Miracle-dz/UniFAR-FTUs huggingface dataset:
Roadmap for the purity determination of pure metallic elements

High purity materials can serve as a realisation of the Systeme International d’Unites (SI) unit amount of substance for the specific element. Solutions prepared from such high purity materials using gravimetric preparation and the concept of molar mass are used as calibration solutions in many fields of analytical chemistry. Calibration solutions prepared this way provide the traceability to the SI and are the metrological basis in elemental analysis. The preparation and characterization of such primary pure substances, representing the realisation of the SI unit amount of substance, is undertaken only by a small number of National Metrology Institutes (NMI) and Designated Institutes (DI). Many other NMIs and DIs, however, prepare elemental calibration solutions as calibrants for their measurement services, such as the certification of matrix Reference Materials or the provision of reference values for Proficiency Testing schemes. The elemental calibration solutions used for this purpose are not a direct service to customers, such as preparing secondary calibration solutions, but provide the source of traceability for the other services. Hence, it is necessary for the NMI or DI to obtain data on the purity of the pure metals or other materials used to prepare the solutions with measurement uncertainties meeting the needs of the above described services. This is commonly undertaken as a “fit for purpose” assessment, appropriate for the uncertainty requirement of the service provided to customers. As a consequence, total purity measurements are a long-term strategy of CCQM-IAWG. Several studies were conducted (CCQM-P107, CCQM-K72 and CCQM-P149) on the measurement of the purity of zinc. From these studies, several conclusions can be drawn for the purity assessment of a pure (metallic) element. These conclusions will be put together in this document in order to assist all NMIs/DIs in performing a purity assessment, whenever needed.
A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study

Using 10-minute wind turbine supervisory control and data acquisition (SCADA) system data to predict faults can be an attractive way of working toward a predictive maintenance strategy without needing to invest in extra hardware. Classification methods have been shown to be effective in this regard, but there have been some common issues in their application within the literature. To use these data-driven methods effectively, historical SCADA data must be accurately labelled with the periods when turbines were down due to faults, as well as with the reason for the fault. This can be manually achieved using maintenance logs, but can be highly tedious and time-consuming due to the often unstructured format in which this information is stored. Alarm systems can also help, but the sheer volume of alarms and false positives generated complicate efforts. Furthermore, a way to implement and evaluate the field deployed system beyond simple classification metrics is needed. In this work, we present a prescribed and reproducible framework for: (i) automatically identifying periods of faulty operation using rules applied to the turbine alarm system; (ii) using this information to perform classification which avoids some of the common pitfalls observed in literature; and (iii) generating alerts based on a sliding window metric to evaluate the performance of the system in a real-world scenario. The framework was applied to a dataset from an operating wind farm and the results show that the system can automatically and accurately label historical stoppages from the alarms data. For fault prediction, classification scores are quite low, with precision of 0.16 and recall of 0.49, but it is envisaged that this can be greatly improved with more training data. Nonetheless, the sliding window metric compensates for the low raw classification scores and shows that 71% of faults can be predicted with an average of 30 h notice, with false alarms being active for 122 h of the year. By adjusting some of the parameters of the fault prediction alerts, the duration of false alarms can be drastically reduced to 2 h, but this also reduces the number of predicted faults to 8%.
Intimate Partner Violence in an Orthodox Jewish Community in the United States: A Qualitative Exploration of Community Members’ Perspectives

This study sought to understand the needs and priorities of the Orthodox Jewish community around intimate partner violence in a medium-sized midwestern city. Twelve Orthodox Jewish rabbis, Rebbetzins, and community members (including survivors) were recruited to participate in in-depth, semistructured interviews. Interviews were analyzed using Dedoose via a mixed content analysis approach. Interviews revealed four themes: (1) Orthodox Gender Norms and Socialization: Risk or Protective Factor? (2) Awareness of DV Services in the Community; (3) Barriers to help-seeking; and (4) Salience of Rabbis and Rebbetzins. Recommendations for culturally congruent programs and practices are discussed.
T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-Up

In this paper, we present an end-to-end approach to generate high-resolution person images conditioned on texts only. State-of-the-art text-to-image generation models are mainly designed for center-object generation, e.g., flowers and birds. Unlike center-placed objects with similar shapes and orientation, person image generation is a more challenging task, for which we observe the followings: 1) the generated images for the same person exhibit visual details with identity-consistency, e.g., identity-related textures/clothes/shoes across the images, and 2) those images should be discriminant for being robust against the inter-person variations caused by visual ambiguities. To address the above challenges, we develop an effective generative model to produce person images with two novel mechanisms. In particular, our first mechanism (called T-Person-GAN-ID) is to integrate the one-stream generator with an identity-preserving network such that the representations of generated data are regularized in their feature space to ensure the identity-consistency. The second mechanism (called T-Person-GAN-ID-MM) is based on the manifold mix-up to produce mixed images via the linear interpolation across generated images from different manifold identities, and we further enforce such interpolated images to be linearly classified in the feature space. This amounts to learning a linear classification boundary that can perfectly separate images from two identities. Our proposed method is empirically validated to achieve a remarkable improvement in text-to-person image generation. Our architecture is orthogonal to StackGAN++ , and focuses on person image generation, with all of them together to enrich the spectrum of GANs for the image generation task. Codes are available on \url{https://github.com/linwu-github/Person-Image-Generation.git}.
InterFace: Adjustable Angular Margin Inter-class Loss for Deep Face Recognition

In the field of face recognition, it is always a hot research topic to improve the loss solution to make the face features extracted by the network have greater discriminative power. Research works in recent years has improved the discriminative power of the face model by normalizing softmax to the cosine space step by step and then adding a fixed penalty margin to reduce the intra-class distance to increase the inter-class distance. Although a great deal of previous work has been done to optimize the boundary penalty to improve the discriminative power of the model, adding a fixed margin penalty to the depth feature and the corresponding weight is not consistent with the pattern of data in the real scenario. To address this issue, in this paper, we propose a novel loss function, InterFace, releasing the constraint of adding a margin penalty only between the depth feature and the corresponding weight to push the separability of classes by adding corresponding margin penalties between the depth features and all weights. To illustrate the advantages of InterFace over a fixed penalty margin, we explained geometrically and comparisons on a set of mainstream benchmarks. From a wider perspective, our InterFace has advanced the state-of-the-art face recognition performance on five out of thirteen mainstream benchmarks. All training codes, pre-trained models, and training logs, are publicly released \footnote{$https://github.com/iamsangmeng/InterFace$}.
Enabling deep reinforcement learning autonomous driving by 3D-LiDAR point clouds

Autonomous driving holds the promise of revolutionizing our lives and society. Robot drivers will run errands such as commuting, parking cars, or taking kids to school. It is expected that, by the mid-century, humans will drive only for their pleasure. Autonomous vehicles will increase the efficiency and safety of the transportation system by reducing accidents and increasing the overall system capacity. Current autonomous driving systems are based on supervised learning that relies on massive, labeled data. It takes a lot of time, resources, and manpower to produce such data sets. While this approach is achieving remarkable results, the required effort to produce data becomes a limiting factor for general driving scenarios. This research explores Reinforcement Learning to advance autonomous driving models without labeled data. Reinforcement Learning is a learning paradigm that uses the concept of rewards to autonomously discover, through trial & error, how to solve a task. This work uses the LiDAR sensor as a case study to explore the effectiveness of Reinforcement Learning in interpreting complex data. LiDARs provide a dynamic high time-space definition map of the environment and it could be one of the key sensors for autonomous driving.
Prophage taming by the adherent-invasive Escherichia coli LF82 upon macrophage infection

Adherent-invasive Escherichia coli (AIEC) strains are frequently recovered from stools of patients with dysbiotic microbiota. They have remarkable properties of adherence to the intestinal epithelium, and survive better than other E. coli in macrophages. The best studied of these AIEC is probably strain LF82, which was isolated from a Crohn’s disease patient. This strain contains five complete prophages, which have not been studied until now. We undertook their analysis, both in vitro and inside macrophages, and show that all of them form virions. The Gally prophage is by far the most active, generating spontaneously over 108 viral particles per mL of culture supernatants in vitro, more than 100-fold higher than the other phages. Gally is over-induced after a genotoxic stress generated by ciprofloxacin and trimethoprim. However, upon macrophage infection, Gally virion production is decreased by more than 20-fold, and the transcription profile of the prophage indicates that part of the structural module is specifically repressed while the replication module is overexpressed compared to unstressed culture conditions. We conclude that strain LF82 has evolved an efficient way to “tame” its most active prophage upon macrophage infection, which may participate to its good survival in macrophages. The results are discussed in light of the active lysogeny process. AUTHOR SUMMARY Prophages are bacterial viruses stably integrated into their host, to which they can provide new functions, thus increasing their fitness in the environment. Thereby, they can participate to the virulence of bacterial pathogens. However, prophages are double-edged swords that can be awakened in response to genotoxic stresses, resulting in the death of their bacterial host. This raises the question of the effect of this type of stress in the natural environments where their bacterial hosts exert their virulence. In this study, we characterized the five active prophages present in Escherichia coli LF82, a strain belonging to the intestinal microbiota and suspected to be involved in Crohn’s disease via its ability to invade macrophages, a highly genotoxic environment. We show that LF82 inhibits the awakening of its prophages in macrophages, allowing it to survive there. Moreover, deletion of its most active prophage does not affect the viability of LF82 in this environment. These results show that LF82 has tamed its prophages in macrophages and also suggest that if they convey fitness advantages, they probably do so in environments differing from macrophages, and which remain to be discovered.
A Relative Value Function Based Learning Beam Search for Longest Common Subsequence Problems

Beam search (BS) is a well-known graph search algorithm frequently used to heuristically ﬁnd good or near-optimal solutions to combinatorial optimization problems in reasonable time. Starting from an initial state r , BS traverses a state graph in a breadth-ﬁrst-search manner seeking a best path from r to a goal state t . To keep the computational eﬀort within limits, BS selects at each level only up to β most promising states to pursue further and discards the others. The selected subset of states is called the beam and parameter β the beam width . To do this selection, each state v obtained at a level is usually evaluated by a function f ( v ) = g ( v ) + h ( v ), where function g ( v ) represents the length of the path from the root state to state v and h ( v ), called the heuristic guidance, is an estimate of the best achievable length-to-go from node v to the goal state. The states with the best values according to this evaluation then form the beam. Clearly, the quality of the solution BS obtains in general depends fundamentally on the heuristic guidance function h . This function needs to be developed carefully for a problem at hand, which is typically a manual and quite time-consuming process requiring problem-speciﬁc knowledge and involving many computational experiments and comparisons of diﬀerent options. We propose to automate this task at least partially by using a machine learning (ML) model as guidance function h , which is trained on a large number of representative randomly generated problem instances in a reinforcement learning manner. The overall approach is inspired by the way Silver et al. [2] mastered chess, shogi, and Go with AlphaZero. In their approach a neural network is trained to predict the outcome of a game state, called value , as well as to provide a policy for the next action to perform, and this network is used within a Monte Carlo Tree Search as guidance. Training samples are obtained by means
Strassen's LIL for the Lorenz Curve

We prove Strassen's law of the iterated logarithm for the Lorenz process assuming that the underlying distribution functionFand its inverseF? 1are continuous, and the momentEX2+? is finite for some? >0. Previous work in this area is based on assuming the existence of the densityf:=F? combined with further assumptions onFandf. Being based only on continuity and moment assumptions, our method of proof is different from that used previously by others, and is mainly based on a limit theorem for the (general) integrated empirical difference process. The obtained result covers all those we are aware of on the LIL problem in this area.
HESS Opinions "A random walk on water"

Abstract. According to the traditional notion of randomness and uncertainty, natural phenomena are separated into two mutually exclusive components, random (or stochastic) and deterministic. Within this dichotomous logic, the deterministic part supposedly represents cause-effect relationships and, thus, is physics and science (the "good"), whereas randomness has little relationship with science and no relationship with understanding (the "evil"). Here I argue that such views should be reconsidered by admitting that uncertainty is an intrinsic property of nature, that causality implies dependence of natural processes in time, thus suggesting predictability, but even the tiniest uncertainty (e.g. in initial conditions) may result in unpredictability after a certain time horizon. On these premises it is possible to shape a consistent stochastic representation of natural processes, in which predictability (suggested by deterministic laws) and unpredictability (randomness) coexist and are not separable or additive components. Deciding which of the two dominates is simply a matter of specifying the time horizon and scale of the prediction. Long horizons of prediction are inevitably associated with high uncertainty, whose quantification relies on the long-term stochastic properties of the processes.
Differences in taxonomic composition of summer phytoplankton in two fjords of West Spitsbergen, Svalbard

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Thin-layer scaling effects on AVO modelling

In AVO modelling with log data, it is a general practice to upscale the log data for reasons such as computational efficiency. In this study, we investigate the AVO sensitivity to thin-layer effects by upscaling log data at different scales. A series of Kennett-based reflectivity modelling exercises are conducted using the resulting elastic ID models. In the presence of thin layers, it is essential to incorporate interbed multiples (including converted wave modes) and therefore, primary-only Zoeppritz modelling could lead to misleading results. On one hand, very fine scale models involve a complex wavefield tuning that renders the theoretical Zoeppritz plane-wave reflection coefficients meaningless. On the other hand, wavefield complexities due to thin-layer multiple scattering also introduce uncertainties in the modelled AVO behaviour. In contrast, with increasing layer scales, Zoeppritz plane-wave reflection coefficients become more applicable, and interbed multiple and tuning effects also have less influence upon the AVO behaviour. At such scales, primary-only AVO modelling by ray tracing where reflection amplitudes are determined by Zoeppritz equations could be an efficient alternative to full-wavefield reflectivity modelling. In our synthetic examples, this occurs at layer scales (d) satisfying a relation to dominant frequency (λ) of λ/d ≤ 4 ~ 5. However, λ and d are interdependent, since the effective velocity used to calculate wavelength λ depends on scale d. To resolve this dilemma in practice, surface seismic data could be analysed and a Q sensitivity analysis may be necessary to determine the dominant wavelength at different target levels.
Decoding the MITRE Engenuity ATT&CK Enterprise Evaluation: An Analysis of EDR Performance in Real-World Environments

Endpoint detection and response (EDR) systems have emerged as a critical component of enterprise security solutions, effectively combating endpoint threats like APT attacks with extended lifecycles. In light of the growing significance of endpoint detection and response (EDR) systems, many cybersecurity providers have developed their own proprietary EDR solutions. It's crucial for users to assess the capabilities of these detection engines to make informed decisions about which products to choose. This is especially urgent given the market's size, which is expected to reach around 3.7 billion dollars by 2023 and is still expanding. MITRE is a leading organization in cyber threat analysis. In 2018, MITRE started to conduct annual APT emulations that cover major EDR vendors worldwide. Indicators include telemetry, detection and blocking capability, etc. Nevertheless, the evaluation results published by MITRE don't contain any further interpretations or suggestions. In this paper, we thoroughly analyzed MITRE evaluation results to gain further insights into real-world EDR systems under test. Specifically, we designed a whole-graph analysis method, which utilizes additional control flow and data flow information to measure the performance of EDR systems. Besides, we analyze MITRE evaluation's results over multiple years from various aspects, including detection coverage, detection confidence, detection modifier, data source, compatibility, etc. Through the above studies, we have compiled a thorough summary of our findings and gained valuable insights from the evaluation results. We believe these summaries and insights can assist researchers, practitioners, and vendors in better understanding the strengths and limitations of mainstream EDR products.
Reviewing the Cost–Benefit Analysis and Multi-Criteria Decision-Making Methods for Evaluating the Effectiveness of Lithium-Ion Batteries in Electric Vehicles

Lithium-ion batteries (LIBs) have a wide range of applications in different fields, starting with electronics and energy storage systems. The potential of LIBs in the transportation sector is high, especially for electric vehicles (EVs). This study aims to investigate the efficiency and effectiveness of, and justification for, the application of LIBs in the field of transport, primarily in EVs. The research focuses on single and multi-criteria evaluations of the efficiency of LIBs. Previous studies in which LIBs were evaluated using cost–benefit analysis (CBA) and multi-criteria decision-making methods (MCDM) were analysed. An electronic literature search of the Web of Science, Scopus, and other relevant databases was performed. The literature was searched using the keywords: “lithium-ion batteries”; “multi-criteria decision-making”; “cost-benefit analysis”; “energy storage”; “vehicles”; “PROMETHEE” (or other MCDM method)”. A total of 40 scientific articles concerning the application of CBA (of which are 20%) and MCDM methods between 1997 and 2023, worldwide, were analysed. The results show multiple applications of both CBA and MCDM methods. The main findings of the areas of application were summarised and future research was discussed.