and outside the bright optical ring of NGC 6720. Emission was detected at all positions except for the most distant position along the nebula's minor axis, and generally covers a broad velocity range, ~50 km/s (FWZI), except at a position along the major axis located just outside the optical ring, where it is significantly narrower (~25 km/s). The one narrow spectrum appears to be probing circumstellar material lying outside the main nebular shell that has not been accelerated by past fast wind episodes from the central star, and therefore most likely comes from equatorial and/or low-latitude regions of this bipolar nebula. Along lines-of-sight passing within about 10 arcsec of the nebular center, the CII column density is a factor 46 higher than the CO column density. The total mass of gas associated with the [CII] emission inside a circular region of diameter 87.5 arcsec is at least 0.11 solar masses. A significant amount of [CII] flux arises from a photodissociation region immediately outside the bright optical ring, where we find a CII to CO ratio of 6.5, lower than that seen towards the central region. Comparing our data with lower-quality CI spectra, which indicate similarly large CI/CO ratios in NGC 6720, we conclude that the bulk of elemental carbon in NGC 6720 is divided roughly equally between CII and CI, and that the emissions from these species are far more robust tracers of circumstellar material than CO in this object and other evolved planetary nebulae.
PROPERTIES OF THE COMPACT H ii REGION COMPLEX G-0.02-0.07

We present new extinction maps and high-resolution Paschen-alpha images of G-0.02-0.07, a complex of compact H ii regions located adjacent to the M-0.02-0.07 giant molecular cloud, 6 pc in projection from the center of the Galaxy. These H ii regions, which lie in projection just outside the boundary of the Sgr A East supernova remnant, represent one of the most recent episodes of star formation in the central parsecs of the Galaxy. The 1.87 μm extinctions of regions A, B, and C are almost identical, approximately 3.7 mag. Region D, in contrast, has a peak extinction of A1.87 = 5.9 mag. Adopting an extinction law specific to the Galactic center, we find that these extinctions correspond to visual extinctions of AV = 45 and AV = 71. The similar and uniform extinctions of regions A, B, and C are consistent with that expected for foreground extinction in the direction of the Galactic center, suggesting that they lie at the front side of the M-0.02-0.07 molecular cloud. Region D is more compact, has a higher extinction, and is thus suspected to be younger and embedded in a dense core in a compressed ridge on the western edge of this cloud.
The Orbital Ephemeris of the Classical Nova RR Pictoris: Presence of a Third Body?

The ex-nova RR Pic presents a periodic hump in its light curve which is considered to refer to its orbital period. By analyzing all available epochs of these hump maxima in the literature and then combining them with those from new light curves obtained in 2013 and 2014, we establish an unique cycle count scheme valid during the past 50 years and derive an ephemeris with the orbital period 0.145025959(15) days. The O—C diagram of this linear ephemeris reveals systematic deviations that could have different causes. One of them could be a light-travel-time effect caused by the presence of a hypothetical third body near the star/brown dwarf mass limit, with an orbital period of the order of 70 years. We also examine the difficulty of the problematic of detecting substellar or planetary companions of close red-dwarf white-dwarf binaries (including cataclysmic variables) and discuss other possible mechanisms responsible for the observed deviations in O—C. For RR Pic, we propose strategies to solve this question by new observations.
THE RATE OF CORE COLLAPSE SUPERNOVAE TO REDSHIFT 2.5 FROM THE CANDELS AND CLASH SUPERNOVA SURVEYS

The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey and Cluster Lensing And Supernova survey with Hubble multi-cycle treasury programs with the Hubble Space Telescope (HST) have provided new opportunities to probe the rate of core-collapse supernovae (CCSNe) at high redshift, now extending to z ≈ 2.5 . > Here we use a sample of approximately 44 CCSNe to determine volumetric rates, RCC, in six redshift bins in the range 0.1 < z < 2.5 . > Together with rates from our previous HST program, and rates from the literature, we trace a more complete history of R CC ( z ) , ? > with R CC = 0.72 ± 0.06 ? > yr−1 Mpc−3 10−4 h 70 3 ? > at z < 0.08 , ? > and increasing to 3.7 − 1.6 + 3.1 ? > yr−1 Mpc−3 10−4 h 70 3 ? > to z ≈ 2.0 . > The statistical precision in each bin is several factors better than than the systematic error, with significant contributions from host extinction, and average peak absolute magnitudes of the assumed luminosity functions for CCSN types. Assuming negligible time delays from stellar formation to explosion, we find these composite CCSN rates to be in excellent agreement with cosmic star formation rate density (SFRs) derived largely from dust-corrected rest-frame UV emission, with a scaling factor of k = 0.0091 ± 0.0017 M ⊙ − 1 , ? > and inconsistent (to > 95 % ? > confidence) with SFRs from IR luminous galaxies, or with SFR models that include simple evolution in the initial mass function over time. This scaling factor is expected if the fraction of the IMF contributing to CCSN progenitors is in the 8–50 M⊙ range. It is not supportive, however, of an upper mass limit for progenitors at < 20 M ⊙ . >
Artificial Intelligence Assisted Optimization Under Uncertainty for Robust Solutions

Uncertainty in process conditions makes the optimal solution from the deterministic optimization case infeasible or suboptimal. This mandates a special treatment for process uncertainty. The advent of unsupervised machine learning technique like clustering can be used to address this problem. Data arising from process conditions is meager and, to perform robust optimization, it is necessary to have enough representation of the uncertain region. For this, the collected data from process conditions is clustered using Support Vector Clustering (SVC). The hyper-parameters of SVC play a crucial role in clustering exercise. To promote this, a novel algorithm for determining optimal SVC models, is proposed. The proposed algorithm is used to cluster the uncertain process data and once clustered, can be used to sample additional data points inside each cluster using the convex hull. The additionally sampled data points in each cluster increases the cluster representativeness. The proposed methodology is implemented on industrial grinding circuits for performing Optimization Under Uncertainty (OUU) for the worst-case scenario and tested against the budget-uncertainty set based OUU.