480 research outputs found
Preoperative Estimation and Resection of Gliomas Using Positron Emission Tomography/Computed Tomography Neuronavigation
Automaticity in processing spatial-numerical associations: Evidence from a perceptual orientation judgment task of Arabic digits in frames.
Human adults are faster to respond to small/large numerals with their left/right hand when they judge the parity of numerals, which is known as the SNARC (spatial-numerical association of response codes) effect. It has been proposed that the size of the SNARC effect depends on response latencies. The current study introduced a perceptual orientation task, where participants were asked to judge the orientation of a digit or a frame surrounding the digit. The present study first confirmed the SNARC effect with native Chinese speakers (Experiment 1) using a parity task, and then examined whether the emergence and size of the SNARC effect depended on the response latencies (Experiments 2, 3, and 4) using a perceptual orientation judgment task. Our results suggested that (a) the automatic processing of response-related numerical-spatial information occurred with Chinese-speaking participants in the parity task; (b) the SNARC effect was also found when the task did not require semantic access; and (c) the size of the effect depended on the processing speed of the task-relevant dimension. Finally, we proposed an underlying mechanism to explain the SNARC effect in the perceptual orientation judgment task
Deconfounded Causal Collaborative Filtering
Recommender systems may be confounded by various types of confounding factors
(also called confounders) that may lead to inaccurate recommendations and
sacrificed recommendation performance. Current approaches to solving the
problem usually design each specific model for each specific confounder.
However, real-world systems may include a huge number of confounders and thus
designing each specific model for each specific confounder is unrealistic. More
importantly, except for those "explicit confounders" that researchers can
manually identify and process such as item's position in the ranking list,
there are also many "latent confounders" that are beyond the imagination of
researchers. For example, users' rating on a song may depend on their current
mood or the current weather, and users' preference on ice creams may depend on
the air temperature. Such latent confounders may be unobservable in the
recorded training data. To solve the problem, we propose a deconfounded causal
collaborative filtering model. We first frame user behaviors with unobserved
confounders into a causal graph, and then we design a front-door adjustment
model carefully fused with machine learning to deconfound the influence of
unobserved confounders. The proposed model is able to handle both global
confounders and personalized confounders. Experiments on real-world e-commerce
datasets show that our method is able to deconfound unobserved confounders to
achieve better recommendation performance.Comment: 9 pages, 5 figures; comments and suggestions are highly appreciate
A Label-Free Electrochemical Immunosensor for Carbofuran Detection Based on a Sol-Gel Entrapped Antibody
In this study, an anti-carbofuran monoclonal antibody (Ab) was immobilized on the surface of a glassy carbon electrode (GCE) using silica sol-gel (SiSG) technology. Thus, a sensitive, label-free electrochemical immunosensor for the direct determination of carbofuran was developed. The electrochemical performance of immunoreaction of antigen with the anti-carbofuran monoclonal antibody was investigated by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), in which phosphate buffer solution containing [Fe(CN)6]3â/4â was used as the base solution for test. Because the complex formed by the immunoreaction hindered the diffusion of [Fe(CN)6]3â/4â on the electrode surface, the redox peak current of the immunosensor in the CV obviously decreased with the increase of the carbofuran concentration. The pH of working solution, the concentration of Ab and the incubation time of carbofuran were studied to ensure the sensitivity and conductivity of the immunosensor. Under the optimal conditions, the linear range of the proposed immunosensor for the determination of carbofuran was from 1 ng/mL to 100 ÎŒg/mL and from 50 ÎŒg/mL to 200 ÎŒg/mL with a detection limit of 0.33 ng/mL (S/N = 3). The proposed immunosensor exhibited good high sensitivity and stability, and it was thus suitable for trace detection of carbofuran pesticide residues
Facing Unknown: Open-World Encrypted Traffic Classification Based on Contrastive Pre-Training
Traditional Encrypted Traffic Classification (ETC) methods face a significant
challenge in classifying large volumes of encrypted traffic in the open-world
assumption, i.e., simultaneously classifying the known applications and
detecting unknown applications. We propose a novel Open-World Contrastive
Pre-training (OWCP) framework for this. OWCP performs contrastive pre-training
to obtain a robust feature representation. Based on this, we determine the
spherical mapping space to find the marginal flows for each known class, which
are used to train GANs to synthesize new flows similar to the known parts but
do not belong to any class. These synthetic flows are assigned to Softmax's
unknown node to modify the classifier, effectively enhancing sensitivity
towards known flows and significantly suppressing unknown ones. Extensive
experiments on three datasets show that OWCP significantly outperforms existing
ETC and generic open-world classification methods. Furthermore, we conduct
comprehensive ablation studies and sensitivity analyses to validate each
integral component of OWCP.Comment: Accepted by 2023 IEEE ISCC, 6 pages, 5 figure
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