Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract)

The maritime industry's continuous commitment to sustainability has led to a dedicated exploration of methods to reduce vessel fuel consumption. This paper undertakes this challenge through a machine learning approach, leveraging a real-world dataset spanning two years of a passenger vessel in west coast Canada. Our focus centers on the creation of a time series forecasting model given the dynamic and static states, actions, and disturbances. This model is designed to predict dynamic states based on the actions provided, subsequently serving as an evaluative tool to assess the proficiency of the vessel's operation under the captain's guidance. Additionally, it lays the foundation for future optimization algorithms, providing valuable feedback on decision-making processes. To facilitate future studies, our code is available at https://github.com/pagand/model_optimze_vessel/tree/AAAI.
Estimating Traffic and Anomaly Maps via Network Tomography

Mapping origin-destination (OD) network traffic is pivotal for network management and proactive security tasks. However, lack of sufficient flow-level measurements as well as potential anomalies pose major challenges towards this goal. Leveraging the spatiotemporal correlation of nominal traffic, and the sparse nature of anomalies, this paper brings forth a novel framework to map out nominal and anomalous traffic, which treats jointly important network monitoring tasks including traffic estimation, anomaly detection, and traffic interpolation. To this end, a convex program is first formulated with nuclear and l1-norm regularization to effect sparsity and low rank for the nominal and anomalous traffic with only the link counts and a small subset of OD-flow counts. Analysis and simulations confirm that the proposed estimator can exactly recover sufficiently low-dimensional nominal traffic and sporadic anomalies so long as the routing paths are sufficiently “spread-out” across the network, and an adequate amount of flow counts are randomly sampled. The results offer valuable insights about data acquisition strategies and network scenaria giving rise to accurate traffic estimation. For practical networks where the aforementioned conditions are possibly violated, the inherent spatiotemporal traffic patterns are taken into account by adopting a Bayesian approach along with a bilinear characterization of the nuclear and l1 norms. The resultant nonconvex program involves quadratic regularizers with correlation matrices, learned systematically from (cyclo)stationary historical data. Alternating-minimization based algorithms with provable convergence are also developed to procure the estimates. Insightful tests with synthetic and real Internet data corroborate the effectiveness of the novel schemes.
Halvade: scalable sequence analysis with MapReduce

Motivation: Post-sequencing DNA analysis typically consists of read mapping followed by variant calling. Especially for whole genome sequencing, this computational step is very time-consuming, even when using multithreading on a multi-core machine. Results: We present Halvade, a framework that enables sequencing pipelines to be executed in parallel on a multi-node and/or multi-core compute infrastructure in a highly efficient manner. As an example, a DNA sequencing analysis pipeline for variant calling has been implemented according to the GATK Best Practices recommendations, supporting both whole genome and whole exome sequencing. Using a 15-node computer cluster with 360 CPU cores in total, Halvade processes the NA12878 dataset (human, 100 bp paired-end reads, 50× coverage) in <3 h with very high parallel efficiency. Even on a single, multi-core machine, Halvade attains a significant speedup compared with running the individual tools with multithreading. Availability and implementation: Halvade is written in Java and uses the Hadoop MapReduce 2.0 API. It supports a wide range of distributions of Hadoop, including Cloudera and Amazon EMR. Its source is available at http://bioinformatics.intec.ugent.be/halvade under GPL license. Contact: jan.fostier@intec.ugent.be Supplementary information: Supplementary data are available at Bioinformatics online.
TMBpro: secondary structure, beta-contact and tertiary structure prediction of transmembrane beta-barrel proteins

MOTIVATION Transmembrane beta-barrel (TMB) proteins are embedded in the outer membranes of mitochondria, Gram-negative bacteria and chloroplasts. These proteins perform critical functions, including active ion-transport and passive nutrient intake. Therefore, there is a need for accurate prediction of secondary and tertiary structure of TMB proteins. Traditional homology modeling methods, however, fail on most TMB proteins since very few non-homologous TMB structures have been determined. Yet, because TMB structures conform to specific construction rules that restrict the conformational space drastically, it should be possible for methods that do not depend on target-template homology to be applied successfully. RESULTS We develop a suite (TMBpro) of specialized predictors for predicting secondary structure (TMBpro-SS), beta-contacts (TMBpro-CON) and tertiary structure (TMBpro-3D) of transmembrane beta-barrel proteins. We compare our results to the recent state-of-the-art predictors transFold and PRED-TMBB using their respective benchmark datasets, and leave-one-out cross-validation. Using the transFold dataset TMBpro predicts secondary structure with per-residue accuracy (Q(2)) of 77.8%, a correlation coefficient of 0.54, and TMBpro predicts beta-contacts with precision of 0.65 and recall of 0.67. Using the PRED-TMBB dataset, TMBpro predicts secondary structure with Q(2) of 88.3% and a correlation coefficient of 0.75. All of these performance results exceed previously published results by 4% or more. Working with the PRED-TMBB dataset, TMBpro predicts the tertiary structure of transmembrane segments with RMSD <6.0 A for 9 of 14 proteins. For 6 of 14 predictions, the RMSD is <5.0 A, with a GDT_TS score greater than 60.0. AVAILABILITY http://www.igb. uci.edu/servers/psss.html.
Consistency and performance of concurrent interactive database applications

In many modern database applications, there is an emerging need for interactive environments where users directly manipulate the contents of the database. Graphical user interfaces (GUIs) display images of the database which must reflect a consistent up-to-date state of the data with minimum perceivable delay to the user. Moreover, the possibility of several applications concurrently displaying different views of the same database increases the overall system complexity. In this paper, we show how design, performance and concurrency issues can be addressed by adapting existing database techniques. We propose the use of suitable display schemas whose instances compose active views of the database, an extended client caching scheme which is expected to yield significant performance benefits, and a locking mechanism that maintains consistency between the GUIs and the database.
Biological and Prognostic Significance of Chromosome 5q Deletions in Myeloid Malignancies

The presence of del(5q), either as the sole karyotypic abnormality or as part of a more complex karyotype, has distinct clinical implications for myelodysplastic syndromes (MDS) and acute myeloid leukemia. The 5q− syndrome, a subtype of low-risk MDS, is characterized by an isolated 5q deletion and <5% blasts in the bone marrow and can serve as a useful model for studying the role of 5q deletions in the pathogenesis and prognosis of myeloid malignancies. Recent clinical results with lenalidomide, an oral immunomodulatory drug, have shown durable erythroid responses, including transfusion independence and complete cytogenetic remissions in patients with del(5q) MDS with or without additional chromosomal abnormalities. These results indicate that lenalidomide can overcome the pathogenic effect of 5q deletion in MDS and restore bone marrow balance. The data provide important new insights into the pathobiology of 5q chromosomal deletions in myeloid malignancies.
Promoting Critical Operant-Based Leadership While Decreasing Ubiquitous Directives and Exhortations

Equipping leaders to successfully motivate their teams is daunting, especially when the prevailing management practice is to simply tell people what to do. Training based on the theoretically and empirically based Operant Model of Effective Supervision was provided to seasoned managers in the private and public sectors. A randomized posttest-only design was used. Trainees responded in their own words to diverse true-to-life scenarios in an in-basket assessment. In both settings, training not only increased the newly learned skills of monitoring and providing positive/neutral consequences, but it also reduced lackluster antecedent-only strategies.
Teachers' Perceptions of Principals' Instructional Leadership and Implications

Little research has examined leadership behaviors that have direct effects on teachers and classroom instruction. In this study, over 800 public elementary, middle, and high schools in diverse regions of the United States described the everyday characteristics (e.g., strategies, behaviors, attitudes, and goals) of principals practicing exemplary instructional leadership. Inductive analysis of the data generated two major themes comprising 11 strategies that enhanced teachers' classroom instruction. The first theme, talking with teachers to promote reflection, included making suggestions, giving feedback, modeling, using inquiry and soliciting advice/opinions, and praising. The second theme, promoting professional growth, included emphasizing the study of teaching and learning; supporting collaboration; developing coaching relationships; encouraging and supporting redesign of programs; applying the principles of adult learning, growth, and development to staff development programs; and implementing action research to inform instructional decision making. The effective instructional leader's approach to instruction was found to be dialogic, inquiry-oriented, inclusive, and pedagogically sound.
Supplementary Figure Legends

Fig. S2. Effect of siRNA-mediated knockdowns on protein levels. (A,B) The stability and detergent-extractability of OST subunits was analysed by direct solubilisation of siRNA-treated HeLa cells in SDS sample buffer followed by infrared immunoblotting. (C) Quantitative representation of the data presented in A,B. Fluorescent signals were quantified, normalised to the corresponding α-tubulin signals, and expressed as a percentage of the signal in control cells.
Myenteric ganglia from the adult guinea‐pig small intestine in tissue culture

Abstract Myenteric ganglia dissociated from the small intestine of adult guinea‐pigs survived in long‐term culture (1–2 months) and progressed to structural organization resembling the myenteric plexus in situ. Developmental changes were similar to cultures derived from neonatal intestine. After one week, the neurons gathered into clusters on a glial cell carpet. Processes from the neurons branched and ramified over the glial substrate. As the cultures matured, the processes joined into tracts and the neurons and glia formed compact aggregates reminiscent of ganglia interconnected by fibre bundles. Injection of dye revealed characteristic Dogiel I and II neuronal morphology. Electrical recording identified electrical and synaptic behaviour comparable to intact myenteric plexus, longitudinal muscle preparations, except slow synaptic excitation was absent. Pharmacological responses to forskolin and 5‐hydroxytryptamine were essentially the same as in freshly dissected preparations. Lucifer yellow injected into single glial cells spread to a broad population indicative of the dye coupling found among glia in the myenteric plexus in situ. The results suggest that adult myenteric ganglia in culture are a useful model for investigation of aspects of enteric neurobiology including: (a) formation of connections in microcircuits; (b) cellular neurophysiology of enteric neurons; (c) neuropharmacology; and (4) cell biology of neuronal‐glial interactions in the myenteric plexus.
Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case

Latent variable models can be used to probabilistically "fill-in" missing data entries. The variational autoencoder architecture (Kingma and Welling, 2014; Rezende et al., 2014) includes a "recognition" or "encoder" network that infers the latent variables given the data variables. However, it is not clear how to handle missing data variables in this network. We show how to calculate exactly the latent posterior distribution for the factor analysis (FA) model in the presence of missing data, and note that this solution exhibits a non-trivial dependence on the pattern of missingness. Experiments compare the effectiveness of various approaches to filling in the missing data.
Local Cerebral Blood Flow in the Conscious Rat As Measured with 14C-Antipyrine, 14C-Iodoantipyrine and 3H-Nicotine

Local cerebral blood flow (LCBF) in the conscious rat was estimated with one of 3 radiotracers -14C-antipyrine, 14C-iodoantipyrine or 3H-nicotine. A tracer was infused intravenously at a constant rate and blood concentration was followed until the animal was killed by decapitation. Tracer concentration was then measured in each of 14 brain regions. The Kety-Schmidt analysis was applied to the data with UCantipyrine and "C-iodoantipyrine. The results confirmed findings of Sakurada et al. (1978) that 14C-iodoantipyrine provides LCBF's that are twice those obtained with uC-antipyrine and that approximate LCBF's found with an inert gas. LCBF was calculated from the 3H-nicotine data by assuming complete extraction of tracer from blood by brain. This assumption was approximated for infusion times of 50 sec or less, when LCBF's derived with 3H-nicotine generally did not differ significantly from LCBF's obtained with "C-iodoantipyrine. Fifty-sec 3H-nicotine-derived flows for the pineal and pituitary glands were, respectively, 2.59 ± 0.36 (SEM) cm3 g ' min1 and 1.28 ± 0.08 cm3 g l min 1. LCBF's calculated for 70 sec to 240 sec of 3H-nicotine infusion were lower than 50-sec values due to back-diffusion, but nevertheless were as high as "C-antipyrine LCBF's due to the marked binding of tracer by brain tissue. The results supported the conclusion of Sakurada et al. (1978) that iodoantipyrine is the non-gaseous agent of choice for measuring LCBF precisely, but also showed that short infusion schedules with nicotine provided good estimates of LCBF.
Patient decision-making of CGM sensor driven insulin therapies in type 1 diabetes: In silico assessment

In type 1 diabetes (T1D) therapy, continuous glucose monitoring (CGM) sensors, which provide glucose concentration in the subcutis every 1-5 min for 7 consecutive days, should allow in principle a more efficient insulin dosing than that based on the conventional 3-4 self-monitoring of blood glucose (SMBG) measurements per day. However, CGM, at variance with SMBG, is still not approved for insulin dosing in T1D management because regulatory agencies, e.g. FDA, are looking for more factual evidence on its safety. An in silico assessment of SMBG- vs CGM-driven insulin therapy can be a first step. Here we present a simulation model of T1D patient decision-making obtained by interconnecting models of glucose-insulin dynamics, SMBG and CGM measurement errors, carbohydrates-counting errors, insulin boluses time variability and forgetfulness, and subcutaneous insulin pump delivery. Inter- and intra- patient variability of model parameters are considered. The T1D patient decision-making model allows to run realistic multi-day simulations scenarios in a population of virtual subjects. We present the first results of simulations run in 20 virtual subjects over a 7-day period, which demonstrates that additional information brought by CGM (trend and hypo/hyperglycemic warnings) with respect to SMBG produces a statistically significant increment (about of 9%) of time spent by the patient in the euglycemic range (70-180mg/dl).
Sulfur Metabolisms in Epsilon- and Gamma-Proteobacteria in Deep-Sea Hydrothermal Fields

In deep-sea hydrothermal systems, super hot and reduced vent fluids from the subseafloor blend with cold and oxidized seawater. Very unique and dense ecosystems are formed within these environments. Many molecular ecological studies showed that chemoautotrophic epsilon- and gamma-Proteobacteria are predominant primary producers in both free-living and symbiotic microbial communities in global deep-sea hydrothermal fields. Inorganic sulfur compounds are important substrates for the energy conservative metabolic pathways in these microorganisms. Recent genomic and metagenomic analyses and biochemical studies have contributed to the understanding of potential sulfur metabolic pathways for these chemoautotrophs. Epsilon-Proteobacteria use sulfur compounds for both electron-donors and -acceptors. On the other hand, gamma-Proteobacteria utilize two different sulfur-oxidizing pathways. It is hypothesized that differences between the metabolic pathways used by these two predominant proteobacterial phyla are associated with different ecophysiological strategies; extending the energetically feasible habitats with versatile energy metabolisms in the epsilon-Proteobacteria and optimizing energy production rate and yield for relatively narrow habitable zones in the gamma-Proteobacteria.
Performance analysis of bipolar Marx generator topologies with input switch charging and inductive charging method

This paper deals with design, simulations and comparison of various bipolar Marx topologies. Output voltage pulse specifications are 10 kV, % voltage droop − 10%, PRF − 1kHz and pulse width of 5μsec. The features of inductive charging and charging through input switch is analyzed. Bipolar Marx generator with six switches, five switches and four switch topologies are designed and simulated. These solid-state circuits are charged through a diode and switch. During the erection mode, a pulse current of kiloampere range of short duration is passing through this switch. A comparative analysis is carried out on the output pulse parameters for the Marx modulators.
Future Standards for Immersive VR: Report on the IEEE Virtual Reality 2007 Workshop

The future standards for immersive VR workshop at the IEEE Virtual Reality 2007 Conference aimed to foster an understanding between the VR and Web3D communities. Although the fields of VR and Web-based 3D graphics tend to view their technologies as different beasts, the technologies share many requirements regarding virtual world content: modeling, lighting, animation, interaction, and direct manipulation. This workshop focused on the common interests around the seamless access and delivery of interactive, network-based VR content and resources.
Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets

Extracting meaningful features from complex, high-dimensional datasets across scientific domains remains challenging. Current methods often struggle with scalability, limiting their applicability to large datasets, or make restrictive assumptions about feature-property relationships, hindering their ability to capture complex interactions. BoUTS's general and scalable feature selection algorithm surpasses these limitations to identify both universal features relevant to all datasets and task-specific features predictive for specific subsets. Evaluated on seven diverse chemical regression datasets, BoUTS achieves state-of-the-art feature sparsity while maintaining prediction accuracy comparable to specialized methods. Notably, BoUTS's universal features enable domain-specific knowledge transfer between datasets, and suggest deep connections in seemingly-disparate chemical datasets. We expect these results to have important repercussions in manually-guided inverse problems. Beyond its current application, BoUTS holds immense potential for elucidating data-poor systems by leveraging information from similar data-rich systems. BoUTS represents a significant leap in cross-domain feature selection, potentially leading to advancements in various scientific fields.
Spectrum sensing with Gaussian approximated eigenvalue ratio based detection

Eigenvalue based detection is an ideal solution to spectrum sensing problem in cognitive radio networks. However, results on its sensing performance are very limited. In this paper, we analytically investigate the sensing performance of the eigenvalue ratio based detection. Firstly, a simple and accurate Gaussian approximation to the extreme eigenvalue distribution is proposed. Secondly, a closed-form expression for the optimal decision threshold is derived. The proposed analytical framework yields a useful approximation in realistic sensing scenarios.
Clustering around medoids based on ultrametric properties

The FFUCA (Fast and Flexible Unsupervised Clustering Algorithm) is a fast clustering method based on ultrametric properties. It aggregates data in the same way as partitional methods. But, it elects representatives differently. Indeed, in FFUCA the cluster representatives are deduced from an ultrametric structure built from a sample data. This ultrametric structure gives the data behavior according to used distance. Thus the results are independent from the cluster representatives. We propose in this paper an extension named FFUCAAM to change for better the quality of clusters. Indeed, we improve the election of these representatives. We substitute them by mediods after every new aggregation. This extension increases the complexity in the average case to O(Σi=1k mi2) where k is the number of the resulting clusters and mi is the size of the cluster Ci. In fact, its computational cost is increased but it still less than O(n2), thus it remains applicable to large databases.
Processing affected speech within human machine interaction

Spoken dialog systems (SDS) integrated into human-machine interaction interfaces is becoming a standard technology. Current state-of-the-art SDS, usually, is not able to provide for the user a natural way of communication. Existing automated dialog systems do not dedicate enough attention to problems in the interaction related to affected user behavior. As a result, Automatic Speech Recognition (ASR) engines are not able to recognize affected speech and dialog strategy does not make use of the user’s emotional state. This paper addresses some aspects of processing affected speech withinnatural human-machine interaction. First of all, we propose an affected speech adapted ASR engine. Second, we describe our methods of emotion recognition within speech and present our results of emotion classification within Interspeech 2009 Emotion Challenge. Third, we test affected speech adapted speech recognition models and introduce an approach to achieve emotion adaptive dialog management in human-machine interaction.
Joint Processing and Discriminative Training for Letter-to-Phoneme Conversion

We present a discriminative structureprediction model for the letter-to-phoneme task, a crucial step in text-to-speech processing. Our method encompasses three tasks that have been previously handled separately: input segmentation, phoneme prediction, and sequence modeling. The key idea is online discriminative training, which updates parameters according to a comparison of the current system output to the desired output, allowing us to train all of our components together. By folding the three steps of a pipeline approach into a unified dynamic programming framework, we are able to achieve substantial performance gains. Our results surpass the current state-of-the-art on six publicly available data sets representing four different languages.