Miracle-dz/UniFAR-FTUs huggingface dataset:
Situación actual de las prestaciones por desempleo en España

espanolEn esta nota, se describe el actual sistema de prestaciones por desempleo en Espana, poniendolo en relacioncon su evolucion en el pasado y con el prevaleciente en los paises de nuestro entorno. Teniendo en cuenta las interconexiones existentes entre el sistema de prestaciones por desempleo y las caracteristicas institucionales del mercado de trabajo se evaluan los problemaspendientes. Se constata que aunque el sistema actual ha reducido notablemente los problemas de incentivos que, en el pasado, fomentaban que el parado no buscase una salida rapida y eficaz del desempleo, dichos problemas de incentivos siguen existiendo ente determinadoscolectivos, donde la normativa actual favorece que surenta disponible aumente con la situacion de paro, alcombinarse prestaciones por desempleo con indemnizaciones por despido. EnglishThis paper describes the present unemployment benefit system in Spain, relating it to itspast trend and to the current system in the surrounding countries. Taking into account the existing interrelation between the unemployment benefit system and theinstitutional characteristics of the labour market, the pending problems are assessed. It shows that, even though the present system has notably reduced the incentive problems which, in the past, encouraged the unemployed not to look for a fast and effective exit from unemployment, such incentive problems still exist in certain groups, where the present normative favours their disposable personal income to increase when unemployed, combining the unemployment benefit and the dismissal indemnities.
Combinatorial Identities for Vacillating Tableaux

Vacillating tableaux are sequences of integer partitions that satisfy specific conditions. The concept of vacillating tableaux stems from the representation theory of the partition algebra and the combinatorial theory of crossings and nestings of matchings and set partitions. In this paper, we further investigate the enumeration of vacillating tableaux and derive multiple combinatorial identities and integer sequences relating to the number of vacillating tableaux, simplified vacillating tableaux, and limiting vacillating tableaux.
Direct initial orbit determination

Initial orbit determination (IOD) is an important early step in the processing chain that makes sense of and reconciles the multiple optical observations of a resident space object. IOD methods generally operate on line-of-sight (LOS) vectors extracted from images of the object, hence the LOS vectors can be seen as discrete point samples of the raw optical measurements. Typically, the number of LOS vectors used by an IOD method is much smaller than the available measurements (\ie, the set of pixel intensity values), hence current IOD methods arguably under-utilize the rich information present in the data. In this paper, we propose a \emph{direct} IOD method called D-IOD that fits the orbital parameters directly on the observed streak images, without requiring LOS extraction. Since it does not utilize LOS vectors, D-IOD avoids potential inaccuracies or errors due to an imperfect LOS extraction step. Two innovations underpin our novel orbit-fitting paradigm: first, we introduce a novel non-linear least-squares objective function that computes the loss between the candidate-orbit-generated streak images and the observed streak images. Second, the objective function is minimized with a gradient descent approach that is embedded in our proposed optimization strategies designed for streak images. We demonstrate the effectiveness of D-IOD on a variety of simulated scenarios and challenging real streak images.
Balanced Representation Learning for Long-tailed Skeleton-based Action Recognition

Skeleton-based action recognition has recently made significant progress. However, data imbalance is still a great challenge in real-world scenarios. The performance of current action recognition algorithms declines sharply when training data suffers from heavy class imbalance. The imbalanced data actually degrades the representations learned by these methods and becomes the bottleneck for action recognition. How to learn unbiased representations from imbalanced action data is the key to long-tailed action recognition. In this paper, we propose a novel balanced representation learning method to address the long-tailed problem in action recognition. Firstly, a spatial-temporal action exploration strategy is presented to expand the sample space effectively, generating more valuable samples in a rebalanced manner. Secondly, we design a detached action-aware learning schedule to further mitigate the bias in the representation space. The schedule detaches the representation learning of tail classes from training and proposes an action-aware loss to impose more effective constraints. Additionally, a skip-modal representation is proposed to provide complementary structural information. The proposed method is validated on four skeleton datasets, NTU RGB+D 60, NTU RGB+D 120, NW-UCLA, and Kinetics. It not only achieves consistently large improvement compared to the state-of-the-art (SOTA) methods, but also demonstrates a superior generalization capacity through extensive experiments. Our code is available at https://github.com/firework8/BRL.
Geometric Mechanics of Simultaneous Nonslip Contact in a Planar Quadruped

In this paper, we develop a geometric framework for generating non-slip quadrupedal two-beat gaits. We consider a four-bar mechanism as a surrogate model for a contact state and develop the geometric tools such as shape-change basis to aid in gait generation, local connection as the matrix-equation of motion, and stratified panels to model net locomotion in line with previous work\cite{prasad2023contactswitch}. Standard two-beat gaits in quadrupedal systems like trot divide the shape space into two equal, decoupled subspaces. The subgaits generated in each subspace space are designed independently and when combined with appropriate phasing generate a two-beat gait where the displacements add up due to the geometric nature of the system. By adding ``scaling"and ``sliding"control knobs to subgaits defined as flows over the shape-change basis, we continuously steer an arbitrary, planar quadrupedal system. This exhibits translational anisotropy when modulated using the scaling inputs. To characterize the steering induced by sliding inputs, we define an average path curvature function analytically and show that the steering gaits can be generated using a geometric nonslip contact modeling framework.
Label Denoising through Cross-Model Agreement

Learning from corrupted labels is very common in real-world machine-learning applications. Memorizing such noisy labels could affect the learning of the model, leading to sub-optimal performances. In this work, we propose a novel framework to learn robust machine-learning models from noisy labels. Through an empirical study, we find that different models make relatively similar predictions on clean examples, while the predictions on noisy examples vary much more across different models. Motivated by this observation, we propose \em denoising with cross-model agreement \em (DeCA) which aims to minimize the KL-divergence between the true label distributions parameterized by two machine learning models while maximizing the likelihood of data observation. We employ the proposed DeCA on both the binary label scenario and the multiple label scenario. For the binary label scenario, we select implicit feedback recommendation as the downstream task and conduct experiments with four state-of-the-art recommendation models on four datasets. For the multiple-label scenario, the downstream application is image classification on two benchmark datasets. Experimental results demonstrate that the proposed methods significantly improve the model performance compared with normal training and other denoising methods on both binary and multiple-label scenarios.
Regional Crop Modeling: How Future Climate May Impact Crop Yields in Switzerland

Crop yield levels and crop yield variability depend to a high extent on climate conditions. Due to climate change the local crop growth conditions in Switzerland are expected to change. This study addresses the question how weather, soil and management factors influenced mean wheat yield levels in twelve study regions in the Swiss Plateau during the period 1990 – 2008. In addition, we assess potential impacts of climate change on wheat yield levels and yield variability. Applying a multiple regression model based on historical yield, weather, soil and management data in the period 1990 – 2008 at a regional scale, we find clear relations between yield levels and weather as well as management and soil factors. We show that global warming is assumed to decrease wheat yield levels in all study regions between 4-10%. Nevertheless, negative impacts of climate change on wheat production can be minimized or even overcompensated if the CO2 fertilization effect and possible adaptation measures are taken into account.
On Active Learning for Gaussian Process-based Global Sensitivity Analysis

This paper explores the application of active learning strategies to adaptively learn Sobol indices for global sensitivity analysis. We demonstrate that active learning for Sobol indices poses unique challenges due to the definition of the Sobol index as a ratio of variances estimated from Gaussian process surrogates. Consequently, learning strategies must either focus on convergence in the numerator or the denominator of this ratio. However, rapid convergence in either one does not guarantee convergence in the Sobol index. We propose a novel strategy for active learning that focuses on resolving the main effects of the Gaussian process (associated with the numerator of the Sobol index) and compare this with existing strategies based on convergence in the total variance (the denominator of the Sobol index). The new strategy, implemented through a new learning function termed the MUSIC (minimize uncertainty in Sobol index convergence), generally converges in Sobol index error more rapidly than the existing strategies based on the Expected Improvement for Global Fit (EIGF) and the Variance Improvement for Global Fit (VIGF). Both strategies are compared with simple sequential random sampling and the MUSIC learning function generally converges most rapidly for low-dimensional problems. However, for high-dimensional problems, the performance is comparable to random sampling. The new learning strategy is demonstrated for a practical case of adaptive experimental design for large-scale Boundary Layer Wind Tunnel experiments.
Highly active antiretroviral therapy and sexual risk behavior: a meta-analytic review.

CONTEXT Evidence suggests that since highly active antiretroviral therapy (HAART) became available, the prevalence of unprotected sex and the incidence of sexually transmitted infections (STIs) have increased. OBJECTIVE To conduct 3 meta-analyses to determine whether (1) being treated with HAART, (2) having an undetectable viral load, or (3) holding specific beliefs about HAART and viral load are associated with increased likelihood of engaging in unprotected sex. DATA SOURCES A comprehensive search included electronic bibliographic databases, including AIDSLINE, MEDLINE, PubMed, CINHAL, PsycInfo, ERIC, EMBASE, and Sociofile, from January 1996 to August 2003, conference proceedings, hand searches of journals, reference lists of articles, and contacts with researchers. STUDY SELECTION Twenty-five English-language studies (some contributing >1 finding) met the selection criteria and examined the association of unprotected sexual intercourse or STIs with receiving HAART (21 findings), having an undetectable viral load (13 findings), or beliefs about HAART and viral load (18 findings). DATA EXTRACTION Reports were screened and information from eligible studies was abstracted independently by pairs of reviewers using a standardized spreadsheet. DATA SYNTHESIS Random-effects models were used to aggregate data. The prevalence of unprotected sex was not higher among persons with the human immunodeficiency virus (HIV) receiving HAART (prevalence range, 9%-56%; median, 33%) vs those not receiving HAART (range, 11%-77%; median, 44%; odds ratio [OR], 0.92; 95% confidence interval [CI], 0.65-1.31) or among HIV-positive persons with an undetectable viral load (range, 10%-68%; median, 39%) vs those with a detectable viral load (range, 14%-70%; median, 42%; OR, 0.99; 95% CI, 0.82-1.21). The prevalence of unprotected sex was elevated (OR, 1.82; 95% CI, 1.52-2.17) in HIV-positive, HIV-negative, and unknown serostatus persons who believed that receiving HAART or having an undetectable viral load protects against transmitting HIV or who had reduced concerns about engaging in unsafe sex given the availability of HAART (range, 17%-81% [median, 49%] vs 9%-68% [median, 38%] for counterparts). CONCLUSIONS In the studies reviewed, HIV-positive patients receiving HAART did not exhibit increased sexual risk behavior, even when therapy achieved an undetectable viral load. However, people's beliefs about HAART and viral load may promote unprotected sex and may be amenable to change through prevention messages.
UVIT/AstroSat Investigation of a low luminous Blue Straggler Star in NGC 362: Detection of extremely low mass white dwarf as companion

In the present study, we identified an extremely low-mass white dwarf as a companion to a low luminous blue straggler star within the Galactic globular cluster NGC 362. To conduct the analysis, we utilized data obtained from various sources, including AstroSat Ultra Violet Imaging Telescope, UVOT, and the 2.2-m ESO telescope. By examining the spectral energy distribution of the blue straggler star candidate, we successfully identified an extremely low mass white dwarf as its binary companion. We determined the effective temperature, radius, and luminosity for both, the extremely low-mass white dwarf and the blue straggler star candidate. Specifically, the extremely low-mass white dwarf exhibits an effective temperature of 15000 K, a radius of 0.17 Rsun, a luminosity of 1.40 Lsun, and a mass range of 0.19-0.20 Msun. Furthermore, the position of the straggler star within the cluster suggests their formation via the Case A/B mass-transfer mechanism in a low-density environment.
HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks

This work considers the problem of heterogeneous graph-level anomaly detection. Heterogeneous graphs are commonly used to represent behaviours between different types of entities in complex industrial systems for capturing as much information about the system operations as possible. Detecting anomalous heterogeneous graphs from a large set of system behaviour graphs is crucial for many real-world applications like online web/mobile service and cloud access control. To address the problem, we propose HRGCN, an unsupervised deep heterogeneous graph neural network, to model complex heterogeneous relations between different entities in the system for effectively identifying these anomalous behaviour graphs. HRGCN trains a hierarchical relation-augmented Heterogeneous Graph Neural Network (HetGNN), which learns better graph representations by modelling the interactions among all the system entities and considering both source-to-destination entity (node) types and their relation (edge) types. Extensive evaluation on two real-world application datasets shows that HRGCN outperforms state-of-the-art competing anomaly detection approaches. We further present a real-world industrial case study to justify the effectiveness of HRGCN in detecting anomalous (e.g., congested) network devices in a mobile communication service. HRGCN is available at https://github.com/jiaxililearn/HRGCN.
Role of Environmental Factors in Shaping Spatial Distribution of Salmonella enterica Serovar Typhi , Fiji

The flood-risk model was created in 4 main steps. First, we created a map depicting depression sites (or sink areas) by using the digital elevation model (DEM) raster. A convex or depression surface was obtained with the formula; original DEM – mean DEM, where values <0 were identified as convex zones. First, a mean DEM raster was created by averaging the elevation of 10 ×10 neighboring (i.e., a 250 m × 250 m area). A depression map was then obtained by subtracting the mean DEM raster from the original DEM map, and selecting only the regions with negative pixel values. Second, areas selected as potential flooding areas where those that were convex and fall within an elevation range between 0 m and 40 m, which is approximately the elevation range corresponding to the lower alluvial plains, which is generally affected during severe flooding (1). Third, a raster map with poorly drained soils was then created by using the polygon features ranging from imperfectly to very poorly drained soils. Fourth, a new raster flood-risk map was created by using only the overlapping regions of the depressions map and the poorly drained soils map. These overlapping regions were marked as regions at high risk for flooding. Finally, a surface map estimating Euclidean distances to these high-risk flooding regions was created.
Knockdown of ccnO decreases the tumorigenicity of gastric cancer by inducing apoptosis

Cyclin O (CCNO) has been reported to be a novel protein of the cyclin family. However, the clinical significance and functional roles of CCNO in human cancer, including gastric cancer (GC), remain largely unexplored. In this study, we investigated the clinical and functional roles of CCNO in GC. Methods: We analyzed CCNO expression patterns in GC patients. To investigate the role of CCNO in malignancy of GC, we used lentivirus-delivered short hairpin RNA to knockdown CCNO expression in GC cell lines. Then multiparametric high-content screening and MTT incorporation assay were used to assess the cell proliferation capability. Cell apoptosis was detected by flow cytometry and Caspase 3/7 assays. Furthermore, the effect of CCNO on tumorigenicity of GC was also determined in vivo. Finally, microarray analysis was performed to elucidate the molecular mechanisms by which shCCNO inhibited the malignancy of GC cells. Results: The analysis from The Cancer Genome Atlas database revealed elevated CCNO mRNA expression in GC tissue than in the adjacent normal tissue. Immunohistochemical studies also showed that stronger cytoplasmic staining of CCNO was detected in GC tissues. Downregulation of CCNO in GC cells efficiently, through infection with the lentivirus-mediated specific short hairpin RNA, could significantly induce cell apoptosis and inhibit the proliferative properties both in vitro and in vivo. Microarray analysis further revealed 652 upregulated genes and 527 downregulated genes in the shCCNO group compared with control, and indicated that CCNO knockdown could inhibit the malignancy of GC cells through inducing genome-wide gene expression changes. Conclusion: Our work is the first to reveal that elevated CCNO expression is closely associated with human GC development and that CCNO knockdown could efficiently inhibit the malignant properties of GC cells by inducing cell apoptosis. Therefore, CCNO could be used as a potential biomarker for prognosis or even as a therapeutic target in human GC.
AVEC ’ 04 Testing Advanced Driver Assistance Systems for Fault Management with the VEHIL Test Facility

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Popular music pedagogy: peer learning in practice

The inclusion of popular music as a content area in music education is not uncommon. The musicological study of popular music is well established in higher education, and even the practice of popular music is becoming more common in both secondary education and the post-compulsory sector. However, when this occurs, it is likely to be taught in more or less the same way as other more established content areas like western classical music or jazz, with teachers being in control of the process and the curriculum, the feedback and the assessment. However, popular music is usually learned in the broader community as a self-directed activity, sometimes including interactions with peers and group activities, but rarely under the direction of an expert mentor/teacher. One Australian conservatorium has adopted the pedagogy of popular music through the creation of a scaffolded self-directed learning environment within its Bachelor of Popular Music programme. This paper argues that the pedagogical approach employed in this programme relates well to the prior learning activities of its students. It draws on the results of a survey of the learning experiences of students before they entered this programme, as a background to subsequent research into their participation in two course activities that provide opportunities for the provision feedback to peers. The study draws on data from surveys, online participation in the provision of work-in-progress feedback, and written feedback provided as part of formal assessment. Students are found to have usually engaged with multiple musical activities and used a variety of ways to enhance their musical abilities before commencing their conservatoire studies. These characteristics are also found to be present in students’ engagement with their degree studies. The paper concludes that these students are well prepared for this kind of peer learning activity and provide useful feedback through the structures provided by the programme.
Traffic Light Control with Reinforcement Learning

Urban traffic signal optimization is important for alleviating congestion in urban transportation systems. This study proposes a real-time traffic light control algorithm based on deep Q learning with a reward function that accounts for queue lengths, delays, travel times, and throughput. The model dynamically decides phase changes based on current traffic conditions. The training of the deep Q network involves an offline stage from pre-generated data with fixed signal timing and an online stage using real-time traffic data. A deep Q network structure with aphase gate component is used to simplify the model's learning task under different phases. Amemory palace" mechanism is used to address sample imbalance during the training process. Both synthetic and real-world traffic flow data are used to validate our approach under an urban road intersection scenario in Hangzhou, China. Results demonstrate significant performance improvements of the proposed method in reducing vehicle waiting time (57.1% to 100%), queue lengths (40.9% to 100%), and total travel time (16.8% to 68.0%) compared to traditional fixed signal timing plans.