Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-camera vehicle tracking and re-identification based on visual and spatial-temporal features

Due to the heavy occlusions, large variations in different viewing perspectives and low video resolutions, the tracking and re-identification of vehicles under multi-camera become challenging tasks for the intelligent transportation system (ITS). In this work, we propose a novel framework for multi-camera tracking, which integrates visual features, orientation prediction and temporal-spatial information of the trajectories for optimization. In addition, based on the tracking information generated by our framework, we propose a united method for multi-camera re-identification that takes both visual features and tracking information into account. In order to make the visual feature robust for occlusion and perspective variation, our method adopts various features that are extracted from global image, regions and areas around key points, and the tracking information are also used to refine the retrieval results generated by the visual features. Our algorithm achieves the first place in vehicle re-identification at the NVIDIA AI City Challenge 2019.
Dense Crowd Counting Convolutional Neural Networks with Minimal Data using Semi-Supervised Dual-Goal Generative Adversarial Networks

In this work, we generalize semi-supervised generative adversarial networks (GANs) from classification problems to regression for use in dense crowd counting. In the last several years, the importance of improving the training of neural networks using semi-supervised training has been thoroughly demonstrated for classification problems. This work presents a dual-goal GAN which seeks both to provide the number of individuals in a densely crowded scene and distinguish between real and generated images. This method allows the dual-goal GAN to benefit from unlabeled data in the training process, improving the predictive capabilities of the discriminating network compared to the fully-supervised version of the network. Typical semi-supervised GANs are unable to function in the regression regime due to biases introduced when using a single prediction goal. Using the proposed approach, the amount of data which needs to be annotated for dense crowd counting can be significantly reduced.
Automatic Clustering of Conversational Patterns from Speech and Motion Data

Behavioral observation of teams is critical not only for team research, but also for leadership trainings. However, most current behavior observation methods used in team research often rely on manual video annotation which is time consuming and thus costly. In our work, we follow a sensor-based approach to automatically extract important pattern that capture conversational structure. Relying on automatic speaker detection from multiple microphones, we first employ a rule-based clustering to measure the proportions of typical conversational regimes taking place in a conversation and extend our method to learn more complex regimes from data using finite mixture models. Furthermore, we investigate the use of motion activity cues extracted from motion sensors. We apply our methods to a recent study on leadership in small groups and show how team meetings can be characterized.
Large-scale Machine Learning and Optimization Spring 2018 Lecture 13 — March 6

where l(w; zi) is the loss for data zi. The idea of stochastic gradient descent (SGD) is randomly sampling a data point and then doing a local optimization. More specifically, SGD updates the parameter estimation by wk+1 = wk − γ · ∇l(wk; zik), where zik is the randomly selected data point at step k. Although SGD achieves improvement in speeds comparing with gradient descents for some machine learning tasks, SGD still take 10+ days on large data sets (see, e.g., [2]). Therefore, it is significantly important to speed up SGD.
MULTIPLE SOLUTIONS FOR A PROBLEM: A TOOL FOR EVALUATION OF MATHEMATICAL THINKING IN GEOMETRY

Based on the presumption that solving mathematical problems in different ways may serve as a double role tool didactical and diagnostic, this paper describes a tool for the evaluation of the performance on multiple solution tasks (MST) in geometry. The tool is designed to enable the evaluation of subject's geometry knowledge and creativity as reflected from his solutions for a problem. The example provided for such evaluation is taken from an ongoing large-scale research aimed to examine the effectiveness of MSTs as a didactical tool. Geometry is a gold mine for MSTs and therefore an ideal focus for the present research, but the suggested tool could be used for different mathematical fields and different diagnostic purposes as well.
LG ] 29 F eb 2 01 6 Easy Monotonic Policy Iteration

A key problem in reinforcement learning for control with general function approximators (such as deep neural networks and other nonlinear functions) is that, for many algorithms employed in practice, updates to the policy or Q-function may fail to improve performance—or worse, actually cause the policy performance to degrade. Prior work has addressed this for policy iteration by deriving tight policy improvement bounds; by optimizing the lower bound on policy improvement, a better policy is guaranteed. However, existing approaches suffer from bounds that are hard to optimize in practice because they include sup norm terms which cannot be efficiently estimated or differentiated. In this work, we derive a better policy improvement bound where the sup norm of the policy divergence has been replaced with an average divergence; this leads to an algorithm, Easy Monotonic Policy Iteration, that generates sequences of policies with guaranteed non-decreasing returns and is easy to implement in a sample-based framework.
Parallelization of the Particle Competition and Cooperation approach for Semi-Supervised Learning

This work emphasis the development of fast ways to perform the particle competition and cooperation model, which is a nature inspired algorithm of semi-supervised learning. To do so, the algorithm was parallelized on multi-core CPU and GPU(Graphics Process Unit). A modified version of the model aiming to increase its velocity was proposed, developed and tested. This paper contains explanations of how the proposed algorithm works, how it was implemented for parallel processing, how the modified version works and experiments comparing the different implementations of the particle competition and cooperation algorithm on real world data sets, followed by analysis of the results. Keywords—Semi-supervised learning; particles competition and cooperation; network-based methods; label propagation; parallelism; GPU implementation;
MORPHOLOGICAL AND MOLECULAR CHARACTERIZATION OF Isostichopus sp. IN THE COLOMBIAN CARIBBEAN SEA CARACTERIZACIÓN MORFOLÓGICA Y MOLECULAR DE Isostichopus sp. EN EL MAR CARIBE COLOMBIANO

Isostichopus sp. (Aspidochirotida: Stichopodidae) are sea cucumbers widely distributed in the Caribbean Sea. Amongst them, Isostichopus badionotus is one of the most harvested species. It shows a wide range of morphotypes widespread in the Caribbean region including different habitats (muddy, sandy and rocky bottoms, and sea grass beds). In Colombia, three morphotypes can be distinguished; two of them live in sea grass beds while the third one is found on rocky substrates. The present study describes the morphological characteristics of these morphotypes and analyzes their genetic structures through 16S rDNA and COI data. Our phylogenetic analyses show that the morphotype living on rocky substrates is morphologically and genetically distinct from the two other morphotypes and might not correspond to I. badionotus, the only species of the Isostichopus genus previously reported for this region.
End-to-end Optimized Image Compression with Attention Mechanism

We present an end-to-end trainable image compression framework for low bit-rate and transparent image compression. Our method is based on variational autoencoder, which consists of a nonlinear encoder transformation, a soft quantizer, a nonlinear decoder transformation and an entropy estimation module. The prior probability of the latent representations is modeled by combining a hyper-prior autoencoder and a Pixelcnn++ based context module and they are trained jointly with the transformation autoen-coder with attention mechanism. In order to improve the compression performance, a non-local convolution based attention mechanism is designed for allocating bits adaptively. Finally, a novel rate allocation algorithm based on linear optimization is used to assign the bits for each image dynamically, considering the bits constraint of the challenge. Across the experimental results on validation and test sets, the optimized framework can generate the highest PSNR and MS-SSIM for low bit-rate compression competition, and cost the lowest bytes for transparent 40db competition.
Network Traffic Characteristics of the IoT Application Use Cases

The Internet of Things (IoT) is a highly discussed paradigm aimed at the connection of everyday objects to the Internet. The idea of enabling objects to communicate with each other brings significant benefits, yet there are several challenges that have to be also addressed. In recent years, researchers have developed many solutions to resolve IoT issues and enhance the overall evolution. New technologies have been proposed and existing approaches redefined to meet the specific requirements of IoT. However, the complexity and diversity of the area make the process quite difficult. This paper describes the IoT paradigm and provides an overview of efforts expended to date. It discusses topics such as IoT definitions, categorisation of IoT application areas and the greatest IoT challenges. One contribution of this paper is the detailed description of IoT network technologies and their illustration in a single protocol stack. Another contribution is the definition of network traffic characteristics of IoT application use cases, which aims to help in better comprehension of IoT demands on network technologies. This knowledge is crucial not only to propose more efficient solutions but also improve the network technologies specifically designed for IoT needs.
J3.15 RAINFALL ESTIMATION AND HYDROMETEOR TYPE CLASSIFICATION WITH A POLARIMETRIC WSR-88D RADAR

The accurate estimation of precipitation type and accumulation has been a long-standing problem for operational meteorologists and hydrologists. When the estimates are obtained by weather radar, inaccuracies can result from radar miscalibration, attenuation of the signal in heavy precipitation, and the presence of nonmeteorological scatterers such as ground returns, birds, and insects. Natural variations in the size, shape, and ice density of cloud and precipitation particles can also result in estimation uncertainties. Fortunately, many of these problems may be at least partially mitigated through the use of radar polarimetry. As part of the evolution and future enhancement of the WSR-88D, the National Severe Storms Laboratory (NSSL) recently upgraded the KOUN WSR-88D radar to include polarimetric capabilities. Since this radar is the first in a possible future national network of polarimetric WSR-88D radars, it represents a significant advance in our ability to improve rainfall estimation and hydrometeor type classification on a large scale. In this paper, we describe polarimetric rainfall estimation and hydrometeor type classification techniques, provide examples of polarimetric products, present initial impressions of the utility of the polarimetric upgrade to the WSR-88D radar, and discuss future plans for the operational demonstration of the KOUN radars capabilities.
EXPLORING RECONSTRUCTED 3 D MODELS IN A VIRTUAL REALITY ENVIRONMENT

This paper proposes a new way of exploring reconstructed models in 3D within a Virtual Reality environment. Different to traditional 2D desktop-based systems, 3D interaction allows for easy and intuitive navigation and selection/manipulation of these models. By using different Virtual Reality output devices, the user is able to explore immersively reconstructed 3D models in full size. The immersion is enhanced by using a stereoscopic setup. Interaction in such environments is eased by using real 3D input devices which can be used for either navigation or object manipulation.The models which can be explored in our environment are generated in a prior 3D reconstruction process where user interaction is kept at a minimum. The data exchange is seamlessly and uses VRML as data format. The main interaction is done in our application where the user can interact with different kinds of reconstructed objects. This paper presents the environment which can be used for such explorations, and necessary interaction aspects.
Synthesis and Activity of Novel Acaricidal / Insecticidal 2 , 4-Diphenyl-1 , 3-oxazolines

Junji SUZUKI, * Tatsuya ISHIDA, * Yasuo KIKUCHI, Yoshiaki ITO, Chiharu MORIKAWA, Yokichi TSUKIDATE, ** Isao TANJI, *** Yasuhiro OTA and Kazuya TODA Agro-science Research Institute, Yashima Chemical Industry Co., Ltd., Tomitake 173-2, Nagano 381-0006, Japan * Market Planning Department, Yashima Chemical Industry Co., Ltd., Futako 6-14-10, Takatsu-ku, Kawasaki 213-0002, Japan ** Environmental Research Laboratory, Eco-Science Corporation Co., Ltd., Tomitake 173-2, Nagano 381-0006, Japan *** Nagoya Branch, Yashima Chemical Industry Co., Ltd., Nishiki 2-IS-22, Naka-ku, Nagoya 460-0003, Japan
Modular Construction of Symmetrical Knots

The goal is to construct highly symmetrical models of mathematical knots from a single modular component that can be fabricated easily on inexpensive 3D printers or by injection molding. The “elbow” module, or Λ-module, is a piece of tubing that bends through a fixed given angle. To keep control over the torsional angle between subsequent modules, the tube cross section is not a circle but a regular polygon. We discuss possible optimization algorithms that allow the design of such knots with minimal amounts of deviation from the geometrical parameters imposed by the given modular component. 1. Mathematical Knots Mathematical knots are fascinating objects. They are closed loops that are defined only at the topological level – by the crossing of different branches of the overall loop in 3D-space (Fig.1a), or by the connectivity of the complement space. A particular knot can take on many different geometrical shapes, and there is still no computer program that can unambiguously prove that two different looking versions of the same mathematical knot are indeed the same.
Study of recognition algorithm for core node in kad network based on BP model

In view of the core node recognition in the KAD(Kademlia),a model based on BP is presented to determine whether a node is core node in real time.According to the result of the measurement for KAD,some attribute characteristics extraction and normalization processing is implemented to obtain an attribute set with higher separable degree.An algorithm in MatLab is designed to train the BP network until the results limit in a predetermined error range.In addition,the trained BP model is adapt to test prepared data,the results of the experiment show that the method can judge a node degrees of importance,and the recognition accuracy rate is up to about 70%.
INVESTIGATION OF FIBER REINFORCED COMPOSITE USING MULTI-MATERIAL 3 D PRINTING

Fiber reinforced composite materials have been commonly fabricated by laying and curing the stiff and high strength fibers within tough matrix to enhance the elastic modulus. The strength and elasticity of the fiber reinforced polymers are dependent on the intrinsic mechanical properties of matrix and fiber, the fiber layup pattern as well as the volume percentage of the matrix and fiber. In this paper, the effects of these factors on the 3D printed fiber reinforced composite materials were investigated. The fiber reinforced polymers were fabricated using multi-material inkjet printer with rubbery material as matrix and rigid strong polymer as the fiber reinforcement. Two types of fiber layup configurations and fiber/matrix volume ratios were designed for this study. The experimental result shows that both tensile strength and elastic modulus of the fiber reinforced polymers could be largely enhanced by varying the fiber/matrix ratio and layup pattern.
The adsorption of sulfur dioxide and ozone molecules on boron nitride nanotubes: A DFT study

Density functional theory calculations were carried out to investigate the adsorption behaviors and electronic structures of SO2 and O3 molecules on the pristine boron nitride nanotubes. The structural and electronic properties of the studied systems were investigated in view of the adsorption energies, band structures and molecular orbitals. Various adsorption positions of gas molecules on the boron nitride nanotubes were examined in detail. The band structure calculations indicate that the pristine BN nanotube works as a wide band gap semiconductor, and can be applied as an efficient candidate for SO2 and O3 sensing purposes. NBO analysis reveals that SO2 acts as a charge donor, whereas O3 molecule behaves as a charge acceptor from the BN nanotube. Molecular orbital calculations indicate that the LUMOs were dominant on the nanotube surface, whereas the electronic densities in the HOMOs were mainly distributed over the adsorbed SO2 and O3 molecules. Moreover, the charge density difference calculations indicate charge accumulation on the adsorbed gas molecule.
Simulating the Coupling between metal lines in milimeter wave frequencies

A study of the impact of metal ground shields on the coupling between two metal lines is presented. The structure is designed over a bulk silicon substrate with epitaxial layer, so the coupling through the substrate is also involved. A lightly doped profile is adopted and is simulated by means of an electromagnetic simulator for various line distances and different metal layers, assuming a 65 nm bulk CMOS technology. The impact of various metal layer ground shields placed below the line layer is examined . Furthermore, the increase of isolation (resulting in reduction of the noise coupling) between the lines by dividing the ground shield into smaller area ground shields, is also analyzed. Finally the impact of the distance’s width between the divided ground shields is examined. S parameters are used to compare the various structures.
Real Estate vs Stock Market : approaching the required rate of return through the Treynor and Black model

Real estate should be part of any well diversified portfolio. Its inclusion in an efficient portfolio is justified by its expected return and its risk features, among which it becomes remarkable their low correlation with financial assets, especially with common stock. Nevertheless, its low liquidity when compared with financial assets, its maintenance and transaction costs require that real estate enables investors to expect an alpha that absorbs these specific costs. On this basis we apply the Treynor and Black model to study their proportion in a portfolio constituted by real estate and common stocks. This model analyses the optimal combination between undervalued assets and the market index. The proportion of real estate in the new portfolio can be studied as the outcome of the combination between its own features and the properties of the market index. We perform a sensitivity analysis between the values of real estate weight and its alpha, which, after realizing that the specific risk of real estate is among the independent variables, gives way to a new relationship between the real estate proportion and its appraisal ratio. Next, we study the value created by the resulting new portfolio between real estate and the market index. The Sharpe ratio on the new portfolio is a function of the appraisal ratio of real estate, and, on this basis, we analytically relate the value created by the new portfolio to the appraisal ratio and the Sharpe ratio of the market index. Finally, we focus on the negative effect of the lack of divisibility of real estate on value creation, its consequences for bubbles and crisis, and on securitization as a way to overcome it.
Georeferencing Eye Tracking Data on Interactive Cartographic Products

A user study was conducted in which participants had to execute four tasks in GoogleMaps (only by means of a panning operation). Two of these tasks consisted of following a route in Belgium; in the two other tasks the participants had to locate Belgium on a less detailed map (scale level 7). Furthermore, an alternation between the map and satellite view was implemented. The recorded mouse actions revealed strong task-related interactive behaviour. The recorded eye movements also indicated differences in the participants’ attentive behaviour on the two scale levels, which are associated with different tasks. Furthermore, the attentive behaviour is also influence by the type of view (map or satellite) and whether it occurs during a panning operation. Moreover, the georeferenced eye movements were imported in a GIS for spatial analyses such as buffers. Based on these spatial queries, we discovered that the duration of the fixations is also associated by its’ location on the map.
Name Disambiguation Using Many-to-One features

As the Web increase drastically, more and more entity information come to appear on Web, including their profile information, their web log containing their idea, activity, speech and so on. However, there are many entities sharing same names. Such entities include persons, locations and so on. This paper presents an approach to estimate the number of entities sharing same name by employing many-to-one features. The basic idea is that the entities are not likely to share all other features even if they have the same name. We list some strategies for selecting key features, present an approach to extract the features on Web, and combine them to estimate the entity number. What is more, we also give a method to identify the fake information which will confuse us and filter them. The referents of a same name appearing on Web are difficult to distinguish due to lack of features which can be used to identify them. Originally, the name is a key feature used to identify one entity from the others. should be a many-to-one relation to prevent misunderstanding. However, while more and more entities come to appear on Web, such relation has been broken out and it becomes to be many-to-many relation. One name may potentially refer to tens or hundreds of entities. One can't just use name to identify an entity on Web because of homonym exists so commonly. Our purpose is to estimate lower bound of referents' number of a name in name list, so we ignore name recognition in general process of name disambiguation. We present a method based on choosing some special features which have many-to-one relations, including profiles of the entities and relations between entities. As to a person name, the person's birthday and his parent's name may be important features. Use these features, combined with the person's name, we are able to distinguish this person from other homonyms. How to choose features and extract features should be considered first. The data on web is always redundant and full of feature patterns. We use iterative pattern relation extraction to make our method scalable. Many web pages just contain entity name and don't contain the features we want. And we will also use query expansion to avoid data sparse.