How Easy Is It to Read Websites About Schizophrenia?

DETERMINATIONS ON THE TEACHING OF ZEYBEK PLECTRUM TECHNIQUE IN BAGLAMA LESSONS IN FINE ARTS HIGH SCHOOLS IN TURKEY

The Treatment of Soft Tissue Hydatidosis through an Oncological Approach: A Review of 13 Cases with an Average Follow-Up of 43 Months

Impact of a vaccine passport on first-dose SARS-CoV-2 vaccine coverage by age and area-level social determinants of health in the Canadian provinces of Quebec and Ontario: an interrupted time series analysis

In Canada, all provinces implemented vaccine passports in 2021 to reduce SARS-CoV-2 transmission in non-essential indoor spaces and increase vaccine uptake (policies active September 2021-March 2022 in Quebec and Ontario). We sought to evaluate the impact of vaccine passport policies on first-dose SARS-CoV-2 vaccination coverage by age, and area-level income and proportion of racialized residents.

A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference

Most evaluations of attribution methods focus on the English language. Inthis work, we present a multilingual approach for evaluating attributionmethods for the Natural Language Inference (NLI) task in terms of faithfulnessand plausibility. First, we introduce a novel cross-lingual strategy to measurefaithfulness based on word alignments, which eliminates the drawbacks oferasure-based evaluations.We then perform a comprehensive evaluation ofattribution methods, considering different output mechanisms and aggregationmethods. Finally, we augment the XNLI dataset with highlight-basedexplanations, providing a multilingual NLI dataset with highlights, to supportfuture exNLP studies. Our results show that attribution methods performing bestfor plausibility and faithfulness are different.

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitativecapabilities with increasing scale. Despite their potentially transformativeimpact, these new capabilities are as yet poorly characterized. In order toinform future research, prepare for disruptive new model capabilities, andameliorate socially harmful effects, it is vital that we understand the presentand near-future capabilities and limitations of language models. To addressthis challenge, we introduce the Beyond the Imitation Game benchmark(BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 450authors across 132 institutions. Task topics are diverse, drawing problems fromlinguistics, childhood development, math, common-sense reasoning, biology,physics, social bias, software development, and beyond. BIG-bench focuses ontasks that are believed to be beyond the capabilities of current languagemodels. We evaluate the behavior of OpenAI's GPT models, Google-internal densetransformer architectures, and Switch-style sparse transformers on BIG-bench,across model sizes spanning millions to hundreds of billions of parameters. Inaddition, a team of human expert raters performed all tasks in order to providea strong baseline. Findings include: model performance and calibration bothimprove with scale, but are poor in absolute terms (and when compared withrater performance); performance is remarkably similar across model classes,though with benefits from sparsity; tasks that improve gradually andpredictably commonly involve a large knowledge or memorization component,whereas tasks that exhibit "breakthrough" behavior at a critical scale ofteninvolve multiple steps or components, or brittle metrics; social bias typicallyincreases with scale in settings with ambiguous context, but this can beimproved with prompting.

Minimum-link $C$-Oriented Paths Visiting a Sequence of Regions in the Plane

Let $E=\{e_1,\ldots,e_n\}$ be a set of $C$-oriented disjoint segments in theplane, where $C$ is a given finite set of orientations that spans the plane,and let $s$ and $t$ be two points. %(We also require that for each orientationin $C$, its opposite orientation is also in $C$.) We seek a minimum-link$C$-oriented tour of $E$, that is, a polygonal path $\pi$ from $s$ to $t$ thatvisits the segments of $E$ in order, such that, the orientations of its edgesare in $C$ and their number is minimum. We present an algorithm for computingsuch a tour in $O(|C|^2 \cdot n^2)$ time. This problem already captures most ofthe difficulties occurring in the study of the more general problem, in which$E$ is a set of not-necessarily-disjoint $C$-oriented polygons.

JUNE-Germany: An Agent-Based Epidemiology Simulation including Multiple Virus Strains, Vaccinations and Testing Campaigns

The June software package is an open-source framework for the detailedsimulation of epidemics based on social interactions in a virtual populationreflecting age, gender, ethnicity, and socio-economic indicators in England. Inthis paper, we present a new version of the framework specifically adapted forGermany, which allows the simulation of the entire German population usingpublicly available information on households, schools, universities,workplaces, and mobility data for Germany. Moreover, JuneGermany incorporatestesting and vaccination strategies within the population as well as thesimultaneous handling of several different virus strains. First validationtests of the framework have been performed for the state of RhinelandPalatinate based on data collected between October 2020 and December 2020 andthen extrapolated to March 2021, i.e. the end of the second wave.

Model-free data-driven inelasticity in Haigh-Westergaard space -- a study how to obtain data points from measurements

Model-free data-driven computational mechanics, first proposed byKirchdoerfer and Ortiz, replaces phenomenological models with numericalsimulations based on sample data sets in strain-stress space. Recent literatureextended the approach to inelastic problems using structured data sets, tangentspace information, and transition rules. From an application perspective, thecoverage of qualified data states and calculating the corresponding tangentspace is crucial. In this respect, material symmetry significantly helps toreduce the amount of necessary data. This study applies the data-drivenparadigm to elasto-plasticity with isotropic hardening. We formulate ourapproach employing Haigh-Westergaard coordinates, providing information on theunderlying material yield surface. Based on this, we use a combinedtension-torsion test to cover the knowledge of the yield surface and a singletensile test to calculate the corresponding tangent space. The resultingdata-driven method minimizes the distance over the Haigh-Westergaard spaceaugmented with directions in the tangent space subject to compatibility andequilibrium constraints.

CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning

Extending Moore's law by augmenting complementary-metal-oxide semiconductor(CMOS) transistors with emerging nanotechnologies (X) has become increasinglyimportant. Accelerating Monte Carlo algorithms that rely on random samplingwith such CMOS+X technologies could have significant impact on a large numberof fields from probabilistic machine learning, optimization to quantumsimulation. In this paper, we show the combination of stochastic magnetictunnel junction (sMTJ)-based probabilistic bits (p-bits) with versatile FieldProgrammable Gate Arrays (FPGA) to design a CMOS + X (X = sMTJ) prototype. Ourapproach enables high-quality true randomness that is essential for Monte Carlobased probabilistic sampling and learning. Our heterogeneous computersuccessfully performs probabilistic inference and asynchronous Boltzmannlearning, despite device-to-device variations in sMTJs. A comprehensivecomparison using a CMOS predictive process design kit (PDK) reveals thatcompact sMTJ-based p-bits replace 10,000 transistors while dissipating twoorders of magnitude of less energy (2 fJ per random bit), compared to digitalCMOS p-bits. Scaled and integrated versions of our CMOS + stochastic nanomagnetapproach can significantly advance probabilistic computing and its applicationsin various domains by providing massively parallel and truly random numberswith extremely high throughput and energy-efficiency.