Miracle-dz/UniFAR-FTUs huggingface dataset:
Apache Gora support for Oracle NoSQL datastore

The recent change in enterprise data needs, the subsequent advent of NoSQL databases and their ongoing evolution have created applications that make concurrent use of various data storage engines to solve different problems. These data storage engines have different data models and different APIs. This concurrent use of a variety of databases, polyglot persistence as it is called, could lead to confusion and may become hard to manage. Additionally, some aggregate-oriented NoSQL databases are able to handle object persistence natively and efficiently, but others do not have such build-in and efficient functionality. For these reasons, a framework is needed that can consolidate the different NoSQL APIs and data models into one, and can provide a single way for object persistence in NoSQL datastores regardless of their implementation. For the relational databases this issue has been long resolved by the various ORM solutions that are available, have been proved trustworthy and have become the standard. However, in the case of NoSQL databases, there is still no standard framework. Apache Gora tries to fill this gap. Apache Gora already supports persistence to 5 NoSQL data stores. In this project, we designed, implemented and tested a new module for integrating Apache Gora with the Oracle NoSQL Database as an object persistence data store. This module proves that an object to a key/value store mapping is possible and thus object persistence is feasible in such data stores.
Netherlands Hydrological Modeling Instrument

Dutch hydrological institutes Alterra, Deltares, Netherlands Environmental Assessment Agency and RWS Waterdienst have cooperated to build a new national hydrological model. The instrument will be used by all three ministries involved in national water policy matters, for instance drought management, manure policy and climate change issues. The basis of the modeling instrument is a state-of-the-art on-line coupling of the groundwater system (MODFLOW) and the unsaturated zone (metaSWAP). Optionally a national surface water module (MOZART-DM) can be added, in which surface water distribution, discharge and supply are accounted for. The data for the instrument is derived from several national databases, including the subsoil, groundwater abstractions, topsoil, land use, drainage, water distribution, water management and meteorological data. In addition the database on vegetation development, vegetation-atmosphere and groundwater-surface water interactions is updated. The Netherlands Hydrological modeling Instrument will be the center point of a framework of models, to coherently model the hydrological system and the multitude of functions it supports. This framework will assist in national policy making. Our ambition is to also make the instrument suitable on a regional and local water board scale. Therefore, for the future refinement of the model, local knowledge of hydrological system needs to be implemented in cooperation with the water boards. The development of the new Netherlands Hydrological modeling Instrument is a quality boost for national water management, thanks to a unique cooperation of all major research institutes and policy makers.
Predicting peptides that bind to MHC molecules using supervised learning of hidden markov models

The binding of a major histocompatibility complex (MHC) molecule to a peptide originating in an antigen is essential to recognizing antigens in immune systems, and it has proved to be important to use computers to predict the peptides that will bind to an MHC molecule. The purpose of this paper is twofold: First, we propose to apply supervised learning of hidden Markov models (HMMs) to this problem, which can surpass existing methods for the problem of predicting MHC‐binding peptides. Second, we generate peptides that have high probabilities to bind to a certain MHC molecule, based on our proposed method using peptides binding to MHC molecules as a set of training data. From our experiments, in a type of cross‐validation test, the discrimination accuracy of our supervised learning method is usually approximately 2–15% better than those of other methods, including backpropagation neural networks, which have been regarded as the most effective approach to this problem. Furthermore, using an HMM trained for HLA‐A2, we present new peptide sequences that are provided with high binding probabilities by the HMM and that are thus expected to bind to HLA‐A2 proteins. Peptide sequences not shown in this paper but with rather high binding probabilities can be obtained from the author (E‐mail: mami@ccm.cl.nec.co.jp). Proteins 33:460–474, 1998. © 1998 Wiley‐Liss, Inc.
Parameter estimation via artificial data generation with the “two-stage” approach

In this paper, we consider one of the most classical estimation problem, that of identifying an unknown parameter in a given model from measurements of input/output data. We present a new method named the two-stage approach which provides efficient estimates. The method is based on the preliminary generation of artificial data, and it is fully non-Bayesian. In this way, it is possible to avoid the well known difficulties encountered when resorting to Kalman filtering techniques in parameter estimation.
Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns

Speech impairments often emerge as one of the primary indicators of Parkinson’s disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification.
Texture Analysis of Temporomandibular Joint Disc Changes Associated with Effusion Using Magnetic Resonance Images

The purpose of this study was to identify changes in the temporomandibular joint disc affected by effusion by using texture analysis of magnetic resonance images (MRIs). Methods: A total of 223 images of the TMJ, 42 with joint effusion and 181 without, were analyzed. Three consecutive slices were then exported to MaZda software, in which two oval ROIs (one in the anterior band and another in the intermediate zone of the joint disc) were determined in each slice and eleven texture parameters were calculated by using a gray-level co-occurrence matrix. Spearman’s correlation coefficient test was used to assess the correlation between texture variables and to select variables for analysis. The Mann–Whitney test was used to compare the groups. Results: The significance level was set at 5%, with the results demonstrating that there was no high correlation between the parameter directions. It was possible to observe a trend between the average parameters, in which the group with effusion always had smaller values than the group without effusion, except for the parameter measuring the difference in entropy. Conclusion: The trend towards lower overall values for the texture parameters suggested a different behavior between TMJ discs affected by effusion and those not affected, indicating that there may be intrinsic changes.
Bioinformatics Applications Note Systems Biology Pathvar: Analysis of Gene and Protein Expression Variance in Cellular Pathways Using Microarray Data

Finding significant differences between the expression levels of genes or proteins across diverse biological conditions is one of the primary goals in the analysis of functional genomics data. However, existing methods for identifying differentially expressed genes or sets of genes by comparing measures of the average expression across predefined sample groups do not detect differential variance in the expression levels across genes in cellular pathways. Since corresponding pathway deregulations occur frequently in microarray gene or protein expression data, we present a new dedicated web application, PathVar, to analyze these data sources. The software ranks pathway-representing gene/protein sets in terms of the differences of the variance in the within-pathway expression levels across different biological conditions. Apart from identifying new pathway deregulation patterns, the tool exploits these patterns by combining different machine learning methods to find clusters of similar samples and build sample classification models.
A Review of Exotic Animal Disease in Great Britain and in Scotland Specifically between 1938 and 2007

Background Incursions of contagious diseases of livestock into disease-free zones are inevitable as long as the diseases persist elsewhere in the world. Knowledge of where, when and how incursions have occurred helps assess the risks, and regionalize preventative and reactive measures. Methodology Based on reports of British governmental veterinary services, we review occurrence of the former OIE List A diseases, and of Aujeszky's disease, anthrax and bovine tuberculosis (bTB) in farm-animals in Great Britain (GB) between 1938 and 2007. We estimate incidence of each disease on GB agricultural holdings and fraction of susceptible farm-animals culled to control the disease each year. We then consider the frequency and incidence of the diseases in Scotland alone. The limitations of available data on historical disease occurrence and denominator populations are detailed in Text S2. Conclusions The numbers of livestock and poultry farmed in GB grew over the years 1938–2007; the number of agricultural holdings decreased. An amalgamation of production on larger holdings took place from the 1940s to the 1980s. The maximum annual incidence of a reviewed disease in GB 1938–2007 was reported for bTB, 1.69% of holdings in 1961. This was followed by Newcastle disease, 1.50% of holdings in 1971, and classical swine fever, 1.09% of holdings in 1940. The largest fractional cull of susceptible livestock in a single year in each of the four decades 1950s–1980s was due to a viral disease primarily affecting swine. During the periods 1938–1949 and 2000–2007 this was due to outbreaks of foot and mouth disease. In the absence of incursions of the former OIE List A diseases in the 1990s, this was due to bTB. Over the 70 years, the diseases were reported with lower frequency and lower annual incidence in Scotland, as compared to when these statistics are considered for GB as a whole.
Downbeat tracking with multiple features and deep neural networks

In this paper, we introduce a novel method for the automatic estimation of downbeat positions from music signals. Our system relies on the computation of musically inspired features capturing important aspects of music such as timbre, harmony, rhythmic patterns, or local similarities in both timbre and harmony. It then uses several independent deep neural networks to learn higher-level representations. The downbeat sequences are finally obtained thanks to a temporal decoding step based on the Viterbi algorithm. The comparative evaluation conducted on varied datasets demonstrates the efficiency and robustness across different music styles of our approach.
Neuroscience Based Design: Fundamentals and Applications

Neuroscience-based or neuroscience-informed design is a new application area of Brain-Computer Interaction (BCI). It takes its roots in study of human well-being in architecture, human factors study in engineering and manufacturing including neuroergonomics. In traditional human factors studies and/or well-being study, mental workload, stress, and emotion are obtained through questionnaires that are administered upon completion of some task and/or the whole experiment. Recent advances in BCI research allow for using Electroencephalogram (EEG) based brain state recognition algorithms to assess the interaction between brain and human performance. We propose and develop an EEG-based system CogniMeter to monitor and analyze human factors measurements of newly designed software/hardware systems and/or working places. Machine learning techniques are applied to the EEG data to recognize levels of mental workload, stress and emotions during each task. The EEG is used as a tool to monitor and record the brain states of subjects during human factors study experiments. We describe two applications of CogniMeter system: human performance assessment in maritime simulator and EEG-based human factors evaluation in Air Traffic Control (ATC) workplace. By utilizing the proposed EEG-based system, true understanding of subjects working patterns can be obtained. Based on the analyses of the objective real time EEG-based data together with the subjective feedback from the subjects, we are able to reliably evaluate current systems/hardware and/or working place design and refine new concepts and design of future systems.
SPITZER OBSERVATIONS OF THE SCUBA/VLA SOURCES IN THE LOCKMAN HOLE: STAR FORMATION HISTORY OF INFRARED-LUMINOUS GALAXIES

We present Spitzer Space Telescope imaging observations at 3.6, 4.5, 5.8, 8.0, and 24 ? m of the SCUBA submillimeter sources and ? Jy VLA radio sources in a 5' ? 5' area in the Lockman Hole East region. Out of the ~40 SCUBA/VLA sources in the field, Spitzer counterparts were detected for nearly all except for the few low-weight SCUBA detections. We show that the majority (80%-90%) of the detected sources are cold (i.e., starburst-like) infrared-luminous galaxies (LIR > 1011 L? ) at redshift 0.5 < z < 3.5, whose star-formation rate density (SFRD) is comparable to that of the optically selected star-forming galaxies.
Extensive phlegmon and pyoderma gangrenosum: diagnostic difficulties

Pyoderma gangrenosum (PG) is a relatively rare neutrophilic dermatosis, characterized by progressive skin necrosis. It typically has a chronic course, of unknown etiology. Pyoderma gangrenosum diagnosis can be difficult because both histopathological examination and results of additional laboratory tests are not specific and the clinical state is conclusive, as for other physicians it poses a number of diagnostic dilemmas. Therefore, this condition should be treated interdisciplinary. We present a case of a 40-year-old patient with a diagnosis of PG, which in the early stages of the disease was treated as an extensive phlegmon by physicians of other specialties and it presented a serious diagnostic as well as therapeutic problem.
Belief propagation, robust reconstruction and optimal recovery of block models

We consider the problem of reconstructing sparse symmetric block models with two blocks and connection probabilities a=n and b=n for inter- and intra-block edge probabilities respectively. It was recently shown that one can do better than a random guess if and only if (a b) 2 > 2(a + b). Using a variant of Belief Propagation, we give a reconstruction algorithm that is optimal in the sense that if (a b) 2 > C(a + b) for some constant C then our algorithm maximizes the fraction of the nodes labelled correctly. Along the way we prove some results of independent interest regarding robust reconstruction for the Ising model on regular and Poisson trees.
Experience-based learning mechanism for neural controller adaptation: Application to walking biped robots

Neurobiology studies showed that the role of the anterior cingulate cortex of the brain is primarily responsible for avoiding repeated mistakes. According to vigilance threshold, which denotes the tolerance to risks, we can differentiate between a learning mechanism that takes risks, and one that averts risks. The tolerance to risk plays an important role in such learning mechanism. Results have shown the differences in learning capacity between risk-taking and risk avert behaviors. In this paper, we propose a learning mechanism that is able to learn from negative and positive feedback. It is composed of two phases, evaluation and decision-making phase. In the evaluation phase, we use a Kohonen Self Organizing Map technique to represent success and failure. Decision-making is based on an early warning mechanism that enables to avoid repeating past mistakes. Our approach is presented with an implementation on a simulated planar biped robot, controlled by a reflexive low-level neural controller. The learning system adapts the dynamics and range of a hip sensor neuron of the controller in order for the robot to walk on flat or sloped terrain. Results show that success and failure maps can learn better with a threshold that is more tolerant to risk. This gives rise to robustness to the controller even in the presence of slope variations.
Automatic detection and counting of fisheries using fish images

In Senegal, stock recovery and fish classification are based on manual data collection, and the fish caught by the fishery are not often declared. What's more, data collection suffers from a lack of tools for monitoring and counting fish caught at fishing docks. Researchers have carried out studies on the fishery in Senegal, but data collection is almost non-existent. Moreover, there is no local fisheries database or automatic detection and counting algorithm. In this paper, a semantic segmentation algorithm is proposed using intelligent systems for the collection of fishery catches, for the formation of the local database. The data are collected by taking images of fish at the Soumbédioune fishing wharf in Senegal, and are completed with the Fishbase database. They were applied to the algorithm and resulted in a segmented dataset with masks. This constitutes our local database. The database is used with YOLO v8. The latter is very important for detecting images with bounding boxes in order to train the model. The results obtained are very promising for the proposed automatic poison detection and counting model. For example, the recall-confidence scores translate into bounding box performance with scores ranging from 0.01 to 0.75, confirming the performance of this model with bounding boxes
A Variable Neighborhood Search Heuristic for Point Coverage, Sink Location and Data Routing in Wireless Sensor Networks

In this work point coverage, sink location and data routing problems are considered in an integrated way and two new mixed integer programming formulations are proposed. Since these models are computationally difficult to solve, a hierarchical solution procedure is proposed. In the top level, best sensor locations are sought by variable neighbourhood search. Given the sensor locations, a simpler formulation can be obtained and a Lagrangean Heuristic is used to solve the sink location and data routing model efficiently. Experimental results indicate that the performance of hierarchical solution approach is very good.
Graphite in the Bishop Tuff and its effect on postcaldera oxygen fugacity

Several cubic kilometers of Paleozoic graphite-bearing argillitic country rocks are present as lithic fragments in Bishop Tuff ignimbrite and fallout. The lithics were entrained by the 650 km3 of rhyolite magma that vented during the 5to 6-day-long, caldera-forming eruption at Long Valley, California. The caldera is floored by a 350 km2 roof plate that collapsed during the eruption and consists in large part of the Paleozoic strata that provided the abundant hornfelsed metapelitic lithic clasts in the tuff. Graphite has been identified by Raman spectroscopy, electron-dispersive spectroscopy, and X-ray diffraction as an irregularly dispersed component in the small fraction of Bishop Tuff pumice that is dark-colored. Carbon concentration has been determined in pumice, lithics, and wall rocks. Values of δ13C range from –21‰ to –29‰ Vienna Peedee Belemnite (VPDB) for pumice, lithics, and argillitic wall rocks, reflecting the biogenic origin of the reduced carbon in oxygen-limited black Paleozoic marine mudrocks. Carbonate contents, measured separately, are negligible in fresh pumice and lithics. Microprobe analyses of titanomagnetite-ilmenite pairs show that oxygen-fugacity values of numerous batches of postcaldera Early Rhyolite (750–640 ka; ~100 km3) are up to one log unit more reduced than those of the temperature–oxygen fugacity (T-fO2) array of the Bishop Tuff (767 ka), despite similar major-element compositions and Fe-Ti– oxide temperature ranges. All of the many batches of Early Rhyolite, which erupted episodically over an interval of ~125,000 years, yield the reduced fO2 values, indicating that reaction with graphite lowered magmatic fO2 after the caldera-forming eruption but before the first eruption of Early Rhyolite. It is inferred that reaction of postcaldera rhyolite magma with the reduced carbon in a great mass of subsided roof rocks lowered its fO2. It is suggested that comparable effects could have attended caldera collapse of other magma chambers hosted in continental sedimentary rocks.
Essence: A Resource Discovery System Based on Semantic File Indexing

Discovering different types of file resources (such as documentation, programs, and images) in the vast amount of data contained within network file systems is useful for both users and system administrators. In this paper we discuss the Essence resource discovery system, which exploits file semantics to index both textual and binary files. By exploiting semantics, Essence extracts keywords that summarize a file, and generates a compact yet representative index. Essence understands nested file structures (such as uuencoded, compressed, ‘‘tar’’ files), and recursively unravels such files to generate summaries for them. These features allow Essence to be used in a number of useful settings, such as anonymous FTP archives. We present measurements of our prototype and compare them to related projects, such as the Wide Area Information Servers (WAIS) system and the MIT Semantic File System (SFS). We demonstrate that Essence can index more data types, generate smaller indexes, and in some cases index data faster than these systems. Our prototype generates WAIS-compatible indexes, allowing WAIS users to take advantage of the Essence indexing methods.