212 research outputs found
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
Learning-based approaches to robotic manipulation are limited by the
scalability of data collection and accessibility of labels. In this paper, we
present a multi-task domain adaptation framework for instance grasping in
cluttered scenes by utilizing simulated robot experiments. Our neural network
takes monocular RGB images and the instance segmentation mask of a specified
target object as inputs, and predicts the probability of successfully grasping
the specified object for each candidate motor command. The proposed transfer
learning framework trains a model for instance grasping in simulation and uses
a domain-adversarial loss to transfer the trained model to real robots using
indiscriminate grasping data, which is available both in simulation and the
real world. We evaluate our model in real-world robot experiments, comparing it
with alternative model architectures as well as an indiscriminate grasping
baseline.Comment: ICRA 201
Iterative algorithm for lane reservation problem on transportation network
International audienceIn this paper, we study an NP-hard lane reservation problem on transportation network. By selecting lanes to be reserved on the existing transportation network under some special situations, the transportation tasks can be accomplished on the reserved lanes with satisfying the condition of time or safety. Lane reservation strategy is a flexible and economic method for traffic management. However, reserving lanes has impact on the normal traffic because the reserved lanes can only be passed by the special tasks. It should be well considered choosing reserved lanes to minimize the total traffic impact when applying the lane reservation strategy for the transportation tasks. In this paper, an integer linear program model is formulated for the considered problem and an optimal algorithm based on the cut-and-solve method is proposed. Some new techniques are developed for the cut-and-solve method to accelerate the convergence of the proposed algorithm. Numerical computation results of 125 randomly generated instances show that the proposed algorithm is much faster than a MIP solver of commercial software CPLEX 12.1 to find optimal solutions on average computing time
Reverberation Time Control by Acoustic Metamaterials in a Small Room
In recent years, metamaterials have gained considerable attention as a
promising material technology due to their unique properties and customizable
design, distinguishing them from traditional materials. This article delves
into the value of acoustic metamaterials in room acoustics, particularly in
small room acoustics that poses specific challenges due to their significant
cavity resonant nature. Small rooms usually exhibit an inhomogeneous frequency
response spectrum, requiring higher wall absorption with specific spectrum to
achieve a uniform acoustic environment, i.e., a constant reverberation time
over a wide audible frequency band. To tackle this issue, we developed a design
that simultaneously incorporates numerous subwavelength acoustic resonators at
different frequencies to achieve customized broadband absorption for the walls
of a specific example room. The on-site experimental measurements agree well
with the numerical predictions, attesting to the robustness of the design and
method. The proposed method of reverse-engineering metamaterials by targeting
specific acoustic requirements has broad applicability and unique advantages in
small confined spaces with high acoustic requirements, such as recording
studios, listening rooms, and car cabins.Comment: 15 pages, 6 figure
STTAR: A Traffic- and Thermal-Aware Adaptive Routing for 3D Network-on-Chip Systems
Since the three-dimensional Network on Chip (3D NoC) uses through-silicon via technology to connect the chips, each silicon layer is conducted through heterogeneous thermal, and 3D NoC system suffers from thermal problems. To alleviate the seriousness of the thermal problem, the distribution of data packets usually relies on traffic information or historical temperature information. However, thermal problems in 3D NoC cannot be solved only based on traffic or temperature information. Therefore, we propose a Score-Based Traffic- and Thermal-Aware Adaptive Routing (STTAR) that applies traffic load and temperature information to routing. First, the STTAR dynamically adjusts the input and output buffer lengths of each router with traffic load information to limit routing resources in overheated areas and control the rate of temperature rise. Second, STTAR adopts a scoring strategy based on temperature and the number of free slots in the buffer to avoid data packets being transmitted to high-temperature areas and congested areas and to improve the rationality of selecting routing output nodes. In our experiments, the proposed scoring Score-Based Traffic- and Thermal-Aware Adaptive Routing (STTAR) scheme can increase the throughput by about 14.98% to 47.90% and reduce the delay by about 10.80% to 35.36% compared with the previous works
Ranking Influential Nodes of Fake News Spreading on Mobile Social Networks
Online fake news can generate a negative impact on both users and society. Due to the concerns with spread of fake news and misinformation, assessing the network influence of online users has become an important issue. This study quantifies the influence of nodes by proposing an algorithm based on information entropy theory. Dynamic process of influence of nodes is characterized on mobile social networks (MSNs). Weibo (i.e., the Chinese version of microblogging) users are chosen to build the real network and quantified influence of them is analyzed according to the model proposed in this paper. MATLAB is employed to simulate and validate the model. Results show the comprehensive influence of nodes increases with the rise of two factors: the number of nodes connected to them and the frequency of their interaction. Indirect influence of nodes becomes stronger than direct influence when the network scope rises. This study can help relevant organizations effectively oversee the spread of online fake news on MSNs
Fundamental CRB-Rate Tradeoff in Multi-Antenna ISAC Systems with Information Multicasting and Multi-Target Sensing
This paper investigates the performance tradeoff for a multi-antenna
integrated sensing and communication (ISAC) system with simultaneous
information multicasting and multi-target sensing, in which a multi-antenna
base station (BS) sends the common information messages to a set of
single-antenna communication users (CUs) and estimates the parameters of
multiple sensing targets based on the echo signals concurrently. We consider
two target sensing scenarios without and with prior target knowledge at the BS,
in which the BS is interested in estimating the complete multi-target response
matrix and the target reflection coefficients/angles, respectively. First, we
consider the capacity-achieving transmission and characterize the fundamental
tradeoff between the achievable rate and the multi-target estimation
Cram\'er-Rao bound (CRB) accordingly.Comment: 32 page
DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph
In the era of information explosion, it is difficult for people to obtain their desired information effectively. In tourism, a travel recommender system based on big travel data has been developing rapidly over the last decade. However, most work focuses on click logs, visit history, or ratings, and dynamic prediction is absent. As a result, there are significant gaps in both dataset and recommender models. To address these gaps, in the first step of this study, we constructed two human-annotated datasets for the travel conversational recommender system. We provided two linked data sets, namely, interaction sequence and dialogue data sets. The usage of the former data set was done to fully explore the static preference characteristics of users based on it, while the latter identified the dynamics changes in user preference from it. Then, we proposed and evaluated BERT-based baseline models for the travel conversational recommender system and compared them with several representative non-conversational and conversational recommender system models. Extensive experiments demonstrated the effectiveness and robustness of our approach regarding conversational recommendation tasks. Our work can extend the scope of the travel conversational recommender system and our annotated data can also facilitate related research
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