Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Cone Beam Computed Tomography to Conventional Radiotherapy: Limited Angle of Projections for Lymph Nodes Located above or below the Collarbone

Various techniques are used in image-guided radiotherapy (IGRT). Cone beam computed tomography (CBCT) is widely used in IGRT on linear accelerators. The increased accuracy of IGRT enables reducing planning target volume margins and doses to normal tissues. However, for radiation therapies requiring fractionated irradiation, increased exposure dose attributed to CBCT becomes problematic. We investigated the smallest projection angle required to provide target accuracy for CBCT of lymph nodes located above or below the collarbone in breast cancer patients.
Evaluating the impact of object-oriented design on software quality

Describes the results of a study where the impact of object-oriented (OO) design on software quality characteristics is experimentally evaluated. A suite of Metrics for OO Design (MOOD) was adopted to measure the use of OO design mechanisms. Data collected on the development of eight small-sized information management systems based on identical requirements were used to assess the referred impact. Data obtained in this experiment show how OO design mechanisms such as inheritance, polymorphism, information hiding and coupling, can influence quality characteristics like reliability or maintainability. Some predictive models based on OO design metrics are also presented.
Proposed Methodology for Battery Aging and Drainage Mitigation

: A longer battery life is a highly sought-after feature for most smartphone users when considering their next device. However, with the emergence of new hardware technology and software applications that require heavy processing, the demand for battery power has significantly increased. Unfortunately, the development of battery technology has not kept up with the rapid advancements in smartphone hardware and software, which rely heavily on battery power. To address this issue, several approaches have been proposed to regulate battery consumption and the charging process on smartphones. In this paper, we summarize the different approaches related to this problem that managed to achieve up to a 61% increase in battery daily usage in simulation testing, highlighting their strengths, limitations, and current challenges. Furthermore, we provide a comprehensive review of various open-source datasets that have the potential to be used in developing new approaches to improve battery drainage and degradation in smartphones. We also discuss the methodology for collecting each dataset. Finally, we propose a new approach to address the current limitations and challenges to solving the problem of battery drainage and degradation that could be developed using the currently available datasets. These new approaches may involve incorporating Machine Learning (ML) techniques to predict battery charging patterns and minimize battery drainage.
Étude du comportement physique des bois au-dessus et au-dessous du point de saturation des fibres

v Table des matières . vii Liste des tableaux . xi Liste des figures . xiii Lexique . xix Remerciements . xxiii Avant-Propos . xxv Introduction . 1 Chapitre 1 Revue de littérature . 5 1.1 Les propriétés physiques du bois . 5 1.2 Les relations eau-bois . 5 1.2. 1 La sorption d’humidité par le bois . 6 1.2. 2 L’hystérèse de sorption d’humidité . 7 1.2. 3 La condensation de l’eau dans la structure poreuse du bois . 11 1.3 Le point de saturation des fibres (PSF) . 13 1.3. 1 Méthodes pour la détermination du PSF . 13 1.3. 2 Facteurs qui affectent le PSF . 16 1.4 Variations dimensionnelles du bois . 16 1.5 Introduction à la résonance magnétique nucléaire (RMN) et à l’imagerie par résonance magnétique nucléaire (IRM) . 23 1.5. 1 Le magnétisme nucléaire . 23 1.5. 2 Le phénomène de RMN . 25 1.5. 3 Le signal en RMN . 30 1.5. 4 Contraste en T1, T2 et densité protonique . 33 1.5. 5 La formation de l’image . 35 1.6 La résonance magnétique nucléaire (RMN) appliquée aux sciences du bois . 42 1.7 L’imagerie par résonance magnétique nucléaire (IRM) appliquée aux sciences du bois . 48 1.8 Le collapse du bois . 54 1.8. 1 Définition et description . 54 1.8. 2 La formation du collapse . 55 1.8. 3 La relation entre le collapse et les gerces superficielles et internes . 57
An Efficient Branch-and-Bound Routing Algorithm for Optical NoCs

Silicon photonics based optical networks-on-chip (ONoCs) are emerging as a power-efficient on-chip communication architecture for the next generation of chip multiprocessors. However, the thermal sensitivity of photonic devices presents power consumption challenges. Existing routing schemes optimized for optical power loss tend to avoid passing through high-temperature nodes, which in turn leads to contention at low-temperature nodes. It remains a crucial challenge to develop an adaptive routing algorithm that strikes a balance between the power consumption optimization and performance optimization. In this paper, we firstly propose an efficient branch-and-bound routing (BBR) algorithm for ONoCs. Secondly, we obtain 3BOR, a variant of the BBR algorithm with a bi-objective bounding function to optimize both the optical power loss and network performance. To the best of our knowledge, it is the first time that the branch-and-bound method is adopted to solve the routing optimization problem in ONoCs. Experimental results demonstrate that the proposed branch-and-bound routing method outperforms the state-of-the-art heuristic routing algorithm in terms of bi-objective optimization effect as well as algorithm running time. In detail, the 3BOR reduces thermal-induced optical power loss by 14.8% while enhancing the saturation injection rate by 6.5% as compared to the state-of-the-art heuristic contention-aware thermal-reliable routing algorithm.
Deep learning-based monitoring of overshooting cloud tops from geostationary satellite data

Overshooting tops can cause a variety of severe weather conditions, such as cloud-to-ground lightning, strong winds, and heavy rainfall, which can affect flight and ground operations. Many previous studies have developed overshooting top (OT) detection models. However, rather than identifying individual pixels in satellite images as OTs or non-OTs, we typically find OTs through visual inspection of the contextual information of pixels (i.e., dome-like shape). Such an approach is more intuitive, accurate, and generalizable regardless of the OT characteristics that are used in the existing OT detection algorithms. In this paper, a new approach is proposed for OT detection using deep learning, more specifically a convolutional neural network (CNN), which can mimic the human process by convolution operation. Himawari-8 satellite images were used as input data, which were chopped into patches (i.e., grids) with a 31 × 31 window size and binary detection (OT or non-OT) for each patch was the output of the model. The validation results show that CNN can be successfully applied for the detection of OTs over the tropical regions, showing a mean probability of detection (POD) of 79.68% and a mean false alarm ratio (FAR) of 9.78%.
Self-supervised terrain classification based on moving objects using monocular camera

For autonomous robots equipped with a camera, terrain classification is essential in finding a safe pathway to a destination. Terrain classification is based on learning, but the amount of data cannot be infinite. This paper presents a self-supervised classification approach to enable a robot to learn the visual appearance of terrain classes in various outdoor environments by observing moving objects, such as humans and vehicles, and to learn about the terrain, based on their paths of movement. We verified the performance of our proposed method experimentally and compared the results with those obtained using supervised classification. The difference in error rates between self-supervised and supervised methods was about 0–11%.
condi-Living organisms and sedimentary remains from high mountain lakes in the Alps

We publish a data set of environmental and biological data collected in 2000 during the ice-free period in high mountain lakes mainly located above the local timberline in the Alps, in Italy, Switzerland and Austria. Environmental data include coordinates, geographical attributes and detailed information on vegetation, bedrock and land use in lake catchments. Chemical analyses of a sample for each lake collected at the lake surface in summer 2000 are also reported. Biological data include phytoplankton, zooplankton, macroinvertebrates, benthic diatoms. Diatoms, cladoceran and chironomids remains and algal and bacterial pigments were also analysed in lake sediments.
DCU at the NTCIR-9 SpokenDoc Passage Retrieval Task

We describe details of our runs and the results obtained for the "IR for Spoken Documents (SpokenDoc) Task" at NTCIR-9. The focus of our participation in this task was the investigation of the use of segmentation methods to divide the manual and ASR transcripts into topically coherent segments. The underlying assumption of this approach is that these segments will capture passages in the transcript relevant to the query. Our experiments investigate the use of two lexical coherence based segmentation algorithms (Text-Tiling, C99). These are run on the provided manual and ASR transcripts, and the ASR transcript with stop words removed. Evaluation of the results shows that TextTiling consistently performs better than C99 both in segmenting the data into retrieval units as evaluated using the centre located relevant information metric and in having higher content precision in each automatically created segment.
Effect of Feature Smoothing Methods in Text Classification Tasks

The number of features to be considered in a text classification system is given by the size of the vocabulary and this is normally in the range of the tens or hundreds of thousands even for small tasks. This leads to parameter estimation problems for statistical based methods and countermeasures have to be found. One of the most widely used methods consists of reducing the size of the vocabulary according to a well defined criterion in order to be able to reliably estimate the set of parameters. In the field of language modeling this problem is also encountered and several smoothing techniques have been developed. In this paper we show that using the full vocabulary together with a suitable choice of the smoothing technique for the text classification task obtains better results than the standard feature selection techniques.
Speech emotion recognition with cross-lingual databases

In this paper, we investigate cross-lingual automatic speech emotion recognition. The basic idea is that since the emotion recognition system is based on the acoustic features only, it is possible to combine data in different languages to improve the recognition accuracy. We begin with the construction of a Mandarin database of emotional speech, which is similar to the well-known Berlin Database of Emotional Speech (EMO-DB) in the composition and size. In order to reduce the variability due to different languages and different speakers, we propose to apply histogram equalization as a data normalization method. Recognition systems based on support vector machines have been evaluated on EMO-DB. Compared to the baseline system without multi-lingual databases and data normalization, the proposed system has achieved a relative improvement of 39.9% in the emotion recognition accuracy, from 86.2% to 91.7%. The accuracy is among the best known results reported on EMO-DB, if not the best.
The Masaoka-Koga Stage Classification for Thymic Malignancies: Clarification and Definition of Terms

No official stage classification for thymic malignancies has been defined by the Union Internationale Contre le Cancer and the American Joint Commission on Cancer. The International Thymic Malignancy Interest group (ITMIG) has selected the Masaoka system1 with the modification proposed by Koga et al.2 as the one that will be used,3 until a scientifically validated system is defined in the next edition of the international tumor staging manuals in 2017. However, there are details of the Masaoka and Koga classifications that have never been defined clearly. This article addresses this issue to achieve consistency in terms from this point forward for ITMIG initiatives. We hope that this standard will be adopted by all researchers because it will enhance collaboration and definition of a better classification system in the future.
A hybrid evolutionary programming algorithm for spread spectrum radar polyphase codes design

This paper presents a hybrid evolutionary programming algorithm to solve the spread spectrum radar polyphase code design problem. The proposed algorithm uses an Evolutionary Programming (EP) approach as global search heuristic. This EP is hybridized with a gradient-based local search procedure which includes a dynamic step adaptation procedure to perform accurate and efficient local search for better solutions. Numerical examples demonstrate that the algorithm outperforms existing approaches for this problem.
Bloom Filters via d-Left Hashing and Dynamic Bit Reassignment Extended Abstract

In recent work, the authors introduced a data structure with the same functionality as a counting Bloom filter (CBF) based on fingerprints and thed-left hashing technique. This paper describes dynamic bit reassignment, an approach that allows the size of the fingerprint to flexibly change with the load in each hash bucket, thereby reducing the probability of a false positive. This technique allows us to not only imp rove our d-left counting Bloom filter, but also to construct a data structure with the same functionality as a Bloom filter, including the ability to handle insertions online, that yields fewer false positives for sufficiently large filters. Our results show that our d-left Bloom filter data structure begins achieving smaller false positive rates than the standard construction at 16 bits per element. We explain the technique, describe why it is amenable to hardware implementation, and provide experimental results.
Personalization Functionality for the Semantic Web: Architectural Outline and First Sample Implementations

We propose a service–based architecture for bringing methods and techniques from the area of adaptive hypermedia to the Semantic Web. In our framework, personalization functionalities from adaptive hypermedia are available as web–services which a user can subscribe / un-subscribe as s/he likes. We have implemented our ideas in the Personal Reader, a framework for defining rule–based personalization algorithms for Semantic Web applications. In this paper, we present the basic architecture of the Personal Reader framework, and describe its realization in one example instance: A Personal Reader for displaying learning resources for Java programming. Other instances like e. g. a Personal Reader for publications, are currently under development.
Data-Driven Intelligent Condition Adaptation of Feature Extraction for Bearing Fault Detection Using Deep Responsible Active Learning

The detection of faulty bearings is an essential step in guaranteeing the safe and efficient operation of rotating machinery. Bearings, which also transmit the loads and pressures generated by the machinery, support the rotating shafts. A common method for bearing fault diagnostics is using signal processing techniques. In terms of accuracy, dependability, and sensitivity to various fault types and severity levels, these techniques do, however, have significant limits. To address these limitations, practitioners often integrate signal processing with advanced techniques like machine learning and data analytics to enhance diagnostic accuracy, reliability, and overall effectiveness in bearing fault detection and predictive maintenance. Exploration of various models has demonstrated enhanced results in managing nonlinear data to a certain degree; however, these models face challenges when dealing with intricately complex patterns. Moreover, CNNs can automatically learn relevant features from raw sensor data, capturing intricate patterns and relationships in the data without the need for manual feature engineering. CNNs can be optimized for scalability and real-time processing, essential for applications requiring quick decisions and computationally less expensive for large datasets compared to other models. An optimized one-dimensional Convolutional Neural Network (1D CNN) using different kernel sizes is proposed for predicting and finding bearing problems to overcome these constraints. This method creates a feature vector by applying many filters of various sizes to the input signal. Using the created feature vector, the input signal can be divided into many categories, such as healthy or unhealthy. In comparison to other methods, the proposed technology performs better and offers a high accuracy of 99.52% in bearing fault identification. The 1D CNN model with multiple kernel sizes excels in preserving data structure during dimensionality reduction, as confirmed by comparing t-Distributed Stochastic Neighbor Embedding plots. Particularly, the optimized 1D CNN with multiple kernel sizes accurately classifies faults with minimal errors, showcasing its fault classification proficiency compared with the other state-of-the-art methods. The visualization underscores the methodology’s efficacy in discerning intricate fault patterns within the data.
A review of Palaeozoic and Mesozoic tetrapods from Greenland

This article presents a synthesis of Palaeozoic and Mesozoic fossil tetrapods from Greenland, including an updated review of the holotypes and a new photographic record of the main specimens. All fossil tetrapods found are from East Greenland, with at least 30 different known taxa: five stem tetrapods (Acanthostega gunnari, Ichthyostega eigili, I. stensioi, I. watsoni, and Ymeria denticulata) from the Late Devonian of the Aina Dal and Britta Dal Formations; four temnospondyl amphibians (Aquiloniferus kochi, Selenocara groenlandica, Stoschiosaurus nielseni, and Tupilakosaurus heilmani) from the Early Triassic of the Wordie Creek Group; two temnospondyls (Cyclotosaurus naraserluki and Gerrothorax cf. pulcherrimus), one testudinatan (cf. Proganochelys), two stagonolepids (Aetosaurus ferratus and Paratypothorax andressorum), the eudimorphodontid Arcticodactylus, undetermined archosaurs (phytosaurs and both sauropodomorph and theropod dinosaurs), the cynodont Mitredon cromptoni, and three mammals (Haramiyavia clemmenseni, Kuehneotherium, and cf. ? Brachyzostrodon), from the Late Triassic of the Fleming Fjord Formation; one plesiosaur from the Early Jurassic of the Kap Stewart Formation; one plesiosaur and one ichthyosaur from the Late Jurassic of the Kap Leslie Formation, plus a previously unreported Late Jurassic plesiosaur from Kronprins Christian Land. Moreover, fossil tetrapod trackways are known from the Late Carboniferous (morphotype Limnopus) of the Mesters Vig Formation and at least four different morphologies (such as the crocodylomorph Brachychirotherium, the sauropodomorph Eosauropus and Evazoum, and the theropodian Grallator) associated to archosaurian trackmakers are known from the Late Triassic of the Fleming Fjord Formation. The presence of rich fossiliferous tetrapod sites in East Greenland is linked to the presence of well-exposed continental and shallow marine deposits with most finds in terrestrial deposits from the Late Devonian and the Late Triassic.
Optimization of Scheduling for Hydro-thermal Power Generation and TransmissionIncluding Pumped Storage Systems

This work presents a novel approach for solving the short-term scheduling of hydro-thermal power generation, including pumped storage systems and transmission constraints. The problem addressed is known as Security Constrained Unit Commitment (SCUC). Pumped Storage Units (PSUs) are important in electric systems during the off-peak and the peak demand periods, providing economic and technical benefits. Linear aproximations are applied to nonlinear equations of this kind of mathematical problems which are: fuel cost functions, generation-discharge curves of PSUs and transmission constraints modeled with Alternating Current power flow model. Thus, MILP models are presented for the problem addessed. To prove the efficiency of the proposed models, two systems with PSUs will be tested: a modified 6-bus and the IEEE 31-bus power system. Results show that the proposed MILP models allow: modeling the SCUC problem more realistically, obtaining feasible solutions within efficient computational times, and reaching production cost savings up to almost 15% compared to power systems that lack capacities to pumping water. Several indicators obtained from results are presented through graphs, as a tool for improving operation and maintenance of power systems. The analysis of these indicators and the graphic interpretation allow to identify and classify critical parts of systems as well as to make recommendations about future system improvements.
Early instrumental seismicity recorded in the eastern Alps

Three hundred and seventy-five earthquakes recorded by the Trieste station in the eastern Alps in the early instrumental period, i.e., before the occurrence of the destructive event of May 6, 1976, have been located after painstaking gathering of seismograms and bulletins. Analysis of the statistical parameters of the locations, together with comparisons with other instrumental and macroseismic locations, have allowed us to assess the quality of the results obtained, taking into account the often limited nature of the data. Using a process of digitization and the study of seismograms, focal parameters, particularly the magnitude Mw, have been recovered for three earthquakes of the 1950s and 1960s.
Maximal Phrases Based Analysis for Prototyping Online Discussion Forums Postings

Chat texts produced in an educational environment are categorized and rated for the purpose of positioning (or placement) of the learner with respect to a learning program (appropriate courses, textbooks, etc). The difficulty lies in the fact that the texts are short and informal. A standard LSA/vector-space model is therefore combined with techniques appropriate for short texts. The approach uses phrases rather than words in the term-document matrix, and for determining prototypical documents of each category, a nonpara-metric permutation test is used.