An inefficient dwarf: Chemical abundances and the evolution of the Ursa Minor dwarf spheroidal galaxy

We present detailed chemical element abundance ratios of 17 elements in three metal poor stars in the Ursa Minor dwarf spheroidal galaxy, which we combine with extant data from the literature to assess the predictions of a novel suite of galaxy chemical evolution models. The spectroscopic data were obtained with the Keck/HIRES instrument and revealed low metallicities of [Fe/H]=-2.12, -2.13 and -2.67 dex. While the most metal poor star in our sample shows an overabundance of [Mn/Fe] and other Fe-peak elements, our overall findings are in agreement with previous studies of this galaxy: elevated values of the [alpha/Fe] ratios that are similar to, or only slightly lower than, the halo values but with SN Ia enrichment at very low metallicity, as well as an enhancement of the ratio of first to second peak neutron capture elements [Y/Ba] with decreasing metallicity. The chemical evolution models which were tailored to reproduce the metallicity distribution function of the dSph, indicate that UMi had an extended star formation which lasted nearly 5 Gyr with low efficiency and are able to explain the [Y/Ba] enhancement at low metallicity for the first time. In particular, we show that the present day lack of gas is probably due to continuous loss of gas from the system, which we model as winds.Comment: 10 pages, 7 figures, table

Hippocampus and retrosplenial cortex combine path integration signals for successful navigation

The current study used fMRI in humans to examine goal-directed navigation in an open field environment. We designed a task that required participants to encode survey-level spatial information and subsequently navigate to a goal location in either first person, third person, or survey perspectives. Critically, no distinguishing landmarks or goal location markers were present in the environment, thereby requiring participants to rely on path integration mechanisms for successful navigation. We focused our analysis on mechanisms related to navigation and mechanisms tracking linear distance to the goal location. Successful navigation required translation of encoded survey-level map information for orientation and implementation of a planned route to the goal. Our results demonstrate that successful first and third person navigation trials recruited the anterior hippocampus more than trials when the goal location was not successfully reached. When examining only successful trials, the retrosplenial and posterior parietal cortices were recruited for goal-directed navigation in both first person and third person perspectives. Unique to first person perspective navigation, the hippocampus was recruited to path integrate self-motion cues with location computations toward the goal location. Last, our results demonstrate that the hippocampus supports goal-directed navigation by actively tracking proximity to the goal throughout navigation. When using path integration mechanisms in first person and third person perspective navigation, the posterior hippocampus was more strongly recruited as participants approach the goal. These findings provide critical insight into the neural mechanisms by which we are able to use map-level representations of our environment to reach our navigational goals

Analysis of return distributions in the coherent noise model

The return distributions of the coherent noise model are studied for the system size independent case. It is shown that, in this case, these distributions are in the shape of q-Gaussians, which are the standard distributions obtained in nonextensive statistical mechanics. Moreover, an exact relation connecting the exponent $\tau$ of avalanche size distribution and the q value of appropriate q-Gaussian has been obtained as q=(tau+2)/tau. Making use of this relation one can easily determine the q parameter values of the appropriate q-Gaussians a priori from one of the well-known exponents of the system. Since the coherent noise model has the advantage of producing different tau values by varying a model parameter \sigma, clear numerical evidences on the validity of the proposed relation have been achieved for different cases. Finally, the effect of the system size has also been analysed and an analytical expression has been proposed, which is corroborated by the numerical results.Comment: 14 pages, 3 fig

Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate

Terror attacks have been linked in part to online extremist content. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. We model this process using a combination of three contextual dimensions -- religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur'an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming. Given the potentially significant social impact, we evaluate the performance of our algorithms to minimize mislabeling, where our approach outperforms a competitive baseline by 10.2% in precision.Comment: 22 page

ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter

The convenience of social media has also enabled its misuse, potentially resulting in toxic behavior. Nearly 66% of internet users have observed online harassment, and 41% claim personal experience, with 18% facing severe forms of online harassment. This toxic communication has a significant impact on the well-being of young individuals, affecting mental health and, in some cases, resulting in suicide. These communications exhibit complex linguistic and contextual characteristics, making recognition of such narratives challenging. In this paper, we provide a multimodal dataset of toxic social media interactions between confirmed high school students, called ALONE (AdoLescents ON twittEr), along with descriptive explanation. Each instance of interaction includes tweets, images, emoji and related metadata. Our observations show that individual tweets do not provide sufficient evidence for toxic behavior, and meaningful use of context in interactions can enable highlighting or exonerating tweets with purported toxicity

Effect of cutting parameters and tool geometry on the performance analysis of one-shot drilling process of AA2024-t3

Drilling is an important machining process in various manufacturing industries. High-quality holes are possible with the proper selection of tools and cutting parameters. This study investigates the effect of spindle speed, feed rate, and drill diameter on the generated thrust force, the formation of chips, post-machining tool condition, and hole quality. The hole surface defects and the top and bottom edge conditions were also investigated using scan electron microscopy. The drilling tests were carried out on AA2024-T3 alloy under a dry drilling environment using 6 and 10 mm uncoated carbide tools. Analysis of Variance was employed to further evaluate the influence of the input parameters on the analysed outputs. The results show that the thrust force was highly influenced by feed rate and drill size. The high spindle speed resulted in higher surface roughness, while the increase in the feed rate produced more burrs around the edges of the holes. Additionally, the burrs formed at the exit side of holes were larger than those formed at the entry side. The high drill size resulted in greater chip thickness and an increased built-up edge on the cutting tools

Self-organization in dissipative optical lattices

We show that the transition from Gaussian to the q-Gaussian distributions occurring in atomic transport in dissipative optical lattices can be interpreted as self-organization by recourse to a modified version of Klimontovich's S-theorem. As a result, we find that self-organization is possible in the transition regime, only where the second moment is finite. Therefore, the nonadditivity parameter q is confined within the range 1<q<5/3, although whole spectrum of q values i.e., 1<q<3, is considered theoretically possible. The range of q values obtained from the modified S-theorem is also confirmed by the experiments carried out by Douglas et al. [Phys. Rev. Lett. 96, 110601 (2006)].Comment: 9 pages, 1 fi