Simple Modules Research Articles

Image Restoration via Frequency Selection.

Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain by considering the large discrepancy between spectra of sharp/degraded image pairs. However, these algorithms commonly utilize transformation tools, e.g., wavelet transform, to split features into several frequency parts, which is not flexible enough to select the most informative frequency component to recover. In this paper, we exploit a multi-branch and content-aware module to decompose features into separate frequency subbands dynamically and locally, and then accentuate the useful ones via channel-wise attention weights. In addition, to handle large-scale degradation blurs, we propose an extremely simple decoupling and modulation module to enlarge the receptive field via global and window-based average pooling. Furthermore, we merge the paradigm of multi-stage networks into a single U-shaped network to pursue multi-scale receptive fields and improve efficiency. Finally, integrating the above designs into a convolutional backbone, the proposed Frequency Selection Network (FSNet) performs favorably against state-of-the-art algorithms on 20 different benchmark datasets for 6 representative image restoration tasks, including single-image defocus deblurring, image dehazing, image motion deblurring, image desnowing, image deraining, and image denoising.

An orthogonal realization of representations of the Temperley–Lieb algebra

Under a suitable hypothesis, we construct a full set of pairwise orthogonal maximal vectors in V⊗n, where V=V(1) is the simple module of highest weight 1 for the quantized enveloping algebra U(sl2). We give a number of applications, one of which is an orthogonal basis of the simple modules for the Temperley–Lieb algebra TLn. We relate this new orthogonal basis to the standard cellular basis.

Automatic detection of standing dead trees based on improved YOLOv7 from airborne remote sensing imagery.

Detecting and localizing standing dead trees (SDTs) is crucial for effective forest management and conservation. Due to challenges posed by mountainous terrain and road conditions, conducting a swift and comprehensive survey of SDTs through traditional manual inventory methods is considerably difficult. In recent years, advancements in deep learning and remote sensing technology have facilitated real-time and efficient detection of dead trees. Nevertheless, challenges persist in identifying individual dead trees in airborne remote sensing images, attributed to factors such as small target size, mutual occlusion and complex backgrounds. These aspects collectively contribute to the increased difficulty of detecting dead trees at a single-tree scale. To address this issue, the paper introduces an improved You Only Look Once version 7 (YOLOv7) model that incorporates the Simple Parameter-Free Attention Module (SimAM), an unparameterized attention mechanism. This improvement aims to enhance the network's feature extraction capabilities and increase the model's sensitivity to small target dead trees. To validate the superiority of SimAM_YOLOv7, we compared it with four widely adopted attention mechanisms. Additionally, a method to enhance model robustness is presented, involving the replacement of the Complete Intersection over Union (CIoU) loss in the original YOLOv7 model with the Wise-IoU (WIoU) loss function. Following these, we evaluated detection accuracy using a self-developed dataset of SDTs in forests. The results indicate that the improved YOLOv7 model can effectively identify dead trees in airborne remote sensing images, achieving precision, recall and mAP@0.5 values of 94.31%, 93.13% and 98.03%, respectively. These values are 3.67%, 2.28% and 1.56% higher than those of the original YOLOv7 model. This improvement model provides a convenient solution for forest management.

Emerging Fe3+ Doped Broad NIR‐Emitting Phosphor Ca2.5Hf2.5(Ga, Al)3O12: Fe3+ for LWUV Pumped NIR LED

AbstractFe3+ is a promising dopant for designing near‐infrared (NIR) phosphors due to its broadband emitting, non‐toxic, and inexpensive properties. However, the 4E (4D) and 4E + 4A1 (4G) levels of Fe3+ ions are independent of the crystal‐field parameter, and the spectral regulation has become a huge challenge. Herein, a broadband NIR‐emitting phosphor Ca2.5Hf2.5(Ga, Al)3O12: Fe3+ is successfully developed toward long‐wave ultraviolet (LWUV) light‐pumped NIR phosphor‐converted LED (pc‐LED). Significantly, the excited transition reverse of Ca2.5Hf2.5Ga3O12: Fe3+ can be realized by simply adjusting Fe3+ concentration, which results in the largely enhanced excitation around 410 nm and matches well with the commercial LWUV LED. Moreover, Ca2.5Hf2.5Ga3O12: Fe3+ can exhibit a broadband NIR emission centered at 770 nm with optimized doping content at 0.01 mol%, which are demonstrated to ascribe to Fe3+ occupying both octahedral Hf 4+ and tetrahedral Ga3+ sites. Further, a simple cation modulation strategy is proposed to successfully break the lattice symmetry around Fe3+ ions and largely enhance the NIR‐emission intensity to 200% as much as before. Finally, a NIR pc‐LED is fabricated by employing Ca2.5Hf2.5Ga3O12: Fe3+, Al3+ coating on a 410 nm LED chip, which shows great potential in non‐destructive inspection applications.

Classical Whittaker modules for the classical affine Kac-Moody algebras

Inspired by Sugawara operators, we introduce a new basis for the universal non-degenerate Whittaker module of level κ over the classical affine Kac-Moody algebra of type BN(1), CN(1) or DN(1). This basis is different from the one derived from the PBW basis and would help us to understand the structure of Whittaker modules. As a result, we classify simple non-degenerate Whittaker modules for the classical affine Kac-Moody algebras whether at the critical or noncritical level.

G-Small Intersection Graph of a Module

Let be a commutative ring with identity, and be a left -module. The g-small intersection graph of non-trivial submodules of , indicated by , is a simple undirected graph whose vertices are in one-to-one correspondence with all non-trivial submodules of and two distinct vertices are adjacent if and only if the intersection of the corresponding submodules is a g-small submodule of . In this article, the interplay among the algebraic properties of , and the graph properties of are studied. Properties of such as connectedness, and completeness are considered. Besides, the girth and the diameter of are determined, as well as presenting a formula to compute the clique and domination numbers of . The graph is complete if, is a generalized hollow module or is a direct sum of two simple modules, is proved.

A Matrix converter-interfaced PM Generator System with Active Filtering Capabilities

In this paper is presented a matrix converter- interfaced Permanent Magnet (PM) generator system which can provide shunt active filtering in addition to converting and transferring energy. The modulation technique is the simple and well-known indirect space vector modulation. The filtering current injected by the matrix converter and the input filter capacitor voltage are controlled by cascaded proportional control loops. Results showing that the matrix converter successfully provides active power filtering are given. Simulations also indicate that the filtering capability of the system depends on the generator side power factor. An explanation to this effect is given.

SWVR: A Lightweight Deep Learning Algorithm for Forest Fire Detection and Recognition

The timely and effective detection of forest fires is crucial for environmental and socio-economic protection. Existing deep learning models struggle to balance accuracy and a lightweight design. We introduce SWVR, a new lightweight deep learning algorithm. Utilizing the Reparameterization Vision Transformer (RepViT) and Simple Parameter-Free Attention Module (SimAM), SWVR efficiently extracts fire-related features with reduced computational complexity. It features a bi-directional fusion network combining top-down and bottom-up approaches, incorporates lightweight Ghost Shuffle Convolution (GSConv), and uses the Wise Intersection over Union (WIoU) loss function. SWVR achieves 79.6% accuracy in detecting forest fires, which is a 5.9% improvement over the baseline, and operates at 42.7 frames per second. It also reduces the model parameters by 11.8% and the computational cost by 36.5%. Our results demonstrate SWVR’s effectiveness in achieving high accuracy with fewer computational resources, offering practical value for forest fire detection.

Divergent Fe-Mediated C-H Activation Paths Driven by Alkali Cations.

The association of the ferrous complex FeIICl2(dmpe)2 (1) with alkali bases M(hmds) (M = Li, Na, K) proves to be an efficient platform for the activation of Ar-H bonds. Two mechanisms can be observed, leading to either Ar-FeII species by deprotonative ferration or hydrido species Ar-FeII-H by oxidative addition of transient Fe0(dmpe)2 generated by reduction of 1. Importantly, the nature of the alkali cation in M(hmds) has a strong influence on the preferred path. Starting from the same iron precursor, diverse catalytic applications can be explored by a simple modulation of the MI cation. Possible strategies enabling cross-coupling using arenes as pro-nucleophiles, reductive dehydrocoupling, or deuteration of B-H bonds are discussed.

Classification of simple smooth modules over the Heisenberg–Virasoro algebra

In this paper, we classify simple smooth modules over the mirror Heisenberg–Virasoro algebra ${\mathfrak {D}}$ , and simple smooth modules over the twisted Heisenberg–Virasoro algebra $\bar {\mathfrak {D}}$ with non-zero level. To this end we generalize Sugawara operators to smooth modules over the Heisenberg algebra, and develop new techniques. As applications, we characterize simple Whittaker modules and simple highest weight modules over ${\mathfrak {D}}$ . A vertex-algebraic interpretation of our result is the classification of simple weak twisted and untwisted modules over the Heisenberg–Virasoro vertex algebras. We also present a few examples of simple smooth ${\mathfrak {D}}$ -modules and $\bar {\mathfrak {D}}$ -modules induced from simple modules over finite dimensional solvable Lie algebras, that are not tensor product modules of Virasoro modules and Heisenberg modules. This is very different from the case of simple highest weight modules over $\mathfrak {D}$ and $\bar {\mathfrak {D}}$ which are always tensor products of simple Virasoro modules and simple Heisenberg modules.

A new modulation approach to decrease total harmonic distortion of the SPWM voltage waveform using genetic optimization technique

The pulse width modulated power inverters have been increasingly using to convert DC power to AC power in small wind plants. When these inverters are used for power conversion, the integrated output voltage waveform is inevitably distorted. Although increasing the switching frequency is one choice to achieve the smoother output voltage, it adds extra harmonics to the output. There are various techniques such as analogue filtering, harmonic elimination etc. to reduce the number of harmonics usually affecting the system performance. In this study, a simple but efficient modulation approach based on the optimization of the shape of the triangular voltage waveform is proposed. The results have shown that the total harmonic distortion of the optimized voltage waveform decreases gradually and thus helps improve on the power quality during the conversion.

SparGD: A Sparse GEMM Accelerator with Dynamic Dataflow

Deep learning has become a highly popular research field, and previously deep learning algorithms ran primarily on CPUs and GPUs. However, with the rapid development of deep learning, it was discovered that existing processors could not meet the specific large-scale computing requirements of deep learning, and custom deep learning accelerators have become popular. The majority of the primary workloads in deep learning are general matrix-matrix multiplications (GEMMs), and emerging GEMMs are highly sparse and irregular. The TPU and SIGMA are typical GEMM accelerators in recent years, but the TPU does not support sparsity, and both the TPU and SIGMA have insufficient utilization rates of the Processing Element (PE). We design and implement SparGD, a sparse GEMM accelerator with dynamic dataflow. SparGD has specific PE structures, flexible distribution networks and reduction networks, and a simple dataflow switching module. When running sparse and irregular GEMMs, SparGD can maintain high PE utilization while utilizing sparsity, and can switch to the optimal dataflow according to the computing environment. For sparse, irregular GEMMs, our experimental results show that SparGD outperforms systolic arrays by 30 times and SIGMA by 3.6 times.

Successful reintroduction of species: improving on windows of opportunity for biodiversity repair

To overcome resistance of degraded ecological communities to restorative interventions, we need to understand windows of opportunity—limited time frames when species reintroduction attempts are still successful. More specifically, we need to understand what makes these windows close, as this may enable us to stretch or reopen them. We investigated this using models of simple food web modules. We show how joint changes of bottom–up and top–down control may be applied to change windows of opportunity and increase reintroduction success. Which reintroduction densities were most effective seemed system‐specific. A more general result is that reintroduction success was strongly enhanced by low to intermediate carrying capacities of basal species (e.g. periphyton and other algae in streams). This can be seen as equivalent to low to intermediate nutrient levels. When these were too high, almost any combination of restorative measures was rendered ineffective. Interestingly, reintroducing primarily and secondarily lost species at the same time was more effective than sequential reintroductions that first attempted to fix secondary extinctions. We could further enhance the success of species reintroductions by reducing the carrying capacities of basal species before the reintroduction of primarily and secondarily lost species. We discuss our results in the light of empirical work on macro‐invertebrates in streams. This serves to exemplify how our results can be applied in the practice of ecological restoration.

Anti-Software Attack Ear Identification System Using Deep Feature Learning and Blockchain Protection

Ear recognition has made good progress as an emerging biometric technology. However, the recognition performance, generalization ability, and feature robustness of ear recognition systems based on hand-crafted features are relatively poor. With the development of deep learning, these problems have been partly overcome. However, the recognition performance of existing ear recognition systems still needs to be improved when facing unconstrained ear databases in realistic scenarios. Another critical problem is that most systems with ear feature template databases are vulnerable to software attacks that disclose users’ privacy and even bring down the system. This paper proposes a software-attack-proof ear recognition system using deep feature learning and blockchain protection to address the problem that the recognition performance of existing systems is generally poor in the face of unconstrained ear databases in realistic scenarios. First, we propose an accommodative DropBlock (AccDrop) to generate drop masks with adaptive shapes. It has an advantage over DropBlock in coping with unconstrained ear databases. Second, we introduce a simple and parameterless attention module that uses 3D weights to refine the ear features output from the convolutional layer. To protect the security of the ear feature template database and the user’s privacy, we use Merkle tree nodes to store the ear feature templates, ensuring the determinism of the root node in the smart contract. We achieve Rank-1 (R1) recognition accuracies of 83.87% and 96.52% on the AWE and EARVN1.0 ear databases, which outperform most advanced ear recognition systems.

A Dual-Branch Fusion Network Based on Reconstructed Transformer for Building Extraction in Remote Sensing Imagery.

Automatic extraction of building contours from high-resolution images is of great significance in the fields of urban planning, demographics, and disaster assessment. Network models based on convolutional neural network (CNN) and transformer technology have been widely used for semantic segmentation of buildings from high resolution remote sensing images (HRSI). However, the fixed geometric structure and the local receptive field of the convolutional kernel are not good at global feature extraction, and the transformer technique with self-attention mechanism introduces computational redundancies and extracts local feature details poorly in the process of modeling the global contextual information. In this paper, a dual-branch fused reconstructive transformer network, DFRTNet, is proposed for efficient and accurate building extraction. In the encoder, the traditional transformer is reconfigured by designing the local and global feature extraction module (LGFE); the branch of global feature extraction (GFE) performs dynamic range attention (DRA) based on the idea of top-k attention for extracting global features; furthermore, the branch of local feature extraction (LFE) is used to obtain fine-grained features. The multilayer perceptron (MLP) is employed to efficiently fuse the local and global features. In the decoder, a simple channel attention module (CAM) is used in the up-sampling part to enhance channel dimension features. Our network achieved the best segmentation accuracy on both the WHU and Massachusetts building datasets when compared to other mainstream and state-of-the-art methods.

Character formulas in category 𝒪_{𝓅}

Let O p \mathcal {O}_p denote the characteristic p > 0 p>0 version of the ordinary category O \mathcal {O} for a semisimple complex Lie algebra. In this paper we give some (formal) character formulas in O p \mathcal {O}_p . First we concentrate on the irreducible characters. Here we give explicit formulas for how to obtain all irreducible characters from the characters of the finitely many restricted simple modules as well as the characters of a small number of infinite dimensional simple modules in O p \mathcal {O}_p with specified highest weights. We next prove a strong linkage principle for Verma modules which allow us to split O p \mathcal {O}_p into a finite direct sum of linkage classes. There are corresponding translation functors and we use these to further cut down the set of irreducible characters needed for determining all others. Then we show that the twisting functors on O \mathcal {O} carry over to twisting functors on O p \mathcal {O}_p , and as an application we prove a character sum formula for Jantzen-type filtrations of Verma modules with antidominant highest weights. Finally, we record formulas relating the characters of the two kinds of tilting modules in O p \mathcal {O}_p .

Finite Group Modular Field Extensions, Green Theory and Absolutely Indecomposable and Simple Modules

Finite Group Modular Field Extensions, Green Theory and Absolutely Indecomposable and Simple Modules

The 3-cyclic quantum Weyl algebras, their prime spectra and a classification of simple modules ($q$ is not a root of unity)

The 3-cyclic quantum Weyl algebra A=A(\alpha, \beta, \gamma;q^{2}) , where \alpha, \beta, \gamma, q^{2} \in \mathbb{K} (a ground field), is a quadratic Noetherian domain of Gelfand–Kirillov dimension 3 that is generated by three subalgebras each of them is either a quantum plane or the quantum Weyl algebra. For the algebras A , their prime, completely prime, primitive and maximal spectra are described together with containments of prime ideals (the Zariski–Jacobson topology on the spectrum) and simple A -modules are classified when q^{2} is not a root of unity. For each prime ideal, an explicit set of ideal generators is given. The centre Z(A) of A is \mathbb{K}[\Omega] where \Omega is a cubic element. A semisimplicity criterion for the category of finite dimensional A -modules is given. Criteria are presented for all ideals of the algebra A to commute and for each ideal of A to be a unique product of primes (up to order).

On the structure of irreducible Yetter-Drinfeld modules over $ D $

A class of algebras $ D(m, d, \xi) $ introduced by <sup>[<span class="xref"><a href="#b22" ref-type="bibr">22</a></span>]</sup> were not pointed and generated by the coradical of $ D(m, d, \xi) $. Let $ D $ be the quotient of $ D(m, d, \xi) $ module the principle ideal $ (g^m-1) $. First, we describe all simple left modules of $ D $. Then, according to Radford's method, we construct the Yetter-Drinfeld module over $ D $ by the tensor product of a simple module of $ D $ and $ D $ itself. Hence, we find some simple left Yetter-Drinfeld modules over $ D $, and the relevant braidings are of a triangular type.

CalibNet: Dual-Branch Cross-Modal Calibration for RGB-D Salient Instance Segmentation.

In this study, we propose a novel approach for RGB-D salient instance segmentation using a dual-branch cross-modal feature calibration architecture called CalibNet. Our method simultaneously calibrates depth and RGB features in the kernel and mask branches to generate instance-aware kernels and mask features. CalibNet consists of three simple modules, a dynamic interactive kernel (DIK) and a weight-sharing fusion (WSF), which work together to generate effective instance-aware kernels and integrate cross-modal features. To improve the quality of depth features, we incorporate a depth similarity assessment (DSA) module prior to DIK and WSF. In addition, we further contribute a new DSIS dataset, which contains 1,940 images with elaborate instance-level annotations. Extensive experiments on three challenging benchmarks show that CalibNet yields a promising result, i.e., 58.0% AP with 320×480 input size on the COME15K-E test set, which significantly surpasses the alternative frameworks. Our code and dataset will be publicly available at: https://github.com/PJLallen/CalibNet.