Miracle-dz/UniFAR-FTUs huggingface dataset:
A Direct 3D Object Tracking Method Based on Dynamic Textured Model Rendering and Extended Dense Feature Fields

We propose a novel method for robust 6-DOF pose tracking of rigid objects from monocular images. In our method, 3D object tracking is achieved by directly aligning video frames to dynamic templates rendered from a textured 3D object model. Unlike previous methods, which usually utilize a small number of discrete templates to align with video frames, we employ an online textured model, rendering to create dynamic templates in continuous pose space according to the previously estimated object pose. In this way, a pose estimator could be easily converged to the optimal state. Besides, the rendered template also helps to detect the occlusion area by comparing it with the current frame, making our method highly robust to partial occlusions. The performance of our method is further improved by introducing a generic representation of dense images features, which we call extended dense feature fields (EDFF). Different kinds of pixel-level image features can be added to the EDFF and be optimized simultaneously in a unified Gauss-Newton optimization scheme. Attributing to dynamic templates from the textured model rendering and complementary features in EDFF, our method is able to deal with poor-textured and specular objects, as well as lighting variation and heavy occlusions. While our method is quite simple and straightforward, it achieves competitive or even superior results compared with the state of the art on challenging data sets.
Effective Attention Mechanism in Dynamic Models for Speech Emotion Recognition

We propose to integrate the attention mechanism into deep recurrent neural network models for speech emotion recognition. This is based on the intuition that it is beneficial to emphasize the expressive part of the speech signal for emotion recognition. By introducing attention mechanism, we make the system learn how to focus on the more robust or informative segments in the input signal. The proposed recognition model is evaluated on the FAU-Aibo tasks as defined in Interspeech 2009 Emotion Challenge. Our baseline deep recurrent neural network model achieves 37.0% unweighted averaged (UA) recall rate, which is on par with the official HMM baseline system for dynamic modeling framework. The proposed integration of attention mechanism on top of the baseline deep RNN model achieves 46.3% UA recall rate. As far as we know, this is the best UA recall rate ever achieved on FAU-Aibo tasks within the dynamic modeling framework.
On the Geometry of Mixtures of Prescribed Distributions

We consider the space of w-mixtures that are finite statistical mixtures sharing the same prescribed component distributions, like Gaussian mixture models sharing the same components. The information geometry induced by the Kullback-Leibler (KL) divergence yields a dually flat space where the KL divergence between two w-mixtures amounts to a Bregman divergence for the negative Shannon entropy generator, called the Shannon information. Furthermore, we prove that the skew Jensen-Shannon statistical divergence between w-mixtures amount to skew Jensen divergences on their parameters and state several divergence inequalities between w-mixtures and their closures.
A Novel Approach to Under-Actuated Control of Fluidic Systems

Thanks to the growing interest in soft robotics, hydropneumatics and inflatable system dynamics are attracting renewed attention from the scientific community. Typical fluidic systems are composed of several chambers and require a complex and bulky network of active components for their control. This paper presents a novel approach to fluidic actuation, which consists in the co-design of both the mechanical parameters of the system and of custom input signals, to enable the elicitation of different behaviors of the system with fewer control components. The principle is presented in theory and simulation and then experimentally validated through the application to a case study, an in-pipe inchworm-like robot. It is shown that it is possible to obtain forward and backward movements by modulating a unique input.
Usability of the Size, Spacing, and Depth of Virtual Buttons on Head-Mounted Displays

Virtual reality (VR) allows users to see and manipulate virtual scenes and items through input devices, like head-mounted displays. In this study, the effects of button size, spacing, and depth on the usability of virtual buttons in VR environments were investigated. Task completion time, number of errors, and subjective preferences were collected to test different levels of the button size, spacing, and depth. The experiment was conducted in a desktop setting with Oculus Rift and Leap motion. A total of 18 subjects performed a button selection task. The optimal levels of button size and spacing within the experimental conditions are 25 mm and between 5 mm and 9 mm, respectively. Button sizes of 15 mm with 1-mm spacing were too small to be used in VR environments. A trend of decreasing task completion time and the number of errors was observed as button size and spacing increased. However, large size and spacing may cause fatigue, due to continuous extension of the arms. For depth effects, the touch method took a shorter task completion time. However, the push method recorded a smaller number of errors, owing to the visual push-feedback. In this paper, we discuss advantages and disadvantages in detail. The results can be applied to many different application areas with VR HMD.
Singular fibres of the Gelfand–Cetlin system on MANI(n)*

In this paper, we show that every singular fibre of the Gelfand–Cetlin system on co-adjoint orbits of unitary groups is a smooth isotropic submanifold which is diffeomorphic to a two-stage quotient of a compact Lie group by free actions of two other compact Lie groups. In many cases, these singular fibres can be shown to be homogeneous spaces or even diffeomorphic to compact Lie groups. We also give a combinatorial formula for computing the dimensions of all singular fibres, and give a detailed description of these singular fibres in many cases, including the so-called (multi-)diamond singularities. These (multi-)diamond singular fibres are degenerate for the Gelfand–Cetlin system, but they are Lagrangian submanifolds diffeomorphic to direct products of special unitary groups and tori. Our methods of study are based on different ideas involving complex ellipsoids, Lie groupoids and also general ideas coming from the theory of singularities of integrable Hamiltonian systems. This article is part of the theme issue ‘Finite dimensional integrable systems: new trends and methods’.
Sentiment-Based Model for Recommender Systems

Recommender systems have proven to be a valuable way for online users to cope with the issues of information overload. They have become one of the most powerful and popular tools in electronic commerce as illustrated by Amazon.com, YouTube, Netflix, Yahoo, and IMDb. While recommender systems have shown significant contribution, they still suffer from the long-standing problems related to cold-start users and data-sparsity. This is due to the fact that recommendation algorithms mostly rely on users' rating to make prediction of items. Such ratings are usually insufficient and very limited. On the other hand, sentiment ratings of items which can be derived from online news services, blogs, social media or even from the recommender systems themselves are seen capable of providing better recommendations to user as opposed to tags alone. Sentiment-based model has been exploited in recommender systems to overcome the data-sparsity problem that exists in conventional recommender systems. Hence, embedding sentiment in recommender systems may significantly enhance the recommendation quality of recommender systems. Among the aims of this research is to integrate sentiment analysis in recommender systems particular to those items with no associated rating that commonly contribute to the problem of data-sparsity.
Face Hallucination Based on Key Parts Enhancement

Face hallucination aims to generate a high resolution face from a low resolution one. Generic super resolution methods can not solve this problem well, because human face has a strong structure. With the rapid development of the deep learning technique, some convolutional neural networks (CNNs) models for face hallucination emerged and achieved state-of-the-art performance. In this paper, we proposed a five-branch network based on five key parts of human face. Each branch of this network aims to generate a high resolution key part. The final high resolution face is the combination of the five branches' output. In addition, we designed a gated enhance unit (GEU) and cascade it to form our network architecture. Experimental results confirm that our method can generate pleasing high resolution faces.
Investigating the effects of losses of a piezoelectric transducer in temperature varying environment through Finite Element Analysis

Piezoelectric ultrasonic transducers are used in many situations, including system and structural health monitoring and for non-destructive testing. For this, a piezoelectric resonator is used to actuate a structure and produce the sound waves that interact with the system under investigation and are then analysed. The behavior of such transducers is dependent on their environment, e.g., on the temperature. The mechanical parameters of the metals can be significantly changed if the temperature modification is large. Furthermore, the piezoelectric materials have their coefficients and losses modified by temperature changes. While the temperature behavior of the piezoelectric and metal can be independently determined experimentally, the role of the temperature change on the transducer performance and generated sound level is not well understood. In this paper, a simple transducer intended for high-temperature applications is investigated using Finite Element Analysis. A piezoelectric disc resonator is glued under a fixed stainless steel membrane, with parameters for the glue considered as those of gold. Effects of losses on the fundamental resonance of the admittance of the transducer and on the sound level it produces are investigated between 20°C and 300°C. Increasing the temperature reduces the resonance frequency of the transducer while maintaining the sound level at resonance. An error of one order of magnitude in input elastic losses of the piezoelectric material will have a stronger effect on the transducer admittance at every frequency than a similar error in the input dielectric and piezoelectric losses.
QoE-Driven and Traffic-Flow-Density-Based Link Scheduling Algorithm in LLC Protocol for HetVNETs

In order to satisfy the requirements of the variety of network applications and the convergence between network physical media in heterogeneous network, a new link scheduling algorithm based on priority of data, link status and traffic flow density(Priority, Delay and Load Link Scheduling Algorithm, PTLCSA) is proposed by improving traditional data-link layer protocol in Vehicular Ad Hoc Network(VANET). At first, based on the four types of vehicle data in vehicle networking standard, data priority is proposed to indicate the sending weight of different data. Meanwhile, delay factor and load factor are proposed to evaluate link status in real time. Then, based on data priority, delay factor, load factor and traffic flow density, data priority matching degree (DPMD) of link is proposed, and according to Pareto optimal, the upper bound of non-WSM (non-WAVE Short Message, non-safety messages) maximal distribution frequency about IEEE 802.11p link is presented, which provide theoretical foundation for data frames to select links. And then, based on DPMD and the upper bound of non-WSM maximal distribution frequency, PTLCSA is designed and realized, and the time complexity of PTLCSA is analyzed in this paper. Finally, simulation results show that on the premise that satisfying the Quality of Experience(QoE) of most network applications, PTLCSA can reduce the delay of security applications and improve the utilization ratio of links.
Modeling Driver Behavior from Demonstrations in Dynamic Environments Using Spatiotemporal Lattices

One of the most challenging tasks in the development of path planners for intelligent vehicles is the design of the cost function that models the desired behavior of the vehicle. While this task has been traditionally accomplished by hand-tuning the model parameters, recent approaches propose to learn the model automatically from demonstrated driving data using Inverse Reinforcement Learning (IRL). To determine if the model has correctly captured the demonstrated behavior, most IRL methods require obtaining a policy by solving the forward control problem repetitively. Calculating the full policy is a costly task in continuous or large domains and thus often approximated by finding a single trajectory using traditional path-planning techniques. In this work, we propose to find such a trajectory using a conformal spatiotemporal state lattice, which offers two main advantages. First, by conforming the lattice to the environment, the search is focused only on feasible motions for the robot, saving computational power. And second, by considering time as part of the state, the trajectory is optimized with respect to the motion of the dynamic obstacles in the scene. As a consequence, the resulting trajectory can be used for the model assessment. We show how the proposed IRL framework can successfully handle highly dynamic environments by modeling the highway tactical driving task from demonstrated driving data gathered with an instrumented vehicle.
Assimilation of formic acid and CO2 by engineered Escherichia coli equipped with reconstructed one-carbon assimilation pathways

Significance While biological utilization of one-carbon (C1) compounds has attracted much attention, previous studies have focused mainly on the utilization of CO2. Here, we report development of Escherichia coli strains capable of assimilating formic acid (FA) and CO2 through the C1 assimilation pathway, synthesizing pyruvate from FA and CO2 by establishing the reconstructed tetrahydrofolate cycle and the reverse glycine cleavage pathway. To generate energy and redox while using less glucose, a heterologous formate dehydrogenase was introduced together with the C1 assimilation pathway. The resulting strain could utilize FA and CO2 as sole carbon sources for sustaining growth. This work demonstrates that the combined use of the C1 assimilation pathway and formate dehydrogenase allows E. coli to utilize FA and CO2 efficiently. Gaseous one-carbon (C1) compounds or formic acid (FA) converted from CO2 can be an attractive raw material for bio-based chemicals. Here, we report the development of Escherichia coli strains assimilating FA and CO2 through the reconstructed tetrahydrofolate (THF) cycle and reverse glycine cleavage (gcv) pathway. The Methylobacterium extorquens formate-THF ligase, methenyl-THF cyclohydrolase, and methylene-THF dehydrogenase genes were expressed to allow FA assimilation. The gcv reaction was reversed by knocking out the repressor gene (gcvR) and overexpressing the gcvTHP genes. This engineered strain synthesized 96% and 86% of proteinogenic glycine and serine, respectively, from FA and CO2 in a glucose-containing medium. Native serine deaminase converted serine to pyruvate, showing 4.5% of pyruvate-forming flux comes from FA and CO2. The pyruvate-forming flux from FA and CO2 could be increased to 14.9% by knocking out gcvR, pflB, and serA, chromosomally expressing gcvTHP under trc, and overexpressing the reconstructed THF cycle, gcvTHP, and lpd genes in one vector. To reduce glucose usage required for energy and redox generation, the Candida boidinii formate dehydrogenase (Fdh) gene was expressed. The resulting strain showed specific glucose, FA, and CO2 consumption rates of 370.2, 145.6, and 14.9 mg⋅g dry cell weight (DCW)−1⋅h−1, respectively. The C1 assimilation pathway consumed 21.3 wt% of FA. Furthermore, cells sustained slight growth using only FA and CO2 after glucose depletion, suggesting that combined use of the C1 assimilation pathway and C. boidinii Fdh will be useful for eventually developing a strain capable of utilizing FA and CO2 without an additional carbon source such as glucose.
In Defense of the Classification Loss for Person Re-Identification

The recent research for person re-identification has been focused on two trends. One is learning the part-based local features to form more informative feature descriptors. The other is designing effective metric learning loss functions such as the triplet loss family. We argue that learning global features with classification loss could achieve the same goal, even with some simple and cost-effective architecture design. In this paper, we first explain why the person re-id framework with standard classification loss usually has inferior performance compared to metric learning. Based on that, we further propose a person re-id framework featured by channel grouping and multi-branch strategy, which divides global features into multiple channel groups and learns the discriminative channel group features by multi-branch classification layers. The extensive experiments show that our framework outperforms prior state-of-the-arts in terms of both accuracy and inference speed.
Curcumin analogs: Their roles in pancreatic cancer growth and metastasis

Curcumin is a polyphenolic constituent of turmeric that is known to have various molecular effects in preclinical models, leading to prevention and anticancer properties. In clinical trials, curcumin has failed to demonstrate activity against pancreatic cancer possibly due to its low bioavailability and potency. Using the curcumin molecular model, our group and others have synthesized several analogs with better bioavailability and higher potency in pancreatic cancer in vitro and xenograft models. This mini review summarizes some of the known molecular effects of curcumin analogs and their potential role as novel therapeutics for pancreatic cancer.
Clustering and Analysis of Household Power Load Based on HMM and Multi-factors

With the advance of the information and communication technology, smart grid, and smart metering, residential electricity usage data are available for analyzing household usage pattern. However, most such usage pattern analyses have been based on smart meter data. Geographical location and environment factors have not been well considered. In order to have a better understanding of residential electricity usage pattern, this paper studies the usage pattern based on both environment data and smart meter data. A Hidden Markov Model (HMM) is applied to learn the consumption dynamic behavior under the corresponding environments and a clustering method is applied to discover the typical usage patterns. The environmental adaptation which indicates the household reaction to the environment during the electricity consumption is revealed.
Somatotopic organization of central arbors from nociceptive afferents develops independently of their intact peripheral target innervation

Functionally important regions of sensory maps are overrepresented in the sensory pathways and cortex, but the underlying developmental mechanisms are not clear. In the spinal cord dorsal horn (DH), we recently showed that paw innervating Mrgprd+ nonpeptidergic nociceptors display distinctive central arbor morphologies that well correlate with increased synapse transmission efficiency and heightened sensitivity of distal limb skin. Given that peripheral and central arbor formation of Mrgprd+ neurons co‐occurs around the time of birth, we tested whether peripheral cues from different skin areas and/or postnatal reorganization mechanisms could instruct this somatotopic difference among central arbors. We found that, while terminal outgrowth/refinement occurs during early postnatal development in both the skin and the DH, postnatal refinement of central terminals precedes that of peripheral terminals. Furthermore, we used single‐cell ablation of Ret to genetically disrupt epidermal innervation of Mrgprd+ neurons and revealed that the somatotopic difference among their central arbors was unaffected by this manipulation. Finally, we saw that region‐specific Mrgprd+ central terminal arbors are present from the earliest postnatal stages, before skin terminals are evident. In summary, we find that region‐specific organization of Mrgprd+ neuron central arbors is present shortly after initial central terminal formation, which likely develops independently of peripheral target innervation. Our data suggest that either cell‐intrinsic and/or DH prepatterning mechanisms are likely to establish this somatotopic difference.
Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing

Wireless distributed computing presents new opportunities to execute intelligent tasks on mobile devices for low-latency applications, by wirelessly aggregating the computation and storage resources among mobile devices. However, for low-latency applications, the key bottleneck lies in the exchange of intermediate results among mobile devices for data shuffling. To improve communication efficiency therein, we establish a novel interference alignment condition by exploiting the locally computed intermediate values as side information. The low-rank optimization model is further developed to maximize the achieved degrees-of-freedom (DoFs). Unfortunately, existing convex relaxation based approach fails to yield satisfied performance due to the poor structure in the formulated low-rank optimization problem, for which we develop a novel difference-of-convex (DC) programming based algorithm. We show that this new approach can significantly improve communication efficiency and the achievable DoF is independent of the number of mobile devices.
Effects of light field subsampling on the quality of experience in refocusing applications

Light field acquisition devices can capture static and dynamic information of the light field in space that is processed using computational imaging techniques for creating visual representation of the captured scene. The main applications are the creation of visual effects such as perspective change, refocusing, recoloring. Advances in light field technologies will provide the devices and tools needed for the development of new services in the virtual/augmented/merged reality domains. This paper presents an analysis of the effects of light field subsampling on the quality of experience in refocusing applications.
Compositional computational constructive critique: or, how my computer learned to appreciate poetry

A well-typed work of art should not sound wrong: we can use DSLs to get this boon. Existing work has cover’d this for song, at least so far as harmony and tune. Well, verse contrains the author’s pencil thus: In English verse, the syllables adhere to certain contours. Catenation plus the empty form create a monoid here. But not all poems stay within their meter; some, like this, can be a little free and so our monoid has to let us teeter on the edge, as tunes oft do with key. Our monoid must be fuzzy at its heart to let us teach computers of this art.