Miracle-dz/UniFAR-FTUs huggingface dataset:
An experimental comparison of software-based power meters: focus on CPU and GPU

The global energy demand for digital activities is constantly growing. Computing nodes and cloud services are at the heart of these activities. Understanding their energy consumption is an important step towards reducing it. On one hand, physical power meters are very accurate in measuring energy but they are expensive, difficult to deploy on a large scale, and are not able to provide measurements at the service level. On the other hand, power models and vendor-specific internal interfaces are already available or can be implemented on existing systems. Plenty of tools, called software-based power meters, have been developed around the concepts of power models and internal interfaces, in order to report the power consumption at levels ranging from the whole computing node to applications and services. However, we have found that it can be difficult to choose the right tool for a specific need. In this work, we qualitatively and experimentally compare several software-based power meters able to deal with CPU or GPU-based infrastructures. For this purpose, we evaluate them against high-precision physical power meters while executing various intensive workloads. We extend this empirical study to highlight the strengths and limitations of each software-based power meter.
Optimization and Cavitation Analysis of Centrifugal Pump with Variable Working Parameters Using Ansys CFX

---------------------------------------------------------------------***---------------------------------------------------------------------Abstract – Centrifugal pumps are widely used in a variety of applications. However, their performance can be affected by a number of factors, including rotation speed (RPM), mass flow rate, blade number, and blade geometry. This study used computational fluid dynamics (CFD) to investigate the impact of these factors on the internal flow behavior of a centrifugal pump. The results showed that increasing the RPM led to higher output pressure, power output, and head rise, but also decreased efficiency due to heightened turbulence and frictional losses. The mass flow rate was also found to be a critical factor, with excessive flow rates leading to overloading and power losses, while insufficient flow rates resulted in diminished performance and overall efficiency. The blade number and geometry were also found to have a significant impact on flow behavior. Initially, a higher blade number enhanced efficiency by facilitating more efficient power transfer to the fluid. However, beyond a threshold of eight blades, diminishing returns were observed due to complex flow patterns and increased losses. Notably, blade geometry played a pivotal role, with the outlet blade angle exhibiting substantial influence on parameters such as efficiency and output pressure, while the inlet blade angle had limited significance. Overall, this study provides valuable insights into the internal flow characteristics of centrifugal pumps. The findings of this study have a number of implications for the design and operation of centrifugal pumps and could also be used to improve efficiency and reliability in practical applications. The results could be used to design pumps that are more resistant to cavitation and also to develop new methods for optimizing the performance of centrifugal pumps
The automatic hierarchical decomposition of images into sub-images for use in image recognition and classification

This thesis addresses the problem of image recognition and classification with the aim of developing a generic framework applicable to a wide range of image types and recognition problems. The approach is to use an algorithm, or set of algorithms, to automatically infer and extract structure which possibly corresponds to meaningful objects. Extracted structure is examined for further structure, hence providing a hierarchical process. The investigated framework, referred to as the UpWrite, consists in its ideal form of localisation, amalgamation and representation phases. Initially, an image is examined locally to extract features which are used as low level primitives. These are analysed using one or several algorithms and amalgamated into higher level entities based on structure. The entities, possibly corresponding to meaningful objects, are represented in a chosen standard form. They may be classified or analysed locally in this new form for amalgamation and representation at an increasingly higher level of abstraction. Hence, the process is hierarchical, transforming numerous low level primitives into increasingly higher level objects. It is also intended to be generic, making few assumptions without image specific parameters, and while being automated, also allow a human to determine why a particular result was produced. The application of the framework is demonstrated on three restricted decomposition problems. Anomaly detection: Grey scale images containing a few isolated defects or anomalous regions on an otherwise homogeneous background are investigated. With the focus on painted steel images containing subtle surface defects, the background is successfully identified and represented. The isolated anomalies are subsequently investigated by locally analysing the corresponding feature vectors. Extracted structure is sufficient to classify images by defect type. Grey scale newspaper segmentation: The second restricted problem aims to decompose grey scale newspaper images into regions corresponding to background, pictures and text. Using the same feature extraction framework applied to anomaly detection, and identifying multiple objects rather than one single homogeneous background, the image is segmented. Results are very good, although subjective, across all images examined. The method compares favourably to segmentation using Gabor filters based on region prototypes. Coloured natural scenes: Finally, the less restricted problem of segmenting coloured natural scenes is examined. Using the same type of low level primitives with the inclusion of a novel colour representation, a region growing segmentation algorithm amalgamates local features into spatially disjoint regions. The representations of these regions are used as predictors to amalgamate objects consisting of disjoint segments. Classification of 1000 images from 11 categories yields
Framework for Recasting Table-to-Text Generation Data for Tabular Inference

Prior work on constructing challenging tabular inference data centered primarily on human annotation or automatic synthetic generation. Both techniques have their own set of issues. Human annotation, despite its diversity and superior reasoning, struggles from scaling concerns. Synthetic data, on the other hand, despite its scalability, suffers from lack of linguistic and reasoning diversity. In this paper, we address both of these concerns by presenting a recasting approach that semi-automatically generates tabular NLI instances. We transform the
A MICROFLUIDICS-BASED APPROACH FOR ISOLATION OF ANTIGEN-SPECIFIC CD8+ T CELLS

iv Table of contents.v List of figures.viii Chapter 1: Introduction.1 1.1 Innate versus adaptive immunity.1 1.2 Major Histocompatibility Complex (MHC) molecules.3 1.3 Antigen-specific CD8+ T cell activation.5 1.4 Non-small cell lung cancer.6 1.5 Treatment of NSCLC.6 1.6 Strategies to isolate tumor-specific CD8+ T cells and current challenges.8 Chapter 2: Materials and methods.11 2.1 Cell lines.11 2.2 Mice.11 2.3 CD8+ T cell isolation and expansion.12 2.4 CD8+ T cell expansion.13
Investigating Design Style Evaluation in User Cognitions Using Statistical Methods

Based on the modern design theory, the product design style is the subjective feeling of the product’s whole form, and its research and classiﬁcation depend on the user’s sensibility evaluation. But for a long time, the emphasis of style quantiﬁcation has focused on the relationship between user evaluation and design form elements, but seldom consider the inﬂuence of user cognition on perceptual evaluation, so it limits the validity and scope of application of such correlation model. Therefore, based on the research approach of cognitive theory, this paper puts forward the hypothesis and the inﬂuence of association and Recognition cognitive process on the evaluation of product design style, and takes a group of modernist products as an example to carry on the empirical research. Based on the research approach of cognitive theory, this paper uses statistical methods, puts forward hypotheses, and conducts empirical research on the impact of cognitive processes such as association and recognition on product design style evaluation, taking machine aesthetic design style as an example. The results show that Lenovo has a great inﬂuence on the style evaluation of products, and its inﬂuence comes from the expressive dimension of design style; There is no signiﬁcant correlation between recognition and product design style evaluation, but only has a weak impact on some single observation variables. In addition, the results of exploratory factor analysis (EFA) on the experimental scale show that there are two potential evaluation dimensions when users evaluate the machine aesthetic style.
UUV Autonomy and Control Near Submarines Using Actively Sampled Surrogates

Many tools have been developed to simulate Unmanned Underwater Vehicle (UUV) motion and autonomous behaviors to evaluate UUV capabilities. However, there is no simulator that performs real-time modeling of the complex hydrodynamic interaction forces that a UUV experiences when operating near a moving submarine. These hydrodynamic interactions must be determined in real time to simulate the launch and recovery of UUVs from submarines. Potential flow models may be fast enough to solve the hydrodynamic interactions in real time, but by oversimplifying the physics and neglecting viscosity, they introduce inaccuracies into the simulations. Computational Fluid Dynamics (CFD) is capable of accurately modeling these hydrodynamic interactions, but simulations take hours or days to solve. To overcome this obstacle, a machine learning method known as Gaussian Process (GP) regression is used to create a surrogate reduced-order-model that predicts the hydrodynamic interactions in real time. The GP regression model is trained by actively sampling CFD simulations in order to accurately model complex hydrodynamic interactions. This new approach allows the GP regression model to be incorporated into a UUV motion simulator and evaluate how the UUV is affected by the hydrodynamic interactions. Operating envelopes are developed that outline regions where the UUV safely overcomes the hydrodynamic interactions and where the UUV is overpowered and collides with the submarine. By incorporating this surrogate model into the autonomy architecture, new autonomous behaviors are created that compensate for the hydrodynamic interactions by adjusting the desired UUV heading and speed which allows it to better stay on course.
Improving Entity Linking Systems With DuckDB

Entity linking systems are complex applications that have an important place in information retrieval systems. Because of its complexity, entity linking is performed by third-party toolkits. While most entity linking toolkits do not use database management systems, there are some toolkits that use the advantage of them. REL is one of these toolkits and uses SQLite, which has a row-based architecture. An alternative for SQLite may be DuckDB. DuckDB has a column-based architecture and has a potential design for zero-copy data transfer in the future. The comparison between the performances of DuckDB and SQLite is made based on the experiments using the advantageous features of DuckDB. DuckDB allows users to manipulate the query optimizer to choose index join over hash join. In addition to this, a novel text compression method FSST is available on DuckDB. The results of the experiments show that SQLite still outperforms DuckDB.
Knowledge Graph and Deep Learning Assisted Question Answering and Ontology Construction: A Survey

Knowledge graphs have emerged as a powerful framework for representing structured knowledge and capturing the relationships between entities in various domains. They provide a comprehensive and interconnected view of information, enabling efficient reasoning, search, and data integration. Language models are trained on a lot of data and perform well on tasks from the general domain. Knowledge graph infusion is a method to teach the domain to a language model and perform better on domain-specific tasks. While KGs from specialized domains are rare to obtain and difficult to create, many OpenIE techniques can accelerate the process. This survey paper presents an overview of knowledge graphs, focusing on their construction, representation, and utilization in QA for specialized domains
Determination of plasma impedance of microwave plasma system by electric field simulation

A simple method has been proposed to determine the plasma impedance based on the electric field simulation of the whole microwave plasma system including the matching network. The plasma impedance can be determined by the experimental parameters in the matching network (positions of the three stub tuner). From the real part of the plasma impedance, the conductivity of the plasma can be deduced. When a reasonable model is assumed to relate the plasma conductivity and the electron density (ne), the average ne independent of any plasma condition may be obtained. To show the possible procedure to extract the information on the average ne, the present method has been applied for the high-pressure hydrogen plasma generated between a narrow gap (<0.5 mm) where the usual Langmuir probe method is not applicable. The obtained average ne is on the order of 1012 cm−3, which is consistent with the available experimental results. The present attempt to extract information on the plasma impedance and ne based on the positions of the three stub tuner may be potentially useful in the control of industrial plasma processes.A simple method has been proposed to determine the plasma impedance based on the electric field simulation of the whole microwave plasma system including the matching network. The plasma impedance can be determined by the experimental parameters in the matching network (positions of the three stub tuner). From the real part of the plasma impedance, the conductivity of the plasma can be deduced. When a reasonable model is assumed to relate the plasma conductivity and the electron density (ne), the average ne independent of any plasma condition may be obtained. To show the possible procedure to extract the information on the average ne, the present method has been applied for the high-pressure hydrogen plasma generated between a narrow gap (<0.5 mm) where the usual Langmuir probe method is not applicable. The obtained average ne is on the order of 1012 cm−3, which is consistent with the available experimental results. The present attempt to extract information on the plasma impedance and ne based on the pos.
Deductive Additivity for Planning of Natural Language Proofs

Current natural language systems designed for multi-step claim validation typically operate in two phases: retrieve a set of relevant premise statements using heuristics (planning), then generate novel conclusions from those statements using a large language model (deduction). The planning step often requires expensive Transformer operations and does not scale to arbitrary numbers of premise statements. In this paper, we investigate whether efficient planning heuristic is possible via embedding spaces compatible with deductive reasoning. Specifically, we evaluate whether embedding spaces exhibit a property we call deductive additivity: the sum of premise statement embeddings should be close to embeddings of conclusions based on those premises. We explore multiple sources of off-the-shelf dense embeddings in addition to fine-tuned embeddings from GPT3 and sparse embeddings from BM25. We study embedding models both intrinsically, evaluating whether the property of deductive additivity holds, and extrinsically, using them to assist planning in natural language proof generation. Lastly, we create a dataset, Single-Step Reasoning Contrast (SSRC), to further probe performance on various reasoning types. Our findings suggest that while standard embedding methods frequently embed conclusions near the sums of their premises, they fall short of being effective heuristics and lack the ability to model certain categories of reasoning.
Politics of climate risk management in local government: a case study of the municipality of Stavanger

The case study presented in this paper was conducted to study the politics of local-level climate risk management and discuss these politics' implications for responses to climate change and democratic deliberation. Local government plays an important role in the response to climate change, in particular with reference to coping with unwanted consequences of climate change, such as more frequent and intense extreme weather events, including torrential rain and flooding. Climate risk management is an approach that local government can adopt to deal with these unwanted consequences. To investigate the politics of local-level climate risk management, we conducted a case study of the municipality of Stavanger in Norway. In analyzing this case study, we drew on literature on the securitization of climate change, in particular, that of risk-based securitization of climate change produced by governmental power. The analysis given here is derived by applying the concept of risk logic understood as the translation of unwanted consequences of climate change into climate risk together with the actions and use of tools influenced by the discipline of risk analysis thereby entailed. Risk logic manifests in political discourse, actors, and tools. In this case study, the justification for risk logic on unwanted consequences of climate change at the local level comes from national-level laws and regulations. Moreover, climate risk management is translated into existing bureaucratic routines, organizational structures, and the activities of professionals. Risk tools play an essential role in making unwanted consequences of climate change governable and can manifest as a consequence of risk logic or can convey risk logic. The analysis implies that the securitization of climate change based on governmental power at the local level has a depoliticizing effect on the issue. Moreover, the unique characteristics of unwanted consequences of climate change fade as climate risk is seen as a risk driver to be factored into existing and well-known risks, and thereby normalizes the situation. Finally, the focus on the cause of climate change seems to diminish because safety is a function of the referent objects, and the response thereby becomes decoupled from the wider issue of global warming.
The influence of early environment and micronutrient availability on developmental epigenetic programming: lessons from the placenta

DNA methylation is the most commonly studied epigenetic mark in humans, as it is well recognised as a stable, heritable mark that can affect genome function and influence gene expression. Somatic DNA methylation patterns that can persist throughout life are established shortly after fertilisation when the majority of epigenetic marks, including DNA methylation, are erased from the pre-implantation embryo. Therefore, the period around conception is potentially critical for influencing DNA methylation, including methylation at imprinted alleles and metastable epialleles (MEs), loci where methylation varies between individuals but is correlated across tissues. Exposures before and during conception can affect pregnancy outcomes and health throughout life. Retrospective studies of the survivors of famines, such as those exposed to the Dutch Hunger Winter of 1944-45, have linked exposures around conception to later disease outcomes, some of which correlate with DNA methylation changes at certain genes. Animal models have shown more directly that DNA methylation can be affected by dietary supplements that act as cofactors in one-carbon metabolism, and in humans, methylation at birth has been associated with peri-conceptional micronutrient supplementation. However, directly showing a role of micronutrients in shaping the epigenome has proven difficult. Recently, the placenta, a tissue with a unique hypomethylated methylome, has been shown to possess great inter-individual variability, which we highlight as a promising target tissue for studying MEs and mixed environmental exposures. The placenta has a critical role shaping the health of the fetus. Placenta-associated pregnancy complications, such as preeclampsia and intrauterine growth restriction, are all associated with aberrant patterns of DNA methylation and expression which are only now being linked to disease risk later in life.
Fence detection in Amsterdam: transparent object segmentation in urban context

Introduction Accessibility and safe movement in urban areas entail infrastructure that minimizes the risks for pedestrians and bikers with diverse levels of abilities. Recognizing and mapping unsafe areas can increase awareness among citizens and inform city projects to improve their infrastructure. This contribution presents an example in which the specific objective is to recognize the unprotected areas around the canals in the city of Amsterdam. Method This is accomplished through running image processing algorithms on 11K waterside panoramas taken from the city of Amsterdam's open data portal. We created an annotated subset of 2K processed images for training and evaluation. This dataset debuts a novel pixel-level annotation style using multiple lines. To determine the best inference practice, we compared the IoU and robustness of several existing segmentation frameworks. Results The best method achieves an IoU of 0.79. The outcome is superimposed on the map of Amsterdam, showing the geospatial distribution of the low, middle, and high fences around the canals. Discussion In addition to this specific application, we discuss the broader use of the presented method for the problem of “transparent object detection” in an urban context.