Feature Selective Networks for Object Detection

Objects for detection usually have distinct characteristics in different sub-regions and different aspect ratios. However, in prevalent two-stage object detection methods, Region-of-Interest (RoI) features are extracted by RoI pooling with little emphasis on these translation-variant feature components. We present feature selective networks to reform the feature representations of RoIs by exploiting their disparities among sub-regions and aspect ratios. Our network produces the sub-region attention bank and aspect ratio attention bank for the whole image. The RoI-based sub-region attention map and aspect ratio attention map are selectively pooled from the banks, and then used to refine the original RoI features for RoI classification. Equipped with a light-weight detection subnetwork, our network gets a consistent boost in detection performance based on general ConvNet backbones (ResNet-101, GoogLeNet and VGG-16). Without bells and whistles, our detectors equipped with ResNet-101 achieve more than 3% mAP improvement compared to counterparts on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO datasets

Ordering policies for a dual sourcing supply chain with disruption risks

Purpose: The main purpose of this article is to explore the trade-off between ordering policies and disruption risks in a dual-sourcing network under specific (or not) service level constraints, assuming that both supply channels are susceptible to disruption risks. Design/methodology/approach: Stochastic newsvendor models are presented under both the unconstrained and fill rate constraint cases. The models can be applicable for different types of disruptions related among others to the supply of raw materials, the production process, and the distribution system, as well as security breaches and natural disasters. Findings: Through the model, we obtain some important managerial insights and evaluate the value of contingency strategies in managing uncertain supply chains. Originality/value: This paper attempts to combine explicitly disruption management with risk aversion issues for a two-stage supply chain with two unreliable suppliers.Peer Reviewe

Longitudinal changes in prospective memory and their clinical correlates at 1-year follow-up in first-episode schizophrenia

This study aimed to investigate prospective memory (PM) and the association with clinical factors at 1-year follow-up in first-episode schizophrenia (FES). Thirty-two FES patients recruited from a university-affiliated psychiatric hospital in Beijing and 17 healthy community controls (HCs) were included. Time- and event-based PM (TBPM and EBPM) performances were measured with the Chinese version of the Cambridge Prospective Memory Test (CCAMPROMPT) at baseline and at one-year follow-up. A number of other neurocognitive tests were also administered. Remission was determined at the endpoint according to the PANSS score _ 3 for selected items. Repeated measures analysis of variance revealed a significant interaction between time (baseline vs. endpoint) and group (FES vs. HCs) for EBPM (F(1, 44) = 8.8, p = 0.005) and for all neurocognitive components. Paired samples ttests showed significant improvement in EBPM in FES (13.1±3.7 vs. 10.3±4.8; t = 3.065, p = 0.004), compared to HCs (15.7±3.6 vs. 16.5±2.3; t = -1.248, p = 0.230). A remission rate of 59.4% was found in the FES group. Analysis of covariance revealed that remitters performed significantly better on EBPM (14.9±2.6 vs. 10.4±3.6; F(1, 25) = 12.2, p = 0.002) than non-remitters at study endpoint. The association between EBPM and 12-month clinical improvement in FES suggests that EBPM may be a potential neurocognitive marker for the effectiveness of standard pharmacotherapy. Furthermore, the findings also imply that PM may not be strictly a trait-related endophenotype as indicated in previous studies

Building a Sustainable Learning Cycle: The Role of ‘the Formative Use of Summative Tests’ (FUST) in Promoting Students’ Developments

The study employed a descriptive mixed-methods qualitative case study approach. Material and interview-based data were collected from two EFL classes in a private international school in central China. Findings from RQ1 suggest that teacher-made summative tests were largely dependable to the extent that the tests reflect the syllabus-based construct and address students’ affective factors. Findings from RQ2 suggest that facilitating factors including in-school continuous professional development (CPD) and teacher collegiality practices may enhance FUST’s prospective role

Tunable electronic properties of graphene through controlling bonding configurations of doped nitrogen atoms

Single–layer and mono–component doped graphene is a crucial platform for a better understanding of the relationship between its intrinsic electronic properties and atomic bonding configurations. Large–scale doped graphene films dominated with graphitic nitrogen (GG) or pyrrolic nitrogen (PG) were synthesized on Cu foils via a free radical reaction at growth temperatures of 230–300 °C and 400–600 °C, respectively. The bonding configurations of N atoms in the graphene lattices were controlled through reaction temperature, and characterized using Raman spectroscopy, X–ray photoelectron spectroscopy and scanning tunneling microscope. The GG exhibited a strong n–type doping behavior, whereas the PG showed a weak n–type doping behavior. Electron mobilities of the GG and PG were in the range of 80.1–340 cm2 V−1·s−1 and 59.3–160.6 cm2 V−1·s−1, respectively. The enhanced doping effect caused by graphitic nitrogen in the GG produced an asymmetry electron–hole transport characteristic, indicating that the long–range scattering (ionized impurities) plays an important role in determining the carrier transport behavior. Analysis of temperature dependent conductance showed that the carrier transport mechanism in the GG was thermal excitation, whereas that in the PG, was a combination of thermal excitation and variable range hopping

Teaching Literary Reading for Transfer: Hugging and Bridging Designed

As researchers are looking for different strategies to reform on a straightforwardly presented instruction in the English literature classroom, the specific ends of teaching literature for transfer are sometimes neglected. That does not mean transfer is not valid in a literature course, but teachers should design a course persistently and systematically enough to foster transfer. This study revisits the hugging-bridging framework to explore the instructor methods in a literary reading course and suggests creative writing as a hub of teaching transfer. Main focus would be given to the design of hugging in class reading instruction and bridging in transferable task of writing. Though effective transfer is decided by students’ familiarity with the knowledge and proficiency in using certain knowledge, learning for transfer could be conducive to shaping a routine problem- minded concept for learners

Drantal-NeRF: Diffusion-Based Restoration for Anti-aliasing Neural Radiance Field

Aliasing artifacts in renderings produced by Neural Radiance Field (NeRF) is a long-standing but complex issue in the field of 3D implicit representation, which arises from a multitude of intricate causes and was mitigated by designing more advanced but complex scene parameterization methods before. In this paper, we present a Diffusion-based restoration method for anti-aliasing Neural Radiance Field (Drantal-NeRF). We consider the anti-aliasing issue from a low-level restoration perspective by viewing aliasing artifacts as a kind of degradation model added to clean ground truths. By leveraging the powerful prior knowledge encapsulated in diffusion model, we could restore the high-realism anti-aliasing renderings conditioned on aliased low-quality counterparts. We further employ a feature-wrapping operation to ensure multi-view restoration consistency and finetune the VAE decoder to better adapt to the scene-specific data distribution. Our proposed method is easy to implement and agnostic to various NeRF backbones. We conduct extensive experiments on challenging large-scale urban scenes as well as unbounded 360-degree scenes and achieve substantial qualitative and quantitative improvements

Spatial-Energy-Aware Dynamic Filtering with Sparse Graph Convolutions for EEG Emotion Recognition

Accurate recognition of human emotions from EEG signals plays a critical role in affective computing and human-computer interaction. However, existing methods face significant challenges in effectively capturing the sparse, dynamic, and energy-dependent characteristics of brain activity during emotional experiences. To address these challenges, we propose a novel framework, Spatial-Energy-Aware Dynamic Filtering with Sparse Graph Convolutions (SEASGC), which rethinks EEG graph modeling from three perspectives: (1) sparse graph construction to adaptively capture the essential functional relationships between brain regions, (2) dynamic and location-dependent filtering to model nonlinear interactions between EEG nodes, and (3) energy-aware feature aggregation to leverage energy changes as critical indicators of emotional intensity. By explicitly integrating these principles, SEASGC provides a more comprehensive representation of EEG signals for emotion recognition. Extensive experiments on benchmark EEG emotion datasets demonstrate that SEASGC achieves state-of-the-art performance, highlighting its effectiveness and generalizability in modeling the complex spatial-spectral dynamics of EEG signals