Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion-Driven Visual Tempo Learning for Video-Based Action Recognition

Action visual tempo characterizes the dynamics and the temporal scale of an action, which is helpful to distinguish human actions that share high similarities in visual dynamics and appearance. Previous methods capture the visual tempo either by sampling raw videos with multiple rates, which require a costly multi-layer network to handle each rate, or by hierarchically sampling backbone features, which rely heavily on high-level features that miss fine-grained temporal dynamics. In this work, we propose a Temporal Correlation Module (TCM), which can be easily embedded into the current action recognition backbones in a plug-in-and-play manner, to extract action visual tempo from low-level backbone features at single-layer remarkably. Specifically, our TCM contains two main components: a Multi-scale Temporal Dynamics Module (MTDM) and a Temporal Attention Module (TAM). MTDM applies a correlation operation to learn pixel-wise fine-grained temporal dynamics for both fast-tempo and slow-tempo. TAM adaptively emphasizes expressive features and suppresses inessential ones via analyzing the global information across various tempos. Extensive experiments conducted on several action recognition benchmarks, e.g. Something-Something V1&V2, Kinetics-400, UCF-101, and HMDB-51, have demonstrated that the proposed TCM is effective to promote the performance of the existing video-based action recognition models for a large margin. The source code is publicly released at https://github.com/zphyix/TCM.
On Learning Mixture of Linear Regressions in the Non-Realizable Setting

While mixture of linear regressions (MLR) is a well-studied topic, prior works usually do not analyze such models for prediction error. In fact, {\em prediction} and {\em loss} are not well-defined in the context of mixtures. In this paper, first we show that MLR can be used for prediction where instead of predicting a label, the model predicts a list of values (also known as {\em list-decoding}). The list size is equal to the number of components in the mixture, and the loss function is defined to be minimum among the losses resulted by all the component models. We show that with this definition, a solution of the empirical risk minimization (ERM) achieves small probability of prediction error. This begs for an algorithm to minimize the empirical risk for MLR, which is known to be computationally hard. Prior algorithmic works in MLR focus on the {\em realizable} setting, i.e., recovery of parameters when data is probabilistically generated by a mixed linear (noisy) model. In this paper we show that a version of the popular alternating minimization (AM) algorithm finds the best fit lines in a dataset even when a realizable model is not assumed, under some regularity conditions on the dataset and the initial points, and thereby provides a solution for the ERM. We further provide an algorithm that runs in polynomial time in the number of datapoints, and recovers a good approximation of the best fit lines. The two algorithms are experimentally compared.
DGSVis: Visual Analysis of Hierarchical Snapshots in Dynamic Graph

Dynamic graph visualization attracts researchers' concentration as it represents time-varying relationships between entities in multiple domains (e.g., social media analysis, academic cooperation analysis, team sports analysis). Integrating visual analytic methods is consequential in presenting, comparing, and reviewing dynamic graphs. Even though dynamic graph visualization is developed for many years, how to effectively visualize large-scale and time-intensive dynamic graph data with subtle changes is still challenging for researchers. To provide an effective analysis method for this type of dynamic graph data, we propose a snapshot generation algorithm involving Human-In-Loop to help users divide the dynamic graphs into multi-granularity and hierarchical snapshots for further analysis. In addition, we design a visual analysis prototype system (DGSVis) to assist users in accessing the dynamic graph insights effectively. DGSVis integrates a graphical operation interface to help users generate snapshots visually and interactively. It is equipped with the overview and details for visualizing hierarchical snapshots of the dynamic graph data. To illustrate the usability and efficiency of our proposed methods for this type of dynamic graph data, we introduce two case studies based on basketball player networks in a competition. In addition, we conduct an evaluation and receive exciting feedback from experienced visualization experts.
Surround-View Fisheye Camera Perception for Automated Driving: Overview, Survey & Challenges

Surround-view fisheye cameras are commonly used for near-field sensing in automated driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover 360° around the vehicle capturing the entire near-field region. Some primary use cases are automated parking, traffic jam assist, and urban driving. There are limited datasets and very little work on near-field perception tasks as the focus in automotive perception is on far-field perception. In contrast to far-field, surround-view perception poses additional challenges due to high precision object detection requirements of 10cm and partial visibility of objects. Due to the large radial distortion of fisheye cameras, standard algorithms cannot be extended easily to the surround-view use case. Thus, we are motivated to provide a self-contained reference for automotive fisheye camera perception for researchers and practitioners. Firstly, we provide a unified and taxonomic treatment of commonly used fisheye camera models. Secondly, we discuss various perception tasks and existing literature. Finally, we discuss the challenges and future direction.
RENs: Relevance Encoding Networks

The manifold assumption for high-dimensional data assumes that the data is generated by varying a set of parameters obtained from a low-dimensional latent space. Deep generative models (DGMs) are widely used to learn data representations in an unsupervised way. DGMs parameterize the underlying low-dimensional manifold in the data space using bottleneck architectures such as variational autoencoders (VAEs). The bottleneck dimension for VAEs is treated as a hyperparameter that depends on the dataset and is fixed at design time after extensive tuning. As the intrinsic dimensionality of most real-world datasets is unknown, often, there is a mismatch between the intrinsic dimensionality and the latent dimensionality chosen as a hyperparameter. This mismatch can negatively contribute to the model performance for representation learning and sample generation tasks. This paper proposes relevance encoding networks (RENs): a novel probabilistic VAE-based framework that uses the automatic relevance determination (ARD) prior in the latent space to learn the data-specific bottleneck dimensionality. The relevance of each latent dimension is directly learned from the data along with the other model parameters using stochastic gradient descent and a reparameterization trick adapted to non-Gaussian priors. We leverage the concept of DeepSets to capture permutation invariant statistical properties in both data and latent spaces for relevance determination. The proposed framework is general and flexible and can be used for the state-of-the-art VAE models that leverage regularizers to impose specific characteristics in the latent space (e.g., disentanglement). With extensive experimentation on synthetic and public image datasets, we show that the proposed model learns the relevant latent bottleneck dimensionality without compromising the representation and generation quality of the samples.
Towards Green AI with tensor networks - Sustainability and innovation enabled by efficient algorithms

The current standard to compare the performance of AI algorithms is mainly based on one criterion: the model's accuracy. In this context, algorithms with a higher accuracy (or similar measures) are considered as better. To achieve new state-of-the-art results, algorithmic development is accompanied by an exponentially increasing amount of compute. While this has enabled AI research to achieve remarkable results, AI progress comes at a cost: it is unsustainable. In this paper, we present a promising tool for sustainable and thus Green AI: tensor networks (TNs). Being an established tool from multilinear algebra, TNs have the capability to improve efficiency without compromising accuracy. Since they can reduce compute significantly, we would like to highlight their potential for Green AI. We elaborate in both a kernel machine and deep learning setting how efficiency gains can be achieved with TNs. Furthermore, we argue that better algorithms should be evaluated in terms of both accuracy and efficiency. To that end, we discuss different efficiency criteria and analyze efficiency in an exemplifying experimental setting for kernel ridge regression. With this paper, we want to raise awareness about Green AI and showcase its positive impact on sustainability and AI research. Our key contribution is to demonstrate that TNs enable efficient algorithms and therefore contribute towards Green AI. In this sense, TNs pave the way for better algorithms in AI.
Investigation of the Factors Affecting the Homicide Counts in the USA by Quantile Regression Cinayet Sayısını Faktörlerin

This study has conducted in the United States of America (USA) in the 1960s, 1970s, 1980s, and 1990s to determine whether divorce rates, unemployment rates, and the population had an impact on homicide numbers. Initially, all variables were examined and interpreted geographically on the map by districts. Subsequently, the stationary circum-stance of variables has been tested with the Augmented Dickey-Fuller (ADF) Test, which is one of the unit root tests. After it has found that the variables did not need to stabilize, regression analysis has performed by the Least Squares (LS) method. Quantile regression, which is an alternative method to the LS method, has been used since all the resulting models do not have a normal distribution. These models have been created with 3 diverse quantile values for each period. Among these models, the ones with the highest correlation coefficient are the models having the 0.75 quantile value. Therefore, the results have been obtained from models with the 0.75 quantile value. Hence, for the homicide counts in the USA, those have found that the country population had a positive effect in the 1960s, the country population and the divorce rates had positive effects in the 1970s, the country population had a positive effect in the 1980s, and the country population and the unemployment rates had positive effects in the 1990s. Furthermore, the unemployment rates in the 1970s and 1980s had a negative effect on the homicide counts in the USA.
PART I. MATHEMATICAL FORMUL ATION

A reasonable and practical method of finding an optimal fault-plane solution and evaluating the degree of its non-uniqueness from a set of inaccurate geodetic data is developed by using the inversion technique. The static deformation fields are modelled by a multiple-fault system in an elastic half-space, which consists of several dimensional faults with various variables, such as the magnitude and direction of a slip, the length, width, depth and location of a fault, the dip-angle and strike direction. An-alytical expressions for the elements of the coefficient matrix which relates a set of model parameters to a set of data parameters are given in section for a case of surface displacement. introducing for convergence a measure of the reliability of the "effective is also introduced, which is defined as the number of effective data contributing to the determination of a model parameter.
A NEW EFFECT OF RETAINED AUSTENITE ON DUCTILITY ENHANCEMENT OF LOW CARBON Q–P–T STEEL

A low carbon Fe–0.25C–1.48Mn–1.20Si–1.51Ni–0.05Nb (mass fraction, %) steel ex-hibits the combination of high tensile strength and good elongation after treated by a novel quenching– partitioning–tempering (Q–P–T) process. The variation in volume fraction of retained austenite in this steel with strain is measured by XRD, and the deformed twin–type martensite plates are also observed by TEM, from which the transformation induced plasticity (TRIP) eﬀect in this steel is conﬁrmed. Based on the measurement of average dislocation densities in both martensite and retained austenite combined with TEM observation, the eﬀect of dislocation absorption by retained austenite (DARA) is found in the low carbon steel, similar to that in the medium carbon steel proposed recently, from which the generation conditions of DARA eﬀect is proposed, and the mechanism of retained austenite on ductility enhancement of high strength steel is clariﬁed.
Learning and teaching the art of sound design: An analysis of best practices

The aim of this study was to investigate the learning and teaching of sound design in higher education contexts. I examined two main aspects: firstly, an investigation into published resources, addressing sound design as creative practice, and, secondly, an examination of the relationship between technical process and creative skills development within curriculum design. I also investigated the perceived educational benefits of collaborative projects and other practical exercises, contrasting them with technical, skills-based learning. Finally, the study suggests avenues for future development that may provide better access to reference materials that support sound design students’ development as creative, industry ready practitioners.
Effect of Body Condition Score on Estrus-Ovulation Synchronization and Pregnancy in Cows and Heifers

The most important factors affecting the success of artificial insemination are the balanced nutrition of the animals and the determination of estrus. Malnutrition causes a decrease in the Body Condition Score (BCS). Low BCS affects the deterioration of the metabolism of cows which has a primary influence on estrus and offspring productivity. In this study, estrus and ovulation synchronization was performed by forming three study groups from cows and heifers with a maximum BCS average of 2.95. Cows that were 3 - 6 years old were included in Group I (n = 12) (BCS: 2.95 ± 0.62), heifers between 18 -22 months of age and showing at least 1 estrus were included in Group II (n = 13) (BCS: 2.03 ± 0.37) and heifers between 18 -22 months of age were included in Group III (n = 45) (BCS: 1.57 ± 0.38). Progesterone Implants remained in the vagina for 10 days in all groups. One day before removal of implants, 2000 IU PMSG and 5 ml PGF2α was injected to all groups. All animals were injected with 1500 IU hCG 24 hours after the implants were removed. After removal of implants, all animals were inseminated at 48 and 72 hours regardless of estrus symptoms. According to the groups, pregnancy rates of 58.33%, 38.46% and 13.33% were obtained, respectively. When the results were compared, it was found that the BCS rate and pregnancy rates were parallel (P<0.01). In line with these results, the importance of feeding and giving birth before has been revealed.
Synthesis and spectroscopic characterization of Copper zinc aluminium nanoferrite particles

Copper doped zinc aluminium ferrites Cu x Zn 1-x. (Al x Fe 2-x )O 4 are synthesized by the solid-state reaction route and characterized by XRD, TEM, EPR and non linear optical spectroscopy techniques. The average particle size is found to be from 35 to 90 nm and the unit cell parameter “a” is calculated as from 8.39 to 8.89 Å . The cation distributions are estimated from X-ray diffraction intensities of various planes. The XRD studies have verified the quality of the synthesis of compounds and have shown the differences in the positions of the diffraction peaks due to the change in concentration of copper ions. TEM pictures clearly indicating that fundamental unit is composed of octahedral and tetrahedral blocks and joined strongly. The selected area electron diffraction (SAED) of the ferrite system shows best crystallinity is obtained when Cu content is very. Some of the d-plane spacings are exactly coinciding with XRD values. EPR spectra is compositional dependent at lower Al/Cu concentration EPR spectra is due to Fe 3+ and at a higher content of Al/Cu the EPR spectra is due to Cu 2+ . Absence of EPR spectra at room temperature indicates that the sample is perfectly ferromagnetic. EPR results at low temperature indicate that the sample is paramagnetic, open aperture z-scan technique indicate that these ferrites are potential candidates for optical limiting applications.
Assisting language learning with new technologies: A case of Spanish degrees facing European educational change process

One of the biggest challenges European countries have lately faced is The Bologna Process which consists of a series of ministerial agreements between European countries designed to ensure comparability in the standards and quality of higher education qualifications. The resulting European Higher Education Area (EHEA) propelled a sudden implementation of a new organizational approach into the Spanish academic system in 2008. This paper traces the evolution of lectures in English for Specific Purposes (ESP) over three semesters in this changing teaching environment in the Health Science and Medicine Faculties of the University of Zaragoza (Spain). The aim of this paper is to assess the value, among students, of the use of social media and virtual tools in a strictly academic context. The implementation of change in instructional design was necessary due to the limitations in the existing teaching practices. Consequently, the teacher’s and students’ experiences over each of the three semesters led to the implementation of Microsoft MSN Messenger, a course blog and a Facebook profile. Data from this contribution to action research suggest that students’ opinions and perceptions of the use of personally oriented technology in the language learning process were positive in my university academic context.
Phospholipid Fatty Acid (PLFA) Analysis: A Robust Indicator for Soil Health?

Phospholipid fatty acid, commonly referred to as PLFA, is an analytical concept that emerged in the late 1980’s for estimating composition of soil microbial communities [1]. While it is popular to describe the test as determining types of microbes in soils, in fact PLFA reveals the content of differing extractable phosphorylated-lipids recognized to be cell-wall constituents of microbes [2] extractable from soil with organic solvents. The advantage of PLFA is frequently characterized by contrasting it to the recognized difficulties that traditional microbiological cultural methods encounter in successful isolation and culturing of species present. PLFA is an indirect and multi-step process. After successful delineation of the range of lipids observed the analyst normally proceeds further to decide on assignments of groups of specific lipids as biomarkers or “signatures” for classes or genera of organisms. This selection process is not necessarily straight-forward and is based on accumulated evidence corroborating structural aspects of the relationships. The assignments generally include terminally branched saturated fatty acids to gram-positive bacteria, monounsaturated fatty acids to gram-negative bacteria, mid-branched saturated fatty acids for actinomycetes, and polyunsaturated fatty acids to fungi.
WAYS OF MOBILIZING FUNDS FROM THE POPULATION BY MUNICIPALITIES: FOREIGN AND RUSSIAN EXPERIENCE

It is generally recognized that local self-government is the most effective way to meet the basic socio-economic needs of the population. It is local self-government that takes on those public law functions that, by their very nature, cannot be realized by the forces of the state. The existing mechanisms of interaction between the state and local self-government are built on the basis of the principle of subsidiarity, aimed at supporting local budgets by the state, but they cannot always ensure sufficient and timely replenishment of local budgets. So, for example, we are left to conclude that local government is unable to influence such parameters as the collection and distribution of taxes. Although tax revenues are partially distributed in favor of local government, municipalities are often forced to look for sources of additional funding, which can be carried out at the expense of the population. specializes in constitutional and municipal law. of 5 reviewed Budget the Federation, Candidate of Legal of Economics. The author specializes in tax law and local government finance. the author of more than 6 books and 40 reviewed articles in journals. The article aims to analyze the existing mechanisms for mobilizing funds from the population by municipalities in the world in general and in Russia in particular.
VMI , a successful supply chain strategy ? Towards a VMI implementation at Kongsberg Automotive

Kongsberg Automotive (KA), a firstand second-tier supplier to some of the largest automotive manufactures, has acknowledged the need to enhance their competitiveness and to collaborate within the supply chain. In order to do this, KA must improve the current business processes by establishing long term relationships with their suppliers. Today, KA face a high level of inventory, which has resulted substantial amount of tied up capital and inefficient processes. Therefore, this study will evaluate KA’s and three chosen suppliers’ possibilities and the effects that a vendor managed inventory (VMI) partnership will imply. VMI is a concept within supply chain management, where the supplier is fully responsible for managing the customer’s inventory level. To achieve this, the supplier is given access to sensitive information of the customer’s inventory level and demand and can, thereby, replenish the customer’s stock when needed. Although, some firms have embraced the concept with success, others have retreated forcefully. To fulfil the purpose of this study, we have done a broad literature review regarding VMI and performance measurements, as well as, organisational structures and information sharing in collaborative partnerships. Further, to better understand the implementation and effects of VMI, a benchmarking study was made at Volvo Powertrain in Skövde, an early VMI pioneer. In addition to the thorough study of KA’s processes, field visits were made at the suppliers’ to visualize their material and information flow in order to examine in what areas VMI would have an impact. The conclusion of this study is that a VMI strategy, in supplement with a consignment stock policy, is possible for KA. However, issues concerning responsibilities and ownership have become apparent throughout the study that needs to be agreed upon. Further, the study indicates that the benefits from a VMI implementation will be greater for KA, than for the suppliers. Specifically, a reduction in inventory value can be attained by KA, while the suppliers only can obtain minor improvements within the areas of inventory, production, and order processing. On the other hand, the suppliers will face drawbacks, mainly in their order processing, which will result in increased costs. However, we advice KA to further discuss and test the VMI and consignment stock strategy in a pilot project in order to find the right solution for the company and the suppliers.