To eliminate the occurrence of false positives and improve accuracy measurement results in process environmental detection, a single-switch dual-mode nanosensing method should be developed for real-time situ detection. Herein, we constructed colorimetric fluorescent optical nanosensor accurate detection As(III) water. The consisted trithiocyanuric acid modified gold nanoparticles (TMT-Au NPs) amino-functionalized carbon dots (NCDs). When TMT-Au NPs were injected into NCDs solutions,...

Abstract Normative modeling frameworks such as Bayesian inference and reinforcement learning provide valuable insights into the fundamental principles governing adaptive behavior. While these are valued for their simplicity interpretability, reliance on few parameters often limits ability to capture realistic biological behavior, leading cycles of handcrafted adjustments that prone research subjectivity. Here, we present a novel approach leveraging recurrent neural networks discover...

Multi-task visual grounding involves the simultaneous execution of localization and segmentation in images based on textual expressions. The majority advanced methods predominantly focus transformer-based multimodal fusion, aiming to extract robust representations. However, ambiguity between referring expression comprehension (REC) image (RIS) is error-prone, leading inconsistencies multi-task predictions. Besides, insufficient understanding directly contributes biased target perception. To...

Reinforcement learning models are widely used in psychology and neuroscience to study cognitive processes underlying decision-making. However, accurately reliably estimating model parameters is often challenging due factors ubiquitous modeling, such as limited data, measurement noise, complexity, hindering the interpretation of these behavioral neural data. Here we evaluate whether deep method -- combining networks with modern optimization techniques can improve parameter estimation compared...

Abstract The processes involved in value evaluation and self‐control are critical when making behavioral choices. However, the evidence linking these two types of to choices intertemporal decision‐making remains elusive. As ventromedial prefrontal cortex (vmPFC), striatum, dorsolateral (dlPFC) have been associated with processes, we focused on three regions. We employed functional magnetic resonance imaging during a delayed discounting task (DDT) using relatively large sample size,...

Artificial Neural Networks (ANNs) trained on specific cognitive tasks have re-emerged as a useful tool to study the brain. However, ANNs would better aid neuroscience if given network could be easily wide range of for which neural recordings are available. Moreover, unintentional divergent implementations can produce variable results, limits their interpretability. Towards this goal, we present NeuroGym, an open-source Python package that provides large collection customizable test and...

Recently, the topic of table pre-training has attracted considerable research interest. However, how to employ boost performance tabular prediction remains an open challenge. In this paper, we propose TapTap, first attempt that leverages empower models for prediction. After on a large corpus real-world data, TapTap can generate high-quality synthetic tables support various applications including privacy protection, low resource regime, missing value imputation, and imbalanced classification....

We propose an interferometer for chiral Majorana modes where the interference effect is caused and controlled by a Josephson junction of proximity-induced topological superconductors, hence, Majorana-Josephson interferometer. This based on two-terminal quantum anomalous Hall bar, as such its transport observables exhibit patterns depending both phase length. Observing these will establish coherent further provide powerful characterization tool relevant system.

This paper proposes a novel approach for text classification by using attention mechanism. In recent works, several models based on deep learning with traditional mechanism mainly learn the weights of steps in entire text. However, information each step is filtered encoder, and same has different effects steps. full attention-based bidirectional GRU (Bi-GRU) neural network, which called FABG. FABG uses Bi-GRU to semantic text, previous current outputs at step, enables representation obtain...

Understanding the connections between artificial and biological intelligent systems can reveal fundamental principles underlying general intelligence. While many intelligence (AI) models have a neuroscience counterpart, such are largely missing in Transformer self-attention mechanism. Here, we examine relationship attention heads human episodic memory. We focus on induction heads, which contribute to in-context learning capabilities of Transformer-based large language (LLMs). demonstrate...

Synaptic plasticity is a complex phenomenon involving multiple biochemical processes that operate on different timescales. Complexity can greatly increase memory capacity when the variables characterizing synaptic dynamics have limited precision, as shown in simple retrieval problems random patterns. Here we turn to real-world problem, face familiarity detection, and show complexity be harnessed store large number of faces recognized at later time. The recognizable grows almost linearly with...

Abstract The aim of this study was to test the psychometric properties a short questionnaire for work‐related stress entitled Work Well index (WWi) and its interaction with different variables self‐reported health. An online survey conducted in sample 1,218 employees (51% female) four countries an international insurance company. Internal consistency reliability, factorial validity, convergent validity criterion 10‐item WWi were analyzed. Good internal reliability obtained (Cronbach's α...

Abstract We investigate theoretically the field-free orientation of CO molecules induced by a single-cycle THz laser pulse train. It is shown that molecular can be obviously enhanced applying The intensity and number have some effects on orientation. high degree |⟨cos θ ⟩| max =0.9246 obtained at temperature T =0 K. variations maximum with experimentally available peak are given. Temperature has considerable influence maximal =0, 10, 20 30 K for N =14 E 0 =1.8 MV/cm =0.9188, 0.7338, 0.6055...

Abstract One of the most fundamental organizational principles brain is separation excitatory (E) and inhibitory (I) neurons. In addition to their opposing effects on post-synaptic neurons, E I cells tend differ in selectivity connectivity. Although many such differences have been characterized experimentally, it not clear why they exist first place. We studied this question an artificial neural network equipped with multiple cell types. found that a deep convolutional recurrent trained...

Abstract Synaptic plasticity is a complex phenomenon involving multiple biochemical processes that operate on different timescales. We recently showed this complexity can greatly increase the memory capacity of neural networks when variables characterize synaptic dynamics have limited precision, as in biological systems. These types synapses been tested mostly simple retrieval problems random and uncorrelated patterns. Here we turn to real-world problem, face familiarity detection, show also...

<title>Abstract</title> Exchange bias (EB) is a fundamental phenomenon in widespread information technologies. However, comprehensive understanding of its microscopic origin remains great challenge. One key issue the debate role frustration and disorder EB mechanism, which motivates exploration effect spin glass (SG) systems. Here, SG state Cr-doped Hund’s metal CsFe2As2, we discover giant with maximum field ~ 2 Tesla, almost two orders magnitude larger than that traditional alloy SGs. Our...

The study attempts to find out whether the asymmetric language switch exists in second learners and what correlation between cost learners' competence based on ERP effects.