Results 31 to 40 of about 357,414 (246)

An MLWE-Based Cut-and-Choose Oblivious Transfer Protocol

Entropy
The existing lattice-based cut-and-choose oblivious transfer protocol is constructed based on the learning-with-errors (LWE) problem, which generally has the problem of inefficiency.
Yongli Tang, Menghao Guo, Yachao Huo, Zongqu Zhao, Jinxia Yu, Baodong Qin   +5 more
doaj   +1 more source

Improving PARP inhibitor efficacy in bladder cancer without genetic BRCAness by combination with PLX51107

Molecular Oncology, EarlyView.
Clinical trials on PARP inhibitors in urothelial carcinoma (UC) showed limited efficacy and a lack of predictive biomarkers. We propose SLFN5, SLFN11, and OAS1 as UC‐specific response predictors. We suggest Talazoparib as the better PARP inhibitor for UC than Olaparib.
Jutta Schmitz, Anna L. Bartkowiak, Michael Rose, Nora Kolks, Patrick Petzsch, Vandana Solanki, Anne Stoffel, Bianca Faßbender, Leandra Lepping, Julka Volkamer, Karl Köhrer, Marc Seifert, Tokameh Mahmoudi, Tahlita C. M. Zuiverloon, Günter Niegisch, Michèle J. Hoffmann   +15 more
wiley   +1 more source

Active Learning with Expected Error Reduction

, 2022
Active learning has been studied extensively as a method for efficient data collection. Among the many approaches in literature, Expected Error Reduction (EER) (Roy and McCallum) has been shown to be an effective method for active learning: select the candidate sample that, in expectation, maximally decreases the error on an unlabeled set. However, EER
Mussmann, Stephen, Reisler, Julia, Tsai, Daniel, Mousavi, Ehsan, O'Brien, Shayne, Goldszmidt, Moises   +5 more
openaire   +2 more sources

RaMBat: Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects

Molecular Oncology, EarlyView.
To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley   +1 more source

Genetic attenuation of ALDH1A1 increases metastatic potential and aggressiveness in colorectal cancer

Molecular Oncology, EarlyView.
Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova, Zuzana Kozovska, Ondrej Pos, Kristina Pavlov, Sachin Gulati, Peter Makovicky, Kristina Jakic, Monika Burikova, Eva Sedlackova, Barbora Svitkova, Silvia Tyciakova, Vojtech Bystry, Nicolas Blavet, Boris Tichy, Matej Hrnciar, Jaroslav Budis, Miroslav Tomas, Peter Dubovan, Georgina Kolnikova, Veronika Repaska, Nikoleta Mojzesova, Eva Zomborska, Daniel Pindak, Michal Mego, Tomas Szemes, Miroslava Matuskova   +25 more
wiley   +1 more source

A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts

Applied Sciences
The lattice-based multi-identity fully homomorphic encryption scheme combines the quantum security of lattice cryptography with the advantage of identity-based encryption.
Ziwei Wang, Ruwei Huang, Xiyi Wei
doaj   +1 more source

NEWMAN ERROR ANALYSIS (NEA): DETECTION OF STUDENT LEARNING BARRIERS IN PPKM IN MATHEMATICS SUBJECTS

Aksioma: Jurnal Program Studi Pendidikan Matematika, 2021
The research aims to analyze students' mistakes in solving straight-line equations and then find the learning barriers experienced during distance learning conducted in the 8th grade of a superior private junior high school in Surakarta City.
Sutama Sutama, Yuni Putri Indriyani
doaj   +1 more source

Learning with three factors: modulating Hebbian plasticity with errors

Current Opinion in Neurobiology, 2017
Synaptic plasticity is a central theme in neuroscience. A framework of three-factor learning rules provides a powerful abstraction, helping to navigate through the abundance of models of synaptic plasticity. It is well-known that the dopamine modulation of learning is related to reward, but theoretical models predict other functional roles of the ...
Łukasz Kuśmierz, Takuya Isomura, Taro Toyoizumi   +2 more
openaire   +2 more sources

Prediction of Myasthenia Gravis Worsening: A Machine Learning Algorithm Using Wearables and Patient‐Reported Measures

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Myasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life‐threatening crises. Timely interventions may be delayed by limited access to care and fragmented documentation. Our objective was to develop predictive algorithms for MG deterioration using multimodal telemedicine data ...
Maike Stein, Haoqi Sun, Sophie Lehnerer, Lea Gerischer, Maximilian Mönch, Christian Meisel, Andreas Meisel, Pushpa Narayanaswami   +7 more
wiley   +1 more source

Inner product encryption from ring learning with errors

Cybersecurity, 2020
The functional encryption scheme designed using the lattice can realize fine-grained encryption and it can resist quantum attacks. Unfortunately, the sizes of the keys and ciphertexts in cryptographic applications based on learning with errors are large,
Shisen Fang, Shaojun Yang, Yuexin Zhang
doaj   +1 more source