Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting and exploiting near-sortedness for efficient relational query evaluation

Many relational operations are best performed when the relations are stored sorted over the relevant attributes (e.g. the common attributes in a natural join operation). However, generally relations are not stored sorted because it is expensive to maintain them this way (and impossible whenever there is more than one relevant sort key). Still, many times relations turn out to be nearly-sorted, where most tuples are close to their place in the order. This state can result from "leftover sortedness", where originally sorted relations were updated, or were combined into interim results when evaluating a complex query. It can also result from weak correlations between attribute values. Currently, nearly-sorted relations are treated the same as unsorted relations, and when relational operations are evaluated for them, a generic algorithm is used. Yet, many operations can be computed more efficiently by an algorithm that exploits this near-ordering. However, to consistently benefit from using such algorithms the system should also refrain from using the wrong algorithm for relations which happen not to be sorted at all. Thus, an efficient test is required, i.e., a very fast approximation algorithm for establishing whether a given relation is sufficiently nearly-sorted. In this paper, we provide the theoretical foundations for improving query evaluation over possibly nearly-sorted relations. First we formally define what it means for a relation to be nearly-sorted, and show how operations over such relations, such as natural join, set operations and sorting, can be executed significantly more efficiently using an algorithm that we provide. If a relation is nearly-sorted enough, then it can be sorted using two sequential reads of the relation, and writing no intermediate data to disk. We then construct efficient probabilistic tests for approximating the degree of the near-sortedness of a relation without having to read an entire file. The role of our algorithms in a database management system setting is illustrated as soon as the theoretical foundation is laid out. Finally, we outline factors that relate to practical implementations of our algorithms. We show how our test can be enhanced to provide an approximation rather than just a yes-no answer, and discuss its implementability in reallife scenarios where some sparseness may be present in the database files (e.g. if they were created using a B*-tree approach). We also show how our sort can benefit distributed systems and systems that use a solid-state drive, which may very well become prevalent in the near future.
Factors Associated With Risky Alcohol Consumption Among Male Street Laborers in Urban Vietnam

ABSTRACT Background: Alcohol consumption is of global concern. However, drinking patterns and associated factors remain under-investigated, especially among low socioeconomic groups such as street laborers. Objective: Using the social cognitive model as a framework for the study we aimed to identify factors associated with risky alcohol consumption. Methods: In a cross-sectional study using structured questionnaires, 450 male street laborers searching for casual works in Hanoi, Vietnam were interviewed. A logistic regression was applied in order to detect predictors of risky alcohol drinking. Results: During the last month, 45% of the participants reported daily consumption while the other 55% consumed weekly or less. Among the drinkers (416 out of 450, 92%), 27% were identified as high-risk drinkers who reported more than 14 standard drinks per week, while only 8% were lifetime abstainers. The multivariable logistic regression showed that older age, higher income were positively associated with a higher likelihood of drinking alcohol, while high school level negatively. The environmental predictor was the higher level of peer connection. The association between drinking and risky behavior was found positive with regards to the number of sexual partners. Conclusions: The study suggests that male street laborers are vulnerable to health risks. Decision makers should note that a significant proportion of this target group exceeds the guidelines for alcohol use and this should be included in future interventions or further research. A multisectoral approach together with an important strategy of education is needed to control alcohol use.
Optimizing Parkinson's Disease Diagnosis with Multimodal Data Fusion Techniques

Parkinson's disease (PD) is a central nervous system neurodegenerative illness. Its symptoms include poor motor skills, speech, cognition, and memory. The condition is incurable, although evidence shows that early identification and therapy reduce symptoms. A lack of medical facilities and personnel hinders PD identification. PD is a common chronic degenerative neurological dyskinesia that threatens the elderly. Multi-modal data fusion may reveal more about PD pathophysiology. This study aims to contribute to the evaluation of PD by introducing a novel multimodal deep-learning technique for distinguishing individuals with PD from those without PD. This study utilizes resting functional magnetic resonance imaging (rfMRI) and gene data obtained from the Parkinson's Progression Markers Initiative (PPMI) and Alzheimer's Disease Neuroimaging Initiative (ADNI) databases. The primary objective is to predict the specific pathological brain regions and identify the risk genes associated with PD. The authors want to learn more about the genetic components and underlying procedures by analyzing these datasets. Contributing to the development and progression of PD. In this study, we present our findings that demonstrate the superior recital of our proposed multimodal method compared to both unimodal approaches and other existing multimodal methods. Our evaluation is based on an extensive dataset consisting of real patients. Specifically, our proposed method stacked deep learning classifiers (SDLC) achieves an impressive F1-score of 0.99 and an accuracy of 99.4%, surpassing the performance of both unimodal approaches and other multimodal methods. These results highlight the efficiency and potential of our method in enhancing the accuracy and reliability of patient data analysis. In this study, we demonstrate that our proposed method consistently surpasses alternative approaches in terms of performance, as indicated by a higher average increase in F1-score. This finding highlights the advantage of training on multiple modalities, even when a particular modality is absent during inference.
Local and scientific knowledge to assess plot quality in Central Spain

ABSTRACT Farmers’ land management is a key factor for sustainable agriculture, yet the traditional knowledge which constitutes its base is seldom incorporated in the land and soil academic literature. The need for a hybrid approach that combines local (farmers’) and scientific knowledge is formally recognized. Plot quality criteria are a component of both sources of knowledge, playing an important role in shaping land management practices. Yet there are few case studies comparing the criteria used by farmers and soil scientists when assessing plot quality, particularly in developed countries. We compare the plot quality assessment done by a sample of 31 farmers in a semiarid area of Spain with an edaphic quality index (EQI) obtained through laboratory analyses of soil samples from 61 farm plots. There is a coincidence between farmer and laboratory assessments in 70% of cases. Plots identified by farmers as being of good quality have a significantly higher EQI than those identified as bad quality. The two key differentiating parameters were clay content (average 21% in good versus 18% in bad quality plots) and soil organic carbon content (1.6 and 1.3%, respectively), both closely related to soil water retention ability. A holistic land vision, a higher level of education and large differences in EQI between good and bad plots as assessed by farmers have a positive influence in the degree of coincidence between farmers’ and laboratory assessments.
Oncostatin M-Induced and Constitutive Activation of the JAK2/STAT5/CIS Pathway Suppresses CCL1, but Not CCL7 and CCL8, Chemokine Expression1

The recruitment of leukocytes to injured tissue is crucial for the initiation of inflammatory responses as well as for immune surveillance to fight tumor progression. In this study, we show that oncostatin M, a member of the IL-6-type cytokine family and potent proinflammatory cytokine stimulates the expression of the chemokines CCL1, CCL7, and CCL8 in primary human dermal fibroblasts at a faster kinetic than IL-1β or TNF-α. The production of CCL1 and CCL8 is important for migration of monocytes, while specific Abs against CCL1 additionally inhibit the migration of T lymphocytes. We identify the mitogen-activated protein kinases ERK1/2 and p38 as crucial factors for the enhanced expression of CCL1 and CCL8. Depletion of the ERK1/2 target genes c-Jun or c-Fos strongly decrease CCL1 and CCL8 expression, while p38 MAPK prolongs the half-life of CCL1, CCL7, and CCL8 mRNA through inhibition of tristetraprolin. None of the STAT transcription factors STAT1, STAT3, or STAT5 stimulate transcription of CCL1 or CCL8. However, we identify a negative regulatory function of activated STAT5 for the gene expression of CCL1. Importantly, not STAT5 itself, but its target gene cytokine inducible SH2-domain containing protein is required for the STAT5 inhibitory effect on CCL1 expression. Finally, we show that constitutive activation of STAT5 through a mutated form of JAK2 (JAK2 V617F) occurring in patients with myeloproliferative disorders similarly suppresses CCL1 expression. Taken together, we identify novel important inflammatory target genes of OSM which are independent of STAT signaling per se, but depend on MAPK activation and are partly repressed through STAT5-dependent expression of cytokine inducible SH2-domain containing protein.
Comparing Density Forecast Models

In this paper we discuss how to compare various (possibly misspecified) density forecast models using the Kullback-Leibler information criterion (KLIC) of a candidate density forecast model with respect to the true density. The KLIC differential between a pair of competing models is the (predictive) log-likelihood ratio (LR) between the two models. Even though the true density is unknown, using the LR statistic amounts to comparing models with the KLIC as a loss function and thus enables us to assess which density forecast model can approximate the true density more closely. We also discuss how this KLIC is related to the KLIC based on the probability integral transform (PIT) in the framework of Diebold et al. (1998). While they are asymptotically equivalent, the PIT-based KLIC is best suited for evaluating the adequacy of each density forecast model and the original KLIC is best suited for comparing competing models. In an empirical study with the S&P500 and NASDAQ daily return series, we find strong evidence for rejecting the normal-GARCH benchmark model, in favor of the models that can capture skewness in the conditional distribution and asymmetry and long memory in the conditional variance. Copyright © 2007 John Wiley & Sons, Ltd.
On traced monoidal closed categories

The structure theorem of Joyal, Street and Verity says that every traced monoidal category arises as a monoidal full subcategory of the tortile monoidal category Int. In this paper we focus on a simple observation that a traced monoidal category is closed if and only if the canonical inclusion from into Int has a right adjoint. Thus, every traced monoidal closed category arises as a monoidal co-reflexive full subcategory of a tortile monoidal category. From this, we derive a series of facts for traced models of linear logic, and some for models of fixed-point computation. To make the paper more self-contained, we also include various background results for traced monoidal categories.
Automated Access Control Rule Generation via Semantic Matching

Semantic Web techniques bring us help in many fields. In this paper, we propose a way to use Semantic Matching on access control. We illustrate the motivation with an eBusiness access control schema based on RelBAC (for Relation Based Access Control). Semantic Matching techniques are applied on the lightweight ontologies of the subjects and the objects to find the semantic similarities that can be used to suggest new rules, to reuse the existing rules or to separate the duties of semantically disjoint user sets.
The Spectrum of a Noisy Free-Running Oscillator Explained by Random Frequency Pulling

We show that the Lorentzian spectrum of a free-running LC oscillator arises from the random pulling of its frequency by circuit noise sources. To prove this, we analyze the oscillator's spectrum using Adler's differential equation, first in response to pulling by a single injected tone and then by two tones mirrored around the free-run frequency. A novel analysis predicts the frequencies and amplitudes of the sidebands caused by the phase modulation that results. Ultimately, we show that frequency pulling by white noise accounts for the Lorentzian broadening of the oscillator spectrum, and that a complete pulling analysis predicts the same linewidth as a simple circuit analysis.
Finding Commonalities in Linked Open Data

The availability of a data source as huge, open, accessible and machine–understandable as the Web of Data asks for new and sophisticated inferences to be implemented in order to deeply exploit such a rich informative content. Towards this direction, the paper proposes an approach for inferring clusters in collections of RDF resources on the basis of the features shared by their descriptions. The approach grounds on an algorithm for Common Subsumers computation proposed in a previous work of some of the authors. The clustering service introduced here returns not only di↵erent cluster proposals for a given collection, but also a description of the informative content shared by the RDF resources within the clusters, in terms of (generalized) RDF triples.
Cattaneo Probabilistic-Possibilistic Belief Networks

The interpretation of membership functions of fuzzy sets as statistical likelihood functions leads to a probabilistic-possibilistic hierarchical description of uncertain knowledge. The fundamental advantage of the resulting fuzzy probabilities with respect to imprecise probabilities is the ability of using all the information provided by the data. This paper studies the possibility of using fuzzy probabilities to describe the uncertain knowledge about the values of the nodes of belief networks.
Systematic and palaeobiological implications of postcranial morphology in the Diprotodontidae (Marsupialia).

This thesis is focused on the information that can be gained from examining diprotodontid forelimb and hindlimb anatomy in a morphological, functional, palaeobiological, and phylogenetic context. This research is complemented through a study of diprotodontid fossil trackways, allowing testing of the hypotheses developed through study of skeletal anatomy, as well as providing insights into the palaeobiology of taxa that are not evident through study of skeletal anatomy. The discovery of an articulated hindlimb of Euowenia grata, from the Warburton River, allowed an investigation of the functional capabilities of this taxon (Chapter 2). It is shown that the development of the graviportal limb morphology exemplified by Diprotodon optatum was already well underway during the early Pliocene in E. grata. It is also suggested that the hindlimb morphology of E. grata is indicative of the ability to rear up against trees. In addition, the pedal digits of Pliocene diprotodontines demonstrate a similar degree of reduction to that seen later Pleistocene taxa such as D. optatum. Pliocene trackways from the Warburton River in central Australia, representing the oldest known Australian mammal trackways, are described in Chapter 3. The track-maker is identified as Euowenia grata, and the six individual trackways indicate gregarious behaviour in this species. Similarities in trackway proportions and pad morphology demonstrate an affinity with D. optatum. Australia’s most speciose and best preserved fossil mammal trackway site is described in Chapter 4. The trackways provide information about palaeoecological species associations. Individual trackways at the site demonstrate that D. optatum had the ability to vary trackway gauge without significantly varying speed. The trackways also record important new information about manual and pedal pad morphology. Functional morphological analyses of the hindlimb and forelimb anatomy of diprotodontids are presented in Chapter 5. These investigations suggested that the plesiomorphic diprotodontids Ngapakaldia and Nimbadon may have been scansorial. This is more remarkable in the latter, given that all other Miocene diprotodontid taxa studied demonstrate distinct adaptations towards a terrestrial lifestyle. It was also found that the limbs of the Plio-Pleistocene diprotodontines (Euowenia and Diprotodon) may have been restricted to a locomotory role, whereas the limbs of the Pleistocene
Recurrent Soft Attention Model for Common Object Recognition

We propose the Recurrent Soft Attention Model, which integrates the visual attention from the original image to a LSTM memory cell through a down-sample network. The model recurrently transmits visual attention to the memory cells for glimpse mask generation, which is a more natural way for attention integration and exploitation in general object detection and recognition problem. We test our model under the metric of the top-1 accuracy on the CIFAR-10 dataset. The experiment shows that our down-sample network and feedback mechanism plays an effective role among the whole network structure.
Expanded examinations of a low frequency modulation feature for speech/music discrimination

A low frequency modulation feature, LFMAD, was examined under several conditions with regard to its robustness on speech/music discrimination. The feature was tested on LF components from 2 Hz to 27 Hz and with different analysis window sizes. This feature performs best when using an analysis window size containing only one period of the LF component to be used. When the music contained much vocals, the error rate increased compared with only instrumental music in the speech/music discrimination task. This effect was found in LFMAD as well as in the MFCC feature, which was used for comparison. Tests were also carried out with signals in additive noise from 30 dB to 0 dB SNR. LFMAD performed better than MFCC in these tests. The error rate was higher for speech signals. There was a bias towards classifying data as music when the test conditions diverged from those of the training condition. This effect is less obvious for LFMAD than for MFCC. The best results in this study were obtained when combining the two features LFMAD and MFCC into a mixed feature. This seems to be a more robust feature regarding the speech/music discrimination ability and could be recommended when scanning data bases of unknown quality for speech events.
Performance Analysis for 3 - RR(RR)R Parallel Mechanism Based on Hessian Matrix

In this paper, the velocity and inertial force 3-RlowbarRlowbar(lowbarRlowbarRmacr)macrRmacr performance of a new parallel mechanism are analyzed by global conditioning index which includes Jacobian matrix and Hessian matrix, and this index is different from the old one which only includes Jacobian matrix. The performance atlas are given, and the mechanism's parameters can be easily selected which are proper to improve the mechanism's performance. The conclusion presented in this paper provides underlying theoretical grounds for the optimum design of the 3RlowbarRlowbar(lowbarRlowbarRmacr)macrRmacr mechanism
Beyond Left-to-Right: Multiple Decomposition Structures for SMT

Standard phrase-based translation models do not explicitly model context dependence between translation units. As a result, they rely on large phrase pairs and target language models to recover contextual e ects in translation. In this work, we explore n-gram models over Minimal Translation Units (MTUs) to explicitly capture contextual dependencies across phrase boundaries in the channel model. As there is no single best direction in which contextual information should flow, we explore multiple decomposition structures as well as dynamic bidirectional decomposition. The resulting models are evaluated in an intrinsic task of lexical selection for MT as well as a full MT system, through n-best reranking. These experiments demonstrate that additional contextual modeling does indeed benefit a phrase-based system and that the direction of conditioning is important. Integrating multiple conditioning orders provides consistent benefit, and the most important directions di er by language pair.
Peer-to-Peer (P2P) Architecture: Definition, Taxonomies, Examples, and Applicability

In this document we provide a survey of P2P (Peer-to-Peer) systems. The survey includes a definition and a taxonomy of P2P systems. This survey also includes a description of which types of applications can be built with P2P technologies and examples of P2P applications that are currently in use on the Internet. Finally, we discuss architectural tradeoffs and provide guidelines for deciding whether or not a P2P architecture would be suitable to meet the requirements of a given application.
Comparison of multicarrier PWM techniques for cascaded H-bridge inverter

Multilevel inverter technology has emerged recently as a very important alternative in the area of high-power medium-voltage energy control. Multicarrier pulse-width-modulation (PWM) strategies for cascaded H-bridge (CHB) inverter are used to identify phase-shifted PWM (PSPWM) that produces almost same THD as produced by carrier disposition PWM or level-shifted PWM (LSPWM), but PSPWM has the effect of even power distribution among cells while LSPWM has the effect of uneven power distribution among cells in a CHB. Simulation for a 9-level CHB is carried out in MATLAB/SIMULINK and simulation results are presented in this paper.
A New Approach for Extraction and Determination of Manganese in Environmental Samples Using Cloud-Point Extraction Coupled with Spectrophotometry

A new and versatile procedure for the spectrophotometric determination of manganese using micelle-mediated extraction was developed. The method involved the formation of an ion association complex between manganese oxyanion (MnO4 - ) and brilliant green in acidic solution and the complex was extracted into the surfactant Triton X-100 at optimum conditions. The surfactant-rich phase which contains manganese complex was mediated with solvent and the Mn content measured spectrophotometerically at absorption maximum (λmax = 657 nm). The effects of the several variables which impact the CPE efficiency were optimized by one-factor-at-a-time (OFAT). Under the optimized conditions, enrichment factor of 282 was achieved leading to limit of detection and limit of quantitation of 0.086 and 0.242 µg mL -1 respectively, with linearity of 0.2- 3 µg mL -1 . The precision (RSD%) of the proposed method was of 1.07% . The interferences effect of such ions was also studied. This method is applicable in the determination of manganese in environmental and vital samples.