Optimal Battery Energy Storage Placement for Transient Voltage Stability Enhancement

A placement problem for multiple Battery Energy Storage System (BESS) units is formulated towards power system transient voltage stability enhancement in this paper. The problem is solved by the Cross-Entropy (CE) optimization method. A simulation-based approach is adopted to incorporate higher-order dynamics and nonlinearities of generators and loads. The objective is to maximize the voltage stability index, which is set up based on certain grid-codes. Formulations of the optimization problem are then discussed. Finally, the proposed approach is implemented in MATLAB/DIgSILENT and tested on the New England 39-Bus system. Results indicate that installing BESS units at the optimized location can alleviate transient voltage instability issue compared with the original system with no BESS. The CE placement algorithm is also compared with the classic PSO (Particle Swarm Optimization) method, and its superiority is demonstrated in terms of fewer iterations for convergence with better solution qualities.Comment: This paper has been accepted by the 2019 IEEE PES General Meeting at Atlanta, GA in August 201

The Expressibility of Polynomial based Attention Scheme

Large language models (LLMs) have significantly improved various aspects of our daily lives. These models have impacted numerous domains, from healthcare to education, enhancing productivity, decision-making processes, and accessibility. As a result, they have influenced and, to some extent, reshaped people's lifestyles. However, the quadratic complexity of attention in transformer architectures poses a challenge when scaling up these models for processing long textual contexts. This issue makes it impractical to train very large models on lengthy texts or use them efficiently during inference. While a recent study by [KMZ23] introduced a technique that replaces the softmax with a polynomial function and polynomial sketching to speed up attention mechanisms, the theoretical understandings of this new approach are not yet well understood. In this paper, we offer a theoretical analysis of the expressive capabilities of polynomial attention. Our study reveals a disparity in the ability of high-degree and low-degree polynomial attention. Specifically, we construct two carefully designed datasets, namely $\mathcal{D}_0$ and $\mathcal{D}_1$, where $\mathcal{D}_1$ includes a feature with a significantly larger value compared to $\mathcal{D}_0$. We demonstrate that with a sufficiently high degree $\beta$, a single-layer polynomial attention network can distinguish between $\mathcal{D}_0$ and $\mathcal{D}_1$. However, with a low degree $\beta$, the network cannot effectively separate the two datasets. This analysis underscores the greater effectiveness of high-degree polynomials in amplifying large values and distinguishing between datasets. Our analysis offers insight into the representational capacity of polynomial attention and provides a rationale for incorporating higher-degree polynomials in attention mechanisms to capture intricate linguistic correlations.Comment: arXiv admin note: substantial text overlap with arXiv:2310.1168

Vision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection

In this letter, we propose a novel mmWave beam selection method based on the environment semantics that are extracted from camera images taken at the user side. Specifically, we first define the environment semantics as the spatial distribution of the scatterers that affect the wireless propagation channels and utilize the keypoint detection technique to extract them from the input images. Then, we design a deep neural network with environment semantics as the input that can output the optimal beam pairs at UE and BS. Compared with the existing beam selection approaches that directly use images as the input, the proposed semantic-based method can explicitly obtain the environmental features that account for the propagation of wireless signals, and thus reduce the burden of storage and computation. Simulation results show that the proposed method can precisely estimate the location of the scatterers and outperform the existing image or LIDAR based works

Multi-User Matching and Resource Allocation in Vision Aided Communications

Visual perception is an effective way to obtain the spatial characteristics of wireless channels and to reduce the overhead for communications system. A critical problem for the visual assistance is that the communications system needs to match the radio signal with the visual information of the corresponding user, i.e., to identify the visual user that corresponds to the target radio signal from all the environmental objects. In this paper, we propose a user matching method for environment with a variable number of objects. Specifically, we apply 3D detection to extract all the environmental objects from the images taken by multiple cameras. Then, we design a deep neural network (DNN) to estimate the location distribution of users by the images and beam pairs at multiple moments, and thereby identify the users from all the extracted environmental objects. Moreover, we present a resource allocation method based on the taken images to reduce the time and spectrum overhead compared to traditional resource allocation methods. Simulation results show that the proposed user matching method outperforms the existing methods, and the proposed resource allocation method can achieve $92\%$ transmission rate of the traditional resource allocation method but with the time and spectrum overhead significantly reduced.Comment: 34 pages, 21 figure

More comprehensive facial inversion for more effective expression recognition

Facial expression recognition (FER) plays a significant role in the ubiquitous application of computer vision. We revisit this problem with a new perspective on whether it can acquire useful representations that improve FER performance in the image generation process, and propose a novel generative method based on the image inversion mechanism for the FER task, termed Inversion FER (IFER). Particularly, we devise a novel Adversarial Style Inversion Transformer (ASIT) towards IFER to comprehensively extract features of generated facial images. In addition, ASIT is equipped with an image inversion discriminator that measures the cosine similarity of semantic features between source and generated images, constrained by a distribution alignment loss. Finally, we introduce a feature modulation module to fuse the structural code and latent codes from ASIT for the subsequent FER work. We extensively evaluate ASIT on facial datasets such as FFHQ and CelebA-HQ, showing that our approach achieves state-of-the-art facial inversion performance. IFER also achieves competitive results in facial expression recognition datasets such as RAF-DB, SFEW and AffectNet. The code and models are available at https://github.com/Talented-Q/IFER-master

Elucidating the dynamic immune responses within the ocular mucosa of rainbow trout (Oncorhynchus mykiss) after infection with Flavobacterium columnare

The eye of vertebrates is constantly faced with numerous challenges from aquatic or airborne pathogens. As a crucial first line of defense, the ocular mucosa (OM) protects the visual organ from external threats in vertebrates such as birds and mammals. However, the understanding of ocular mucosal immunity in early vertebrates, such as teleost fish, remains limited, particularly concerning their resistance to bacterial infections. To gain insights into the pivotal role of the OM in antibacterial immunity among teleost fish, we developed a bacterial infection model using Flavobacterium columnare in rainbow trout (Oncorhynchus mykiss). Here the qPCR and immunofluorescence results showed that F. columnare could invade trout OM, suggesting that the OM could be a primary target and barrier for the bacteria. Moreover, immune-related genes (il-6, il-8, il-11, cxcl10, nod1, il1-b, igm, igt, etc.) were upregulated in the OM of trout following F. columnare infection, as confirmed by qPCR, which was further proved through RNA-seq. The results of transcriptome analyses showed that bacterial infection critically triggers a robust immune response, including innate, and adaptive immune-related signaling pathways such as Toll-like, NOD-like, and C-type lectin receptor signaling pathway and immune network for IgA production, which underscores the immune role of the OM in bacterial infection. Interestingly, a substantial reduction in the expression of genes associated with visual function was observed after infection, indicating that bacterial infection could impact ocular function. Overall, our findings have unveiled a robust mucosal immune response to bacterial infection in the teleost OM for the first time, providing valuable insights for future research into the mechanisms and functions of ocular mucosal immunity in early vertebrate species

Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration—a bibliometrics and visualization analysis

BackgroundArtificial intelligence (AI) technology has made breakthroughs in spinal cord neural injury and restoration in recent years. It has a positive impact on clinical treatment. This study explores AI research’s progress and hotspots in spinal cord neural injury and restoration. It also analyzes research shortcomings related to this area and proposes potential solutions.MethodsWe used CiteSpace 6.1.R6 and VOSviewer 1.6.19 to research WOS articles on AI research in spinal cord neural injury and restoration.ResultsA total of 1,502 articles were screened, in which the United States dominated; Kadone, Hideki (13 articles, University of Tsukuba, JAPAN) was the author with the highest number of publications; ARCH PHYS MED REHAB (IF = 4.3) was the most cited journal, and topics included molecular biology, immunology, neurology, sports, among other related areas.ConclusionWe pinpointed three research hotspots for AI research in spinal cord neural injury and restoration: (1) intelligent robots and limb exoskeletons to assist rehabilitation training; (2) brain-computer interfaces; and (3) neuromodulation and noninvasive electrical stimulation. In addition, many new hotspots were discussed: (1) starting with image segmentation models based on convolutional neural networks; (2) the use of AI to fabricate polymeric biomaterials to provide the microenvironment required for neural stem cell-derived neural network tissues; (3) AI survival prediction tools, and transcription factor regulatory networks in the field of genetics were discussed. Although AI research in spinal cord neural injury and restoration has many benefits, the technology has several limitations (data and ethical issues). The data-gathering problem should be addressed in future research, which requires a significant sample of quality clinical data to build valid AI models. At the same time, research on genomics and other mechanisms in this field is fragile. In the future, machine learning techniques, such as AI survival prediction tools and transcription factor regulatory networks, can be utilized for studies related to the up-regulation of regeneration-related genes and the production of structural proteins for axonal growth

Water balance of tropical eucalypt plantations in south-eastern

Abstract Monthly, seasonal and annual water balances of Eucalyptus urophylla plantations on the Leizhou Peninsula, southeastern China were estimated in 40 m × 40 m plots at two sites with contrasting soil types. The Jijia site is located on basalt-derived clay rich soils, while the Hetou site is characterised by coarse textured soils formed on Quaternary sediments. Observations of evaporative processes (overstorey canopy interception and transpiration, and soil evaporation), soil moisture dynamics, and climate variables were collected at both sites over 2 years. Canopy interception was measured by throughfall troughs and stemflow collectors, daily transpiration was measured by the heat pulse technique in year 1 and estimated from regressions with potential evapotranspiration and available soil water in year 2, soil evaporation was measured by periodic microlysimetry and used to derive a daily soil surface resistance-matric potential relationship for estimation of daily soil evaporation throughout the study period. Soil moisture storage was measured to 4 m depth and drainage estimated as the residual term in a water balance equation. Total annual evapotranspiration (E t ) was similar at 1118 and 1150 mm at Jijia and 969 and 1024 mm at Hetou for years 1 and 2, respectively, despite 20-30% higher rainfall in year 2. These values represent 71 and 66% of annual rainfall in year 1, and 54 and 50% in year 2. Transpiration did not exceed 600 mm in either year and annual soil evaporation was 15-26% of E t , with the higher values from Jijia. The higher rainfall in year 2 was predicted to produce an increase in drainage and runoff rather than tree water use. Dry season water balances showed E t exceeded or approached rainfall, indicating water use from deep soil or ground water storages following soil water depletion, particularly at Hetou. However, storages were replenished by high wet season recharge. The differences in soil properties between the sites resulted in a three-fold greater soil water store at Jijia that provided a supply for E s , and the sandier Hetou soils with poor water holding capacity had greater wet season drainage and higher dry season abstraction from deep storages. The water use of the eucalypts does not appear to be seriously deleterious for water supply in this area

Spontaneous rotational symmetry breaking in KTaO3_3 interface superconductors

Strongly correlated electrons could display intriguing spontaneous broken symmetries in the ground state. Understanding these symmetry breaking states is fundamental to elucidate the various exotic quantum phases in condensed matter physics. Here, we report an experimental observation of spontaneous rotational symmetry breaking of the superconductivity at the interface of YAlO$_3$/KTaO$_3$ (111) with a superconducting transition temperature of 1.86 K. Both the magnetoresistance and upper critical field in an in-plane field manifest striking twofold symmetric oscillations deep inside the superconducting state, whereas the anisotropy vanishes in the normal state, demonstrating that it is an intrinsic property of the superconducting phase. We attribute this behavior to the mixed-parity superconducting state, which is an admixture of $s$-wave and $p$-wave pairing components induced by strong spin-orbit coupling. Our work demonstrates an unconventional nature of the pairing interaction in the KTaO$_3$ interface superconductor, and provides a new platform to clarify a delicate interplay of electron correlation and spin-orbit coupling.Comment: 7 pages, 4 figure