Sneutrino Higgs models explain lepton non-universality in CMS excesses

Recent searches for first-generation leptoquarks and heavy right-handed $W_R$ bosons have seen excesses in final states with electrons and jets. A bizarre property of these excesses is that they appear to violate lepton universality. With these results in mind, we study the phenomenology of supersymmetric models in which the Higgs arises as the sneutrino in an electron supermultiplet. Since the electron is singled out in this approach, one can naturally account for the lepton flavor structure of the excesses. In this work, we show that in such a framework, one can significantly alleviate the tension between the Standard Model and the data and yet evade current constraints from other searches. Lastly we point out that correlated excesses are expected to be seen in future multilepton searches.Comment: 17 pages, 7 figure

Probing a slepton Higgs on all frontiers

We study several aspects of supersymmetric models with a $U(1)_R$ symmetry where the Higgs doublet is identified with the superpartner of a lepton. We derive new, stronger bounds on the gaugino masses based on current measurements, and also propose ways to probe the model up to scales of $\mathcal{O}(10\, \textrm{TeV})$ at future $e^+e^-$ colliders. Since the $U(1)_R$ symmetry cannot be exact, we analyze the effects of $R$-symmetry breaking on neutrino masses and proton decay. In particular, we find that getting the neutrino mixing angles to agree with experiments in a minimal model requires a UV cutoff for the theory at around $10 \text{ TeV}$.Comment: 33 pages, 5 figures; v2: added reference. Matches version published in JHE

Building high-level features using large scale unsupervised learning

We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200x200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bodies. Starting with these learned features, we trained our network to obtain 15.8% accuracy in recognizing 20,000 object categories from ImageNet, a leap of 70% relative improvement over the previous state-of-the-art

Luciferase expression and bioluminescence does not affect tumor cell growth in vitro or in vivo

Live animal imaging is becoming an increasingly common technique for accurate and quantitative assessment of tumor burden over time. Bioluminescence imaging systems rely on a bioluminescent signal from tumor cells, typically generated from expression of the firefly luciferase gene. However, previous reports have suggested that either a high level of luciferase or the resultant light reaction produced upon addition of D-luciferin substrate can have a negative influence on tumor cell growth. To address this issue, we designed an expression vector that allows simultaneous fluorescence and luminescence imaging. Using fluorescence activated cell sorting (FACS), we generated clonal cell populations from a human breast cancer (MCF-7) and a mouse melanoma (B16-F10) cell line that stably expressed different levels of luciferase. We then compared the growth capabilities of these clones in vitro by MTT proliferation assay and in vivo by bioluminescence imaging of tumor growth in live mice. Surprisingly, we found that neither the amount of luciferase nor biophotonic activity was sufficient to inhibit tumor cell growth, in vitro or in vivo. These results suggest that luciferase toxicity is not a necessary consideration when designing bioluminescence experiments, and therefore our approach can be used to rapidly generate high levels of luciferase expression for sensitive imaging experiments

De la compétence à l’empathie : évaluation de l’évolution de la perception qu’ont les étudiants en médecine des chirurgiens dans le cadre d’un programme associant le patient comme enseignant et la réflexion basée sur les arts

Introduction: The purpose of this study was to identify whether the incorporation of a combined Patient as teacher (PAT) and arts-based reflection (ABR) program during a surgical clerkship rotation could influence more humanistic perceptions of surgeons, using an innovative evaluation approach. Methods: A novel, single question evaluation tool was created. Third year medical-students were asked to “list the top 5 attributes of a surgeon, in order of perceived importance” both before and after their surgical clerkship rotations and participation in the PAT/ABR program. Attributes identified by students were coded as either “humanistic” or “non-humanistic,” which were then analyzed using generalized linear regression models under a Bayesian framework. Results: After participation in the PAT/ABR program, the predicted probability of students ranking a humanistic characteristic as the most important attribute of a surgeon had increased by 17%, and the predicted probability of students ranking a humanistic characteristic amongst their top three attributes for a surgeon had increased by 21%. Conclusion: This innovative evaluative method suggested the success of a combined PAT/ABR program in encouraging a humanistic perspective of surgery and this approach could potentially be explored to evaluate other humanistic education initiatives.Résumé Introduction : L’objectif de cette étude était de déterminer si l’introduction d’une nouvelle approche d’évaluation associant la participation de Patients comme enseignants (PCE) à une Réflexion basée sur les arts (RBA) dans un stage d’externat en chirurgie permettait de mieux percevoir les qualités humanistes chez les chirurgiens. Méthodes : Un nouvel outil d’évaluation à question unique a été créé. Des étudiants en troisième année de médecine ont été invités à ‘’énumérer les cinq principaux attributs d’un chirurgien, par ordre d’importance perçue’’, avant et après leur stage d’externat en chirurgie et le programme PCE/RBA. Les attributs identifiés par les étudiants ont été codés comme « humanistes » ou « non humanistes », puis analysés à l’aide de modèles de régression linéaire généralisée dans un cadre bayésien. Résultats : Après leur participation au programme PCE/RBA, la probabilité prédite moyenne que les étudiants classent un trait humaniste comme l’attribut le plus important d’un chirurgien a augmenté de 17 %, et la probabilité prédite que les étudiants classent un trait humaniste parmi les trois premiers attributs d’un chirurgien a augmenté de 21 %. Conclusion : Cette méthode d’évaluation innovante porte à croire que le programme PCE/RBA réussit en effet à favoriser une vision humaniste de la chirurgie. Cette approche peut être explorée pour évaluer d’autres activités de formation axées sur l’humanisme

An Empirical Evaluation of Deep Learning on Highway Driving

Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.Comment: Added a video for lane detectio

Assessing Needs and Outcomes of Children and Youth Receiving Intensive Services

This study investigated whether children/youth in Ontario triaged to residential services showed a higher intensity of need than those referred to outpatient services, and whether residential treatment gains were sufficient for transition to community services. Participants included 2053 children/youth assessed at 23 diverse mental health agencies across Ontario using the interRAI™ Child and Youth Mental Health (ChYMH) instrument. Various presenting problems were examined utilizing scales including: Disruptive/Aggressive Behavior, Hyperactive/Distraction, Social Disengagement, Anxiety, and Sleep Difficulties. Analyses were conducted separately for boys and girls. Notable differences were found in the initial assessment, with residential boys scoring higher on all scales than outpatient boys, and residential girls scoring higher on the externalizing scales (Disruptive/Aggressive Behavior, Hyperactive/Distraction) than outpatient girls. Treatment gains at residential discharge included improvements in Anxiety, Social Disengagement, Hyperactive/Distraction and Sleep Difficulties for boys and girls to levels at or below the initial scores of outpatient peers. Disruptive/Aggressive Behavior is still a high need following residential services. The results highlight differences in severity of mental health presentation between children/youth receiving residential and outpatient services, and how multiple agencies in Ontario are providing services that successfully reduce the severity of mental health needs

Current Approaches to the Treatment of Early Hepatocellular Carcinoma

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139902/1/onco0034.pd

NAIL: An evolutionarily conserved lncRNA essential for licensing coordinated activation of p38 and NFκB in colitis

Akıncılar SC, Wu L, NG QF, et al., NAIL: an evolutionarily conserved lncRNA essential for licensing coordinated activation of p38 and NFκB in colitis. Gut Published Online First: 25 November 2020. doi: 10.1136/gutjnl-2020-32298