Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatio-Temporal Transformer for Online Video Understanding

Leading methods in the field of online video understanding try to extract useful information from the spatial and temporal dimensions of an input video. But they are suffering from two problems: (1) These methods can only extract local video information, and cannot relate to the important features of the temporal context in the video. (2) Although some methods can quickly process the information of each frame in the video, the processing efficiency of the whole video is not good, so this type of method cannot be applied to online video understanding tasks. This article introduces a Transformer-based network, which considers spatial and temporal content, and can quickly process each video at the same time. Our approach can efficiently handle up to 170 videos with hundreds of frames per second for action classification. Our method achieve 10 to 90 times faster than existing methods on the action classification datasets.
OP-K-Means: Optimized Algorithm for Recommendation System Based on User Preferences

For a recommender system (RS), it is difficult to capture all the user’s interest lists simultaneously, which leads to the problem of insufficient performance of the existing joint RS based on the K-Means clustering algorithm. In this paper (1), we introduce a cluster optimization method OP -K-means for user preference data. This method starts with propagation from the center of the user preference data. By selecting relatively distant positions between each initial center, the distance between them is increased as much as possible. (2) Finally, we validate the effectiveness of our algorithm on a dataset from Facebook and compare our algorithm with original K-means. Our experimental results justify the validity of our OP -K-means algorithm.
Satellite Segmentation with Pre-trained CNN Models

In a generic satellite relative pose estimation pipeline, finding sufficient features in objects is quite essential to build the correct matching relationship and then solve the relative movement. However, for low-earth-orbit (LEO) satellites, since the earth background contains much more texture than objects, an object segmentation process is necessary to provide a prior range for feature extraction. In this work, we address this task with the pre-trained Deeplabv3 and fully convolutional network (FCN). Unlike the fine-tuning or transfer learning processes in other researches, we obtain probabilistic maps from the high-dimensional output of the above-mentioned CNN models and achieve a rough satellite extraction. Our method makes Deeplabv3 and FCN models work in a totally unfamiliar LEO scene and still achieves 0.2927 and 0.2122 in average intersection over union (IoU) respectively.
Prediction of Liquid Holdup in Horizontal Stratified Two-Phase Flow

This paper provides a combined theoretical and experimental investigation into the prediction ofhold-up ioi a stratified two-phase concurrent flow in a horizontal circular pipe. The test section, l0 m long, with an inside diameter 54 mm was made of transparent acrylic glass to permit visual observation of the flow patterns. The experiments were carried out under various air and water flow rates in the regime of smooth and wavy stratified flows. Stainless ring electrodes were mounted flush in the tube wall for measuring the liquid hold-up which is defined as the ratio of the crosssectional area filled with liquid to the total crossectional area of the pipe. Calculation method for predicting the liquid hold-up was developed by using the Taitel and Dukler momentum balance.The iatio of interfaciat friction factor and superfrcial gas-wall friction factor,(f1lfs6) was assumed to be constant. Hold-up curves calculated by this method are compared with present experimental data and those of other researchers. A ratio of f;/f56 ,which corresponds with the flow conditions, (laminar or turbulent) are presented.
Multiple Transformer Mining for VizWiz Image Caption

This paper proposes a multiple transformer mining al-gorithm (MTMA) for the VizWiz image captioning task. MTMA consists of grid image feature extraction, OCR and object detectors to effectively describe the image information. Self-Critical Sequence Training (SCST) approach is adopted for image captioning models in the training phase, and semantic similarity aggregation is adopted in the post-processing phase. Meanwhile, ensemble power is leveraged in multi-modal feature fusion and post-caption generation to further enhance the performance. As a result, the proposed algorithm outperforms others with 94.06 CIDEr.
Algoritmo de gestión para la recarga de vehículos eléctricos

Este articulo presenta un esquema de gestion para la recarga de vehiculos electricos, considerando que la penetracion masiva de estos puede generar efectos negativos en las redes de distribucion, comprometiendo la confiabilidad, calidad y seguridad del sistema. Con el algoritmo propuesto, el operador de red se beneficia al evitar la sobrecarga del circuito de distribucion, reducir las perdidas de potencia y mejorar el perfil de tension. El modelo planteado asigna una carga inicial a cada vehiculo siguiendo una funcion de probabilidad que representa la energia requerida segun los patrones de movilidad de la ciudad, la capacidad de la bateria y el consumo. Finalmente, se comprueba la efectividad del esquema de gestion propuesto analizando un caso de estudio para la ciudad de Bogota.
Gallistel's metatheory of action

The book describes three elementary units of action the reflex, the oscillator, and the servomechanism and the principles by which they are combined to make complex units. The combining of elementary units to make complex units gives behavior and the neural circuitry underlying behavior a hierarchical structure. Circuits at higher levels govern the operation of lower circuits by selective potentiation and depotentiation: by regulating the potential for operation in lower circuits raising the potential for some and lowering it for others a higher unit establishes the overall pattern to be exhibited in the combined operations of the lower units, while leaving it to the lower units to determine the details of the implementation of this pattern. A theory of motivation of the kind long championed by ethologists and physiological psychologists grows out of the notion of the selective potentiation and depotentiation of hierarchically structured units of behavior. The question then becomes how this conception of motivation may be integrated with a conception of a learned representation of the world to yield a theory of how animals produce novel motivated behavioral sequences based on learned representations. The representation of space, which is central to the behavior of organisms at least as lowly as the digger wasp, is a promising domain for the investigation of this question. Another promising area is the representation of skilled movements, such as handwriting. A number of issues in cognitive and developmental psychology are illumined by this theory about the organization of action. Among these are the degrees-of-freedom problem, the role of practice in development and in the learning of skilled action, and the role of mental rotation of spatial representations.
The hippocampus seen in the context of declarative and procedural control

Abstract Various apparently incompatible theories of hippocampal function have been proposed but integration is now needed. It is argued that the involvement of the hippocampus is most clearly seen when the animal needs to extrapolate beyond current sensory information. Such control can involve both the initiation of behaviour in the absence of appropriate sensory input and the inhibition of behaviour that might otherwise be triggered by current sensory input.
South Carolina's Colonial Land Market: An Analysis of Rural Property Sales, 1720-1775

This study analyzes noncredit, rural property transfers from colonial South Carolina. These records are used to measure the frequency, annual timing, geographic spread, and turnover rate of land sales. These data also are used to derive a hedonic land-price index. We argue that these estimates reflect variations in the local expectations of future economic growth and conclude that the rapid increase in land prices reflected the fact that the Lowcountry economy was indeed fueled by plantation agriculture.
Of Rules and Speakers

The study of the institutional development of the U.S. House of Representatives, long a mainstay of traditional scholarship, has recently undergone a revival with the rise of rational choice approaches of interpretation. In earlier years, most research was highly formal and descriptive; the emphasis was on legislative offices and parliamentary rules. While scholars noted changes in the formal structure of the House of Representatives, their descriptions of these changes consisted mostly of anecdotal accounts in which theoretical explanations played little part. More recently, many scholars have defined institutions primarily or even solely in terms of procedural rules and, from that perspective, have focused on organizational form as an important factor in the empowerment of legislative coalitions and the expression of member preferences. However, because rules are frequently viewed as a rigid structural setting for legislative behavior, institutional change has often been downplayed as outside the scope of most research.
WSR #9 125-134 / 9

culture to another (Leininger 1995). What is naturally known or constitutes proof in one culture may not be understood or considered relevant in another culture. As a consequence, there are different ways of gathering, understanding, and/or applying information. These variations can influence researchers of diverse cultures to conduct and guide the research process in a fashion atypical to the linear, quantitative research studies. This article is a description of one American Indian researcher’s experience prior to and while conducting research for a phenomenological nursing doctoral thesis, “The Lived Experience of Ojibwa and Cree Women Healers” (Struthers 1999). This author’s intention is to share a personal approach to research that was culturally sensitive, balanced, harmonious, circular, sacred, natural, holistic, and unfolded with fluidity and grace for the researcher and research participants. (“American Indian” is used synonymously with “Native American,” “Native,” “aboriginal,” and “indigenous.”) A problem identified by the researcher was that the research process is customarily designed to occur in a linear fashion. This includes selecting a research topic, reviewing literature related to the subject, formulating a research question, choosing a research design, conducting the research, analyzing data, forming a conclusion and implications, plus potentially publishing the research results. Additionally, research studies are reported in a linear construct that includes the following 125
02 Amporn p30-62.indd

This article examines the roles of transnational media in the lives of Shan communities in Burma with a focus on their consumption of Thai satellite television, the main window to the outside world available to them. It will analyse how the Shan appropriation of transnational television creates a new site of identity transcending national boundaries and expressing an ambivalent sense of interaction with mediated modernity. The paper will demonstrate the role of transnational television as a catalyst for the emergence of Shan migration to Thailand.
PCAT-UNet: UNet-like network fused convolution and transformer for retinal vessel segmentation

The accurate segmentation of retinal vessels images can not only be used to evaluate and monitor various ophthalmic diseases, but also timely reflect systemic diseases such as diabetes and blood diseases. Therefore, the study on segmentation of retinal vessels images is of great significance for the diagnosis of visually threatening diseases. In recent years, especially the convolutional neural networks (CNN) based on UNet and its variant have been widely used in various medical image tasks. However, although CNN has achieved excellent performance, it cannot learn global and long-distance semantic information interaction well due to the local computing characteristics of convolution operation, which limits the development of medical image segmentation tasks. Transformer, currently popular in computer vision, has global computing features, but due to the lack of low-level details, local feature information extraction is insufficient. In this paper, we propose Patches Convolution Attention based Transformer UNet (PCAT-UNet), which is a U-shaped network based on Transformer with a Convolution branch. We use skip connection to fuse the deep and shallow features of both sides. By taking advantage of the complementary advantages of both sides, we can effectively capture the global dependence relationship and the details of the underlying feature space, thus improving the current problems of insufficient extraction of retinal micro vessels feature information and low sensitivity caused by easily predicting of pixels as background. In addition, our method enables end-to-end training and rapid inference. Finally, three publicly available retinal vessels datasets (DRIVE, STARE and CHASE_DB1) were used to evaluate PCAT-UNet. The experimental results show that the proposed PCAT-UNET method achieves good retinal vessel segmentation performance on these three datasets, and is superior to other architectures in terms of AUC, Accuracy and Sensitivity performance indicators. AUC reached 0.9872, 0.9953 and 0.9925, Accuracy reached 0.9622, 0.9796 and 0.9812, Sensitivity reached 0.8576, 0.8703 and 0.8493, respectively. In addition, PCAT-UNET also achieved good results in two other F1-Score and Specificity indicators.
Death-Associated Protein Kinase 1 (DAPK1) Protects against Myocardial Injury Induced by Myocardial Infarction in Rats via Inhibition of Inflammation and Oxidative Stress

Objective Heart failure and ventricular remodeling after acute myocardial infarction (AMI) are important factors affecting the prognosis of patients. Therefore, we expected to explore the therapeutic target of AMI by studying the effect of death-associated protein kinase 1 (DAPK1) on AMI rat model. Materials and Methods We used male Sprague-Dawley rats to make AMI model, and after 1, 3, 7, and 14 d, we detect the success rate of modeling and the expression change of DAPK1 through 2, 3, 5-triphenyl tetrazolium chloride staining, myocardial injury markers detection, echocardiographic detection, and histological experiment. In addition, we determined the effect of DAPK1 on AMI by subcutaneous injection of the DAPK1 inhibitor (TC-DAPK 6). The effect of DAPK1 on cardiomyocytes has also been verified in cell experiments on H9c2 cells. Results The expression of DAPK1 in AMI rats was significantly higher than that in sham group, and it increased with time. The expression of inflammatory factors (interleukin- (IL-) 1β, IL-6, and tumor necrosis factor-α) in AMI rats treated by TC-DAPK 6 was reduced. In addition, TC-DAPK 6 also reduced the activity of malonaldehyde and increased the activities of superoxide dismutase, glutathione, and catalase. The expression of antioxidant molecules such as peroxiredoxin1/4 and glutathione peroxidase1/3 was also promoted by TC-DAPK 6. In H9c2 cells, TC-DAPK 6 also reduced its oxidative stress level. Conclusions The increase of DAPK1 may be related to the pathogenesis of AMI. DAPK1 inhibitors protect cardiomyocytes from AMI-induced myocardial injury by reducing levels of inflammation and oxidative stress in myocardial tissue and cells.
Chronic Obstructive Pulmonary Disease Is Not Associated with a Poor Prognosis in COVID-19

Background The effect of underlying chronic obstructive pulmonary disease (COPD) on coronavirus disease 2019 (COVID-19) during a pandemic is controversial. The purpose of this study was to examine the prognosis of COVID-19 according to the underlying COPD. Methods COVID-19 patients were assessed using nationwide health insurance data. Comorbidities were evaluated using the modified Charlson Comorbidity Index (mCCI) which excluded COPD from conventional CCI scores. Baseline characteristics were assessed. Univariable and multiple logistic and linear regression analyses were performed to determine effects of variables on clinical outcomes. Ages, sex, mCCI, socioeconomic status, and underlying COPD were selected as variables. Results COPD patients showed older age (71.3±11.6 years vs. 47.7±19.1 years, p<0.001), higher mCCI (2.6±1.9 vs. 0.8±1.3, p<0.001), and higher mortality (22.9% vs. 3.2%, p<0.001) than non-COPD patients. The intensive care unit admission rate and hospital length of stay were not significantly different between the two groups. All variables were associated with mortality in univariate analysis. However, underlying COPD was not associated with mortality unlike other variables in the adjusted analysis. Older age (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.11–1.14; p<0.001), male sex (OR, 2.29; 95% CI, 1.67–3.12; p<0.001), higher mCCI (OR, 1.30; 95% CI, 1.20–1.41; p<0.001), and medical aid insurance (OR, 1.55; 95% CI, 1.03–2.32; p=0.035) were associated with mortality. Conclusion Underlying COPD is not associated with a poor prognosis of COVID-19.
Rayleigh EigenDirections (REDs): GAN latent space traversals for multidimensional features

We present a method for finding paths in a deep generative model's latent space that can maximally vary one set of image features while holding others constant. Crucially, unlike past traversal approaches, ours can manipulate multidimensional features of an image such as facial identity and pixels within a specified region. Our method is principled and conceptually simple: optimal traversal directions are chosen by maximizing differential changes to one feature set such that changes to another set are negligible. We show that this problem is nearly equivalent to one of Rayleigh quotient maximization, and provide a closed-form solution to it based on solving a generalized eigenvalue equation. We use repeated computations of the corresponding optimal directions, which we call Rayleigh EigenDirections (REDs), to generate appropriately curved paths in latent space. We empirically evaluate our method using StyleGAN2 on two image domains: faces and living rooms. We show that our method is capable of controlling various multidimensional features out of the scope of previous latent space traversal methods: face identity, spatial frequency bands, pixels within a region, and the appearance and position of an object. Our work suggests that a wealth of opportunities lies in the local analysis of the geometry and semantics of latent spaces.
Inverse Faraday effect in massive Dirac electrons

We study the inverse Faraday effect (IFE) in a Dirac Hamiltonian with random impurities using Keldysh formalism and diagrammatic perturbation theory. The mass term in the Dirac Hamiltonian is essential for IFE, where the spin magnetic moment induced by circularly polarized light is proportional to the frequency of the incident light within the THz regime. For massive Dirac electrons, the corrections due to short-range impurities on spin magnetic moment vertex exhibits mixing of the spin magnetic moment vertex and spin angular momentum vertex. The spin magnetic density response is divergently enhanced by the vertex corrections near the band edge, indicting a long-range diffusion of spin density profile in massive Dirac electrons.
Enabling Synchronous Uplink NOMA in Wi-Fi Networks

Non-Orthogonal Multiple Access (NOMA) is a promising technology for future Wi-Fi, In uplink NOMA, stations with different channel conditions transmit simultaneously at the same frequency by splitting the signal by power level. Since Wi-Fi uses random access, the implementation of uplink NOMA in Wi-Fi faces many challenges. The paper presents a data transmission mechanism in Wi-Fi networks that enables synchronous uplink NOMA, where multiple stations start data transmission to the access point simultaneously. The developed mechanism can work with the legacy Enhanced Distributed Channel Access (EDCA) mechanism in Wi-Fi. With simulation, it is shown that the developed mechanism can double the total throughput and geometric mean throughput compared with the legacy EDCA.
Blind Image Deblurring: a Review

This is a review on blind image deblurring. First, we formulate the blind image deblurring problem and explain why it is challenging. Next, we bring some psychological and cognitive studies on the way our human vision system deblurs. Then, relying on several previous reviews, we discuss the topic of metrics and datasets, which is non-trivial to blind deblurring. Finally, we introduce some typical optimization-based methods and learning-based methods.
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems

We consider the problem of controlling an unknown stochastic linear system with quadratic costs - called the adaptive LQ control problem. We re-examine an approach called ''Reward Biased Maximum Likelihood Estimate'' (RBMLE) that was proposed more than forty years ago, and which predates the ''Upper Confidence Bound'' (UCB) method as well as the definition of ''regret'' for bandit problems. It simply added a term favoring parameters with larger rewards to the criterion for parameter estimation. We show how the RBMLE and UCB methods can be reconciled, and thereby propose an Augmented RBMLE-UCB algorithm that combines the penalty of the RBMLE method with the constraints of the UCB method, uniting the two approaches to optimism in the face of uncertainty. We establish that theoretically, this method retains $\Tilde{\mathcal{O}}(\sqrt{T})$ regret, the best-known so far. We further compare the empirical performance of the proposed Augmented RBMLE-UCB and the standard RBMLE (without the augmentation) with UCB, Thompson Sampling, Input Perturbation, Randomized Certainty Equivalence and StabL on many real-world examples including flight control of Boeing 747 and Unmanned Aerial Vehicle. We perform extensive simulation studies showing that the Augmented RBMLE consistently outperforms UCB, Thompson Sampling and StabL by a huge margin, while it is marginally better than Input Perturbation and moderately better than Randomized Certainty Equivalence.
Migration of self-propelling agent in a turbulent environment with minimal energy consumption

We present a numerical study of training a self-propelling agent to migrate in the unsteady flow environment. We control the agent to utilize the background flow structure by adopting the reinforcement learning algorithm to minimize energy consumption. We considered the agent migrating in two types of flows: one is simple periodical double-gyre flow as a proof-of-concept example, while the other is complex turbulent Rayleigh–Bénard convection as a paradigm for migrating in the convective atmosphere or the ocean. The results show that the smart agent in both flows can learn to migrate from one position to another while utilizing background flow currents as much as possible to minimize the energy consumption, which is evident by comparing the smart agent with a naive agent that moves straight from the origin to the destination. In addition, we found that compared to the double-gyre flow, the flow field in the turbulent Rayleigh–Bénard convection exhibits more substantial fluctuations, and the training agent is more likely to explore different migration strategies; thus, the training process is more difficult to converge. Nevertheless, we can still identify an energy-efficient trajectory that corresponds to the strategy with the highest reward received by the agent. These results have important implications for many migration problems such as unmanned aerial vehicles flying in a turbulent convective environment, where planning energy-efficient trajectories are often involved.