A Case Study of Educational Equity in Saskatchewan Schools and Implications for Educational Development in China

This paper probes the phenomenon of underperforming indigenous students in Canada through a case study in the school district of Saskatchewan. It is discerned that the disparity between indigenous students’ home culture and the mainstream classroom culture is the major obstacle between indigenous students and academic success. Such a disparity is caused by a couple of reasons. First of all, educators’ misconception, along with education decision-makers’ ineffectiveness, leads to adversity for indigenous students to face in the classroom; secondly, biased evaluation and misjudgments in the current education system also result in indigenous students’ underperformance. Lastly, educators’ low cultural proficiency towards indigenous culture culminates in indigenous students’ low classroom engagement. The results of the case study could be enlightening for Chinese education decision-makers, given that the Chinese booming economy has caused millions of internal migrant workers to work in an alien subculture, their children could face similar social and linguistic debacles as compared to indigenous students in Saskatchewan

Deep-learning continuous gravitational waves

We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals [D. George and E. A. Huerta, Phys. Rev. D 97, 044039 (2018)10.1103/PhysRevD.97.044039; H. Gabbard, M. Williams, F. Hayes, and C. Messenger, Phys. Rev. Lett. 120, 141103 (2018)10.1103/PhysRevLett.120.141103]. We train a convolutional neural network with residual (shortcut) connections and compare its detection power to that of a fully coherent matched-filtering search using the Weave pipeline [K. Wette, S. Walsh, R. Prix, and M. A. Papa, Phys. Rev. D 97, 123016 (2018)10.1103/PhysRevD.97.123016]. As test benchmarks we consider two types of all-sky searches over the frequency range from 20 to 1000 Hz: an "easy" search using T=105 s of data, and a "harder" search using T=106 s. The detection probability pdet is measured on a signal population for which matched filtering achieves pdet=90% in Gaussian noise. In the easiest test case (T=105 s at 20 Hz) the DNN achieves pdet∼88%, corresponding to a loss in sensitivity depth of ∼5% versus coherent matched filtering. However, at higher frequencies and for longer observation times the DNN detection power decreases, until pdet∼13% and a loss of ∼66% in sensitivity depth in the hardest case (T=106 s at 1000 Hz). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search. © 2019 authors. Published by the American Physical Society

Instruct and Extract: Instruction Tuning for On-Demand Information Extraction

Large language models with instruction-following capabilities open the door to a wider group of users. However, when it comes to information extraction - a classic task in natural language processing - most task-specific systems cannot align well with long-tail ad hoc extraction use cases for non-expert users. To address this, we propose a novel paradigm, termed On-Demand Information Extraction, to fulfill the personalized demands of real-world users. Our task aims to follow the instructions to extract the desired content from the associated text and present it in a structured tabular format. The table headers can either be user-specified or inferred contextually by the model. To facilitate research in this emerging area, we present a benchmark named InstructIE, inclusive of both automatically generated training data, as well as the human-annotated test set. Building on InstructIE, we further develop an On-Demand Information Extractor, ODIE. Comprehensive evaluations on our benchmark reveal that ODIE substantially outperforms the existing open-source models of similar size. Our code and dataset are released on https://github.com/yzjiao/On-Demand-IE.Comment: EMNLP 202

Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors

In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendations are becoming popular to explore the temporal characteristics of customers' interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers' long-term stable preferences and evolutions into account. In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations. Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning. Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users. Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods.Comment: 10 pages, 7 figures, KDD 201

Proteostasis by STUB1/HSP70 complex controls sensitivity to androgen receptor targeted therapy in advanced prostate cancer.

Protein homeostasis (proteostasis) is a potential mechanism that contributes to cancer cell survival and drug resistance. Constitutively active androgen receptor (AR) variants confer anti-androgen resistance in advanced prostate cancer. However, the role of proteostasis involved in next generation anti-androgen resistance and the mechanisms of AR variant regulation are poorly defined. Here we show that the ubiquitin-proteasome-system (UPS) is suppressed in enzalutamide/abiraterone resistant prostate cancer. AR/AR-V7 proteostasis requires the interaction of E3 ubiquitin ligase STUB1 and HSP70 complex. STUB1 disassociates AR/AR-V7 from HSP70, leading to AR/AR-V7 ubiquitination and degradation. Inhibition of HSP70 significantly inhibits prostate tumor growth and improves enzalutamide/abiraterone treatments through AR/AR-V7 suppression. Clinically, HSP70 expression is upregulated and correlated with AR/AR-V7 levels in high Gleason score prostate tumors. Our results reveal a novel mechanism of anti-androgen resistance via UPS alteration which could be targeted through inhibition of HSP70 to reduce AR-V7 expression and overcome resistance to AR-targeted therapies

Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers

The segmentation of kidney layer structures, including cortex, outer stripe, inner stripe, and inner medulla within human kidney whole slide images (WSI) plays an essential role in automated image analysis in renal pathology. However, the current manual segmentation process proves labor-intensive and infeasible for handling the extensive digital pathology images encountered at a large scale. In response, the realm of digital renal pathology has seen the emergence of deep learning-based methodologies. However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation. Addressing this gap, this paper assesses the feasibility of performing deep learning based approaches on kidney layer structure segmetnation. This study employs the representative convolutional neural network (CNN) and Transformer segmentation approaches, including Swin-Unet, Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We quantitatively evaluated six prevalent deep learning models on renal cortex layer segmentation using mice kidney WSIs. The empirical results stemming from our approach exhibit compelling advancements, as evidenced by a decent Mean Intersection over Union (mIoU) index. The results demonstrate that Transformer models generally outperform CNN-based models. By enabling a quantitative evaluation of renal cortical structures, deep learning approaches are promising to empower these medical professionals to make more informed kidney layer segmentation

Copper-based charge transfer multiferroics with a d9d^9 configuration

Multiferroics are materials with a coexistence of magnetic and ferroelectric order allowing the manipulation of magnetism by applications of an electric field through magnetoelectric coupling effects. Here we propose an idea to design a class of multiferroics with a $d^9$ configuration using the magnetic order in copper-oxygen layers appearing in copper oxide high-temperature superconductors by inducing ferroelectricity. Copper-based charge transfer multiferroics SnCuO2 and PbCuO2 having the inversion symmetry breaking $P4mm$ polar space group are predicted to be such materials. The active inner s electrons in Sn and Pb hybridize with O $2p$ states leading the buckling in copper-oxygen layers and thus induces ferroelectricity, which is known as the lone pair mechanism. As a result of the $d^9$ configuration, SnCuO2 and PbCuO2 are charge transfer insulators with the antiferromagnetic ground state of the moment on Cu retaining some strongly correlated physical properties of parent compounds of copper oxide high-temperature superconductors. Our work reveals the possibility of designing multiferroics based on copper oxide high-temperature superconductors.Comment: 18 pages, 5 figures, 1 tabl

Elemental topological ferroelectrics and polar metals of few-layer materials

Ferroelectricity can exist in elemental phases as a result of charge transfers between atoms occupying inequivalent Wyckoff positions. We investigate the emergence of ferroelectricity in two-dimensional elemental materials with buckled honeycomb lattices. Various multi-bilayer structures hosting ferroelectricity are designed by stacking-engineering. Ferroelectric materials candidates formed by group IV and V elements are predicted theoretically. Ultrathin Bi films show layer-stacking-dependent physical properties of ferroelectricity, topology, and metallicity. The two-bilayer Bi film with a polar stacking sequence is found to be an elemental topological ferroelectric material. Three and four bilayers Bi films with polar structures are ferroelectric-like elemental polar metals with topological nontrivial edge states. For Ge and Sn, trivial elemental polar metals are predicted. Our work reveals the possibility of design two-dimensional elemental topological ferroelectrics and polar metals by stacking-engineering.Comment: 18 pages, 6 figure