Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-View Fuzzy Representation Learning With Rules Based Model

Unsupervised multi-view representation learning has been studied extensively for mining multi-view data. However, some critical challenges remain. On the one hand, the existing methods cannot explore multi-view data comprehensively since they usually learn a common representation between views and ignore the specific information within each view. On the other hand, to mine the nonlinear relationship between the data, kernel or neural network methods are commonly used for multi-view representation learning but they lack interpretability. To this end, this paper proposes a new multi-view fuzzy representation learning method based on the interpretable Takagi-Sugeno-Kang (TSK) fuzzy system (MVRL_FS). The method realizes multi-view representation learning from two aspects. First, multi-view data are transformed into a high-dimensional fuzzy feature space, while the common information between views and specific information of each view are explored simultaneously. Second, a new regularization method based on <inline-formula><tex-math notation="LaTeX">${L}_{2,1}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="deng-ieq1-3295874.gif"/></alternatives></inline-formula>-norm regression is proposed to mine the consistency information between views, while the geometric structure of the data is preserved through the Laplacian graph. Extensive experiments on many benchmark multi-view datasets are conducted to validate the superiority of the proposed method.
Household Sustainable Consumption Intention

Household consumption could hurt the environment. What we collectively choose and consume on a day-to-day basis determines what’s left for future generations. As household consumption is one of the largest activities in an economy, sustainable consumption is an important key to achieving sustainability goals. Improvement in the role of individual citizens in society, concerning sustainability is vital for a better future in all aspects of the environment, society and economy. The purpose of this article is to examine how education, sustainable awareness and attitude, as well as social influence impact the intention to consume sustainably among households. The method used is quantitative where cross-sectional data was collected through an online self-administered questionnaire in which a convenience sampling method was applied to 151 respondents. The data was analyzed using partial least square structural equation modeling (PLS-SEM) by utilizing SmartPLS3 software. The findings suggest that social influence and sustainable awareness and attitude had a significant influence on the intention toward sustainable consumption among households. Education however was found to be insignificant and not sufficient as the predictor in this study. Implications for policymakers and the directions of future research are proposed.
(110) Facet of MgTe Zinc Blende Semiconductor: A Holy Grail for Modern Spintronics

In this work, we propose mono/few layers of MgTe (110) facets of zinc-blende structure for a unique next-generation ferromagnet-free nonballistic spin-field effect transistor. The atomic arrangement in this facet exhibits a unique combination of three basic symmetries found in nature: rotation, reflection, and translation. The electronic properties based on the density functional theory (DFT) reveal momentum-independent unidirectional spin polarization known as persistent spin texture (PST) that leads to an infinite spin lifetime in this particular facet. PST in these 2D structures originating from crystal symmetry makes them an ideal alternative to quantum well structures. Although the proposed Datta-Das s-FET is applicable to nonballistic s-FET, there has been little technological progress in this rapidly growing field of research and only a trivial amount of 2D semiconductors have been identified that exhibit PST intrinsically. In this regard, this particular facet studied in this work will be a great asset. A detailed theoretical insight into the origin of PST and its application in a ferromagnet-free s-FET has been proposed combining both the spin-Hall effect and inverse spin-Hall effects, harmonizing spintronics with conventional electronics.
Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering

The application of artificial intelligence to simulate air-to-air combat scenarios is attracting increasing attention. To date the high-dimensional state and action spaces, the high complexity of situation information (such as imperfect and filtered information, stochasticity, incomplete knowledge about mission targets) and the nonlinear flight dynamics pose significant challenges for accurate air combat decision-making. These challenges are exacerbated when multiple heterogeneous agents are involved. We propose a hierarchical multi-agent reinforcement learning framework for air-to-air combat with multiple heterogeneous agents. In our framework, the decision-making process is divided into two stages of abstraction, where heterogeneous low-level policies control the action of individual units, and a high-level commander policy issues macro commands given the overall mission targets. Low-level policies are trained for accurate unit combat control. Their training is organized in a learning curriculum with increasingly complex training scenarios and league-based self-play. The commander policy is trained on mission targets given pre-trained low-level policies. The empirical validation advocates the advantages of our design choices.
Generalized Kohn-Sham approach for the electronic band structure of spin-orbit coupled materials

Spin-current density functional theory (SCDFT) is a formally exact framework designed to handle the treatment of interacting many-electron systems including spin-orbit coupling at the level of the Pauli equation. In practice, robust and accurate calculations of the electronic structure of these systems call for functional approximations that depend not only on the densities, but also on spin-orbitals. Here we show that the call can be answered by resorting to an extension of the Kohn-Sham formalism, which admits the use of non-local effective potentials, yet it is firmly rooted in SCDFT. The power of the extended formalism is demonstrated by calculating the spin-orbit-induced band-splittings of inversion-asymmetric MoSe$_2$ monolayer and inversion-symmetric bulk $\alpha$-MoTe$_2$. We show that quantitative agreement with experimental data is obtainable via global hybrid approximations by setting the fraction of Fock exchange at the same level which yields accurate values of the band gap. Key to these results is the ability of the method to self-consistently account for the spin currents induced by the spin-orbit interaction. The widely used method of refining spin-density functional theory by a second-variational treatment of spin-orbit coupling is unable to match our SCDFT results.
Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction

In Grammatical Error Correction (GEC), it is crucial to ensure the user's comprehension of a reason for correction. Existing studies present tokens, examples, and hints as to the basis for correction but do not directly explain the reasons for corrections. Although methods that use Large Language Models (LLMs) to provide direct explanations in natural language have been proposed for various tasks, no such method exists for GEC. Generating explanations for GEC corrections involves aligning input and output tokens, identifying correction points, and presenting corresponding explanations consistently. However, it is not straightforward to specify a complex format to generate explanations, because explicit control of generation is difficult with prompts. This study introduces a method called controlled generation with Prompt Insertion (PI) so that LLMs can explain the reasons for corrections in natural language. In PI, LLMs first correct the input text, and then we automatically extract the correction points based on the rules. The extracted correction points are sequentially inserted into the LLM's explanation output as prompts, guiding the LLMs to generate explanations for the correction points. We also create an Explainable GEC (XGEC) dataset of correction reasons by annotating NUCLE, CoNLL2013, and CoNLL2014. Although generations from GPT-3 and ChatGPT using original prompts miss some correction points, the generation control using PI can explicitly guide to describe explanations for all correction points, contributing to improved performance in generating correction reasons.
Comparative study of Deep Learning Models for Binary Classification on Combined Pulmonary Chest X-ray Dataset

CNN-based deep learning models for disease detection have become popular recently. We compared the binary classification performance of eight prominent deep learning models: DenseNet 121, DenseNet 169, DenseNet 201, EffecientNet b0, EffecientNet lite4, GoogleNet, MobileNet, and ResNet18 for their binary classification performance on combined Pulmonary Chest Xrays dataset. Despite the widespread application in different fields in medical images, there remains a knowledge gap in determining their relative performance when applied to the same dataset, a gap this study aimed to address. The dataset combined Shenzhen, China (CH) and Montgomery, USA (MC) data. We trained our model for binary classification, calculated different parameters of the mentioned models, and compared them. The models were trained to keep in mind all following the same training parameters to maintain a controlled comparison environment. End of the study, we found a distinct difference in performance among the other models when applied to the pulmonary chest Xray image dataset, where DenseNet169 performed with 89.38 percent and MobileNet with 92.2 percent precision. Keywords: Pulmonary, Deep Learning, Tuberculosis, Disease detection, Xray
Targeted C-to-T and A-to-G dual mutagenesis system for RhtA transporter in vivo evolution

ABSTRACT T7 RNA polymerase (T7RNAP) has been fused with cytosine or adenine deaminase individually, enabling in vivo C-to-T or A-to-G transitions on DNA sequence downstream of T7 promoter, and greatly accelerated directed protein evolution. However, its base conversion type is limited. In this study, we created a dual-functional system for simultaneous C-to-T and A-to-G in vivo mutagenesis, called T7-DualMuta, by fusing T7RNAP with both cytidine deaminase (PmCDA1) and a highly active adenine deaminase (TadA-8e). The C-to-T and A-to-G mutagenesis frequencies of T7-DualMuta were 4.02 × 10−3 and 1.20 × 10−2, respectively, with 24 h culturing and distributed mutations evenly across the target gene. The T7-DualMuta system was used to in vivo directed evolution of L-homoserine transporter RhtA, resulting in efficient variants that carried the four types of base conversions by T7-DualMuta. The evolved variants greatly increased the host growth rates at L-homoserine concentrations of 8 g/L, which was not previously achieved, and demonstrated the great in vivo evolution capacity. The novel molecular device T7-DualMuta efficiently provides both C/G-to-T/A and A/T-to-G/C mutagenesis on target regions, making it useful for various applications and research in Enzymology and Synthetic Biology studies. It also represents an important expansion of the base editing toolbox. Importance A T7-DualMuta system for simultaneous C-to-T and A-to-G in vivo mutagenesis was created. The mutagenesis frequency was 4.02 × 107 fold higher than the spontaneous mutation, which was reported to be approximately 10−10 bases per nucleotide per generation. This mutant system can be utilized for various applications and research in Enzymology and Synthetic Biology studies. A T7-DualMuta system for simultaneous C-to-T and A-to-G in vivo mutagenesis was created. The mutagenesis frequency was 4.02 × 107 fold higher than the spontaneous mutation, which was reported to be approximately 10−10 bases per nucleotide per generation. This mutant system can be utilized for various applications and research in Enzymology and Synthetic Biology studies.
Communication-Efficient Ridge Regression in Federated Echo State Networks

Federated Echo State Networks represent an efficient methodology for learning in pervasive environments with private temporal data due to the low computational cost required by the learning phase. In this paper, we propose Partial Federated Ridge Regression ( pFedRR ), an approximate, communication-efficient version of the exact method for learning the readout in a federated setting. Each client compresses the local statistics to be exchanged with the server via an importance-based method, which selects the most relevant neurons with respect to the local distribution. We evaluate the methodology on two Human State Monitoring benchmarks, and results show that the importance-based selection of the information significantly reduces the communication cost, while acting as a regularization method to improve the generalization capabilities.
Trogocytosis of cancer-associated fibroblasts promotes pancreatic cancer growth and immune suppression via phospholipid scramblase anoctamin 6 (ANO6)

In pancreatic ductal adenocarcinoma (PDAC), the fibroblastic stroma constitutes most of the tumor mass and is remarkably devoid of functional blood vessels. This raises an unresolved question of how PDAC cells obtain essential metabolites and water-insoluble lipids. We have found a critical role for cancer-associated fibroblasts (CAFs) in obtaining and transferring lipids from blood-borne particles to PDAC cells via trogocytosis of CAF plasma membranes. We have also determined that CAF-expressed phospholipid scramblase anoctamin 6 (ANO6) is an essential CAF trogocytosis regulator required to promote PDAC cell survival. During trogocytosis, cancer cells and CAFs form synapse-like plasma membranes contacts that induce cytosolic calcium influx in CAFs via Orai channels. This influx activates ANO6 and results in phosphatidylserine exposure on CAF plasma membrane initiating trogocytosis and transfer of membrane lipids, including cholesterol, to PDAC cells. Importantly, ANO6-dependent trogocytosis also supports the immunosuppressive function of pancreatic CAFs towards cytotoxic T cells by promoting transfer of excessive amounts of cholesterol. Further, blockade of ANO6 antagonizes tumor growth via disruption of delivery of exogenous cholesterol to cancer cells and reverses immune suppression suggesting a potential new strategy for PDAC therapy.
Epigallocatechin Gallate Affects the Structure of Chromatosomes, Nucleosomes and Their Complexes with PARP1

The natural flavonoid epigallocatechin gallate has a wide range of biological activities, including being capable of binding to nucleic acids; however, the mechanisms of the interactions of epigallocatechin gallate with DNA organized in chromatin have not been systematically studied. In this work, the interactions of epigallocatechin gallate with chromatin in cells and with nucleosomes and chromatosomes in vitro were studied using fluorescent microscopy and single-particle Förster resonance energy transfer approaches, respectively. Epigallocatechin gallate effectively penetrates into the nuclei of living cells and binds to DNA there. The interaction of epigallocatechin gallate with nucleosomes in vitro induces a large-scale, reversible uncoiling of nucleosomal DNA that occurs without the dissociation of DNA or core histones at sub- and low-micromolar concentrations of epigallocatechin gallate. Epigallocatechin gallate does not reduce the catalytic activity of poly(ADP-ribose) polymerase 1, but causes the modulation of the structure of the enzyme–nucleosome complex. Epigallocatechin gallate significantly changes the structure of chromatosomes, but does not cause the dissociation of the linker histone. The reorganization of nucleosomes and chromatosomes through the use of epigallocatechin gallate could facilitate access to protein factors involved in DNA repair, replication and transcription to DNA and, thus, might contribute to the modulation of gene expression through the use of epigallocatechin gallate, which was reported earlier.
Self-FI: Self-Supervised Learning for Disease Diagnosis in Fundus Images

Self-supervised learning has been successful in computer vision, and its application to medical imaging has shown great promise. This study proposes a novel self-supervised learning method for medical image classification, specifically targeting ultra-wide-field fundus images (UFI). The proposed method utilizes contrastive learning to pre-train a deep learning model and then fine-tune it with a small set of labeled images. This approach reduces the reliance on labeled data, which is often limited and costly to obtain, and has the potential to improve disease detection in UFI. This method employs two contrastive learning techniques, namely bi-lateral contrastive learning and multi-modality pre-training, to form positive pairs using the data correlation. Bi-lateral learning fuses multiple views of the same patient’s images, and multi-modality pre-training leverages the complementary information between UFI and conventional fundus images (CFI) to form positive pairs. The results show that the proposed contrastive learning method achieves state-of-the-art performance with an area under the receiver operating characteristic curve (AUC) score of 86.96, outperforming other approaches. The findings suggest that self-supervised learning is a promising direction for medical image analysis, with potential applications in various clinical settings.
Coping with COVID

The recent COVID-19 pandemic has brought with it an unprecedented amount of change to the daily lives of individuals around the world. Stay at home/safer at home measures have drastically limited individuals’ face to face contact. College students have had to quickly adapt to new modes of instruction, including various forms of distance learning. Many colleges have yet to make final decisions regarding how instruction will be delivered in the coming semester. The uncertainty COVID-19 brings in regard to daily living and higher education pursuits may lead to a variety of negative outcomes for college students, including increased stress and anxiety and decreased academic performance. The current research examines how undergraduate students attending a small college in the Southwestern United States are coping with the pandemic compared to the general adult population. 289 participants completed survey items related to coping, hope, and personality. Results reveal that college students employ significantly more maladaptive coping strategies than the general population in response to the stress surrounding the pandemic. Additionally, they have significantly lower levels of hope and possess lower levels of conscientiousness, emotional engagement, and openness to new experiences. The results of this study have important implications for higher education institutions, who should take these findings into consideration as they make plans for the coming academic year.
Butterfly Recognition and Detection Method Based on Improved YOLOv5 Model

As one of the most advanced models in computer vision, the model of the YOLO series has excellent accuracy and efficiency. However, YOLOv5 has achieved great success in object detection. Even since YOLOv5 has a function of image classification, we can still modify and optimize Yolov5 based on model structure, data augmentation, loss function and optimizer, and attention mechanism. Based on the above problems, we replaced the backbone of YOLOv5 with RepVGG as the backbone network of our model. We also used methods such as Cutout, HSV, etc., for image augmentation. We also introduced CBAM (Convolutional Block Attention Module) based on the attention mechanism to improve the model's performance. Besides, we also replaced the GIOU (Generalized Intersection over Union) loss function with the CIOU (Complete-IOU). At the same time, some suitable activation function is selected according to the model, which improves the robustness of the model. Finally, we use the 'Pink Butterfly' dataset to test this model. We were pleasantly surprised to find out about the promising performance. The precision was improved by 4.451 % (from 91.950% to 96.401%). The recall declined 0.413% (from 80.198 % to 79.785%). The map@0.5 was improved 2.466 %(from 88.826 % to 91.292%). The map@0.5:0.95 was improved by 2.466 % (from 68.975 % to 73.017 %). Moreover, the model has better robustness.
X-ray - cosmic shear cross-correlations: first detection and constraints on baryonic effects

We report a first detection, at very high significance ($25\sigma$), of the cross-correlation between cosmic shear and the diffuse X-ray background, using data from the Dark Energy Survey and the ROSAT satellite. The X-ray cross-correlation signal is sensitive to the distribution of the surrounding gas in dark matter haloes. This allows us to use our measurements to place constraints on key physical parameters that determine the impact of baryonic effects in the matter power spectrum. In particular, we determine the mass of haloes in which feedback has expelled half of their gas content on average to be $\log_{10}(M_c/M_\odot)=13.643^{+0.081}_{-0.12}$, and the polytropic index of the gas to be $\Gamma = 1.231^{+0.015}_{-0.011}$. This represents a first step in the direct use of X-ray cross-correlations to obtain improved constraints on cosmology and the physics of the intergalactic gas.
Efficient Multi-Channel Speech Enhancement with Spherical Harmonics Injection for Directional Encoding

Multi-channel speech enhancement extracts speech using multiple microphones that capture spatial cues. Effectively utilizing directional information is key for multi-channel enhancement. Deep learning shows great potential on multi-channel speech enhancement and often takes short-time Fourier Transform (STFT) as inputs directly. To fully leverage the spatial information, we introduce a method using spherical harmonics transform (SHT) coefficients as auxiliary model inputs. These coefficients concisely represent spatial distributions. Specifically, our model has two encoders, one for the STFT and another for the SHT. By fusing both encoders in the decoder to estimate the enhanced STFT, we effectively incorporate spatial context. Evaluations on TIMIT under varying noise and reverberation show our model outperforms established benchmarks. Remarkably, this is achieved with fewer computations and parameters. By leveraging spherical harmonics to incorporate directional cues, our model efficiently improves the performance of the multi-channel speech enhancement.
Multi-Sideband RABBIT in Argon

We report a joint experimental and theoretical study of a three-sideband (3-SB) modiﬁcation of the “reconstruction of attosecond beating by interference of two-photon transitions” (RABBIT) setup. The 3-SB RABBIT scheme makes it possible to investigate phases resulting from interference between transitions of different orders in the continuum. Furthermore, the strength of this method is its ability to focus on the atomic phases only, independent of a chirp in the harmonics, by comparing the RABBIT phases extracted from speciﬁc SB groups formed by two adjacent harmonics. We verify earlier predictions that the phases and the corresponding time delays in the three SBs extracted from angle-integrated measurements become similar with increasing photon electron energy. A variation in the angle dependence of the RABBIT phases in the three SBs results from the distinct Wigner and continuum-continuum coupling phases associated with the individual angular momentum channels. A qualitative explanation of this dependence is attempted by invoking a propensity rule. Comparison between the experimental data and predictions from an R -matrix (close-coupling) with time dependence calculation shows qualitative agreement in the observed trends.