The Mathematics Education of Prospective Secondary Teachers Around the World

Review of the book The Mathematics Education of Prospective Secondary Teachers Around the World

Aligning Large Language Models through Synthetic Feedback

Aligning large language models (LLMs) to human values has become increasingly important as it enables sophisticated steering of LLMs, e.g., making them follow given instructions while keeping them less toxic. However, it requires a significant amount of human demonstrations and feedback. Recently, open-sourced models have attempted to replicate the alignment learning process by distilling data from already aligned LLMs like InstructGPT or ChatGPT. While this process reduces human efforts, constructing these datasets has a heavy dependency on the teacher models. In this work, we propose a novel framework for alignment learning with almost no human labor and no dependency on pre-aligned LLMs. First, we perform reward modeling (RM) with synthetic feedback by contrasting responses from vanilla LLMs with various sizes and prompts. Then, we use the RM for simulating high-quality demonstrations to train a supervised policy and for further optimizing the model with reinforcement learning. Our resulting model, Aligned Language Model with Synthetic Training dataset (ALMoST), outperforms open-sourced models, including Alpaca, Dolly, and OpenAssistant, which are trained on the outputs of InstructGPT or human-annotated instructions. Our 7B-sized model outperforms the 12-13B models in the A/B tests using GPT-4 as the judge with about 75% winning rate on average.Comment: Preprint, 9 pages (with 10 pages of supplementary

Recognition of the microbiota by Nod2 contributes to the oral adjuvant activity of cholera toxin through the induction of interleukin-1β

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152023/1/imm13105_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152023/2/imm13105.pd

Current Status of Clinical Diagnosis and Genetic Analysis of Hereditary Hemorrhagic Telangiectasia in South Korea: Multicenter Case Series and a Systematic Review

Purpose Hereditary hemorrhagic telangiectasia (HHT), a rare genetic vascular disorder, has been rarely reported in South Korea. We investigated the current prevalence and presenting patterns of genetically confirmed HHT in South Korea. Materials and Methods We defined HHT patients as those with proven mutations on known HHT-related genes (ENG, ACVRL1, SMAD4, and GDF2) or those fulfilling 3 or 4 of the Curaçao criteria. A computerized systematic search was performed in PubMed and KoreaMed using the following search term: (“hereditary hemorrhagic telangiectasia” AND “Korea”) OR (“Osler-Weber-Rendu” AND “Korea”). We also collected government health insurance data. HHT genetic testing results were collected from three tertiary hospitals in which the genetic tests were performed. We integrated patient data by analyzing each case to obtain the prevalence and presenting pattern of HHT in South Korea. Results We extracted 90 cases from 52 relevant articles from PubMed and KoreaMed. An additional 22 cases were identified from the three Korean tertiary hospitals after excluding seven cases that overlapped with those in the published articles. Finally, 112 HHT patients were identified (41 males and 71 females, aged 4–82 years [mean±standard deviation, 45.3±20.6 years]). The prevalence of HHT in South Korea is about 1 in 500,000, with an almost equal prevalence among men and women. Forty-nine patients underwent genetic testing, of whom 28 had HHT1 (ENG mutation) and 19 had HHT2 (ACVRL1 mutation); the other two patients were negative for ENG, ACVRL1, and SMAD4 mutations. Conclusion The prevalence of HHT is underestimated in Korea. The rate of phenotypic presentation seems to be similar to that found worldwide. Korean health insurance coverage is limited to representative genetic analysis to detect ENG and ACVRL1 mutations. Further genetic analyses to detect HHT3, HHT4, and other forms of HHT should be implemented

VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting

Label-efficient and reliable semantic segmentation is essential for many real-life applications, especially for industrial settings with high visual diversity, such as waste sorting. In industrial waste sorting, one of the biggest challenges is the extreme diversity of the input stream depending on factors like the location of the sorting facility, the equipment available in the facility, and the time of year, all of which significantly impact the composition and visual appearance of the waste stream. These changes in the data are called ``visual domains'', and label-efficient adaptation of models to such domains is needed for successful semantic segmentation of industrial waste. To test the abilities of computer vision models on this task, we present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting. Our challenge incorporates a fully-annotated waste sorting dataset, ZeroWaste, collected from two real material recovery facilities in different locations and seasons, as well as a novel procedurally generated synthetic waste sorting dataset, SynthWaste. In this competition, we aim to answer two questions: 1) can we leverage domain adaptation techniques to minimize the domain gap? and 2) can synthetic data augmentation improve performance on this task and help adapt to changing data distributions? The results of the competition show that industrial waste detection poses a real domain adaptation problem, that domain generalization techniques such as augmentations, ensembling, etc., improve the overall performance on the unlabeled target domain examples, and that leveraging synthetic data effectively remains an open problem. See https://ai.bu.edu/visda-2022/Comment: Proceedings of Machine Learning Researc

MUSiC : a model-unspecific search for new physics in proton-proton collisions at root s=13TeV

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1), are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.Peer reviewe

Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV

A search is presented for new particles produced at the LHC in proton-proton collisions at root s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb(-1), collected in 2017-2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb(-1), collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.Peer reviewe

Measurement of prompt open-charm production cross sections in proton-proton collisions at root s=13 TeV

The production cross sections for prompt open-charm mesons in proton-proton collisions at a center-of-mass energy of 13TeV are reported. The measurement is performed using a data sample collected by the CMS experiment corresponding to an integrated luminosity of 29 nb(-1). The differential production cross sections of the D*(+/-), D-+/-, and D-0 ((D) over bar (0)) mesons are presented in ranges of transverse momentum and pseudorapidity 4 < p(T) < 100 GeV and vertical bar eta vertical bar < 2.1, respectively. The results are compared to several theoretical calculations and to previous measurements.Peer reviewe