scholarly journals High-precision spatial analysis of mouse courtship vocalization behavior reveals sex and strain differences

2021 ◽  
Author(s):  
Gabriel Oliveira-Stahl ◽  
Soha Farboud ◽  
Max L. Sterling ◽  
Jesse J. Heckman ◽  
Bram van Raalte ◽  
...  
Keyword(s):  
Sound Localization ◽  
State Of The Art ◽  
Strain Differences ◽  
Spatial Localization ◽  
Spatial Relations ◽  
Ultrasonic Vocalizations ◽  
Spatial Accuracy ◽  
High Complexity ◽  
Frequency Level ◽  
Novel Algorithm

Mice display a wide repertoire of vocalizations that varies with sex, strain, and context. Especially during social interaction, mice emit sequences of ultrasonic vocalizations (USVs) of high complexity. As animals of both sexes vocalize, a reliable attribution of USVs to their emitter is essential. The state-of-the-art in sound localization for USVs in 2D allows spatial localization at a resolution of multiple centimeters. However, animals interact at closer ranges, e.g. snout-to-snout. Hence, improved algorithms are required to reliably assign USVs. We present a novel algorithm, SLIM (Sound Localization via Intersecting Manifolds), that achieves a 3-fold improvement in accuracy (12-14.3mm) using only 4 microphones and extends to many microphones and localization in 3D. This accuracy allows reliable assignment of 84.3% of all USVs in our dataset. We apply SLIM to courtship interactions between adult C57Bl/6J wildtype mice and those carrying a heterozygous Foxp2 variant (R552H). The improved spatial accuracy reveals detailed vocalization preferences for specific spatial relations between the mice. Specifically, vocalization probability, duration, Wiener entropy, and frequency level differed in particular spatial relations between WT females, Foxp2-R552H and WT males. In conclusion, the improved attribution of vocalizations to their emitters provides a foundation for better understanding social vocal behaviors.

scholarly journals Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

2019 ◽  
Vol 11 (16) ◽  
pp. 1933 ◽  
Author(s):  
Yangyang Li ◽  
Ruoting Xing ◽  
Licheng Jiao ◽  
Yanqiao Chen ◽  
Yingte Chai ◽  
...  
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Sample Selection ◽  
Spatial Relations ◽  
Speckle Noise ◽  
Classification Performance ◽  
Selection Strategy ◽  
Training Set ◽  
Polarimetric Synthetic Aperture Radar ◽  
Unlabeled Sample

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

scholarly journals Relocation of macrophages maintains the barrier function of the urothelium and protects against persistent infection

2019 ◽  
Author(s):  
Jenny Bottek ◽  
Camille Soun ◽  
Julia K Volke ◽  
Akanksha Dixit ◽  
Stephanie Thiebes ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Urinary Tract Infection ◽  
Connective Tissue ◽  
Barrier Function ◽  
Bacterial Infections ◽  
Mass Spectrometry Imaging ◽  
Recurrent Urinary Tract Infection ◽  
State Of The Art ◽  
Urothelial Cells ◽  
Novel Algorithm

SUMMARYMacrophages perform essential functions during bacterial infections, such as phagocytosis of pathogens and elimination of neutrophils to reduce spreading of infection, inflammation and tissue damage. The spatial distribution of macrophages is critical to respond to tissue specific adaptations upon infections. Using a novel algorithm for correlative mass spectrometry imaging and state-of-the-art multiplex microscopy, we report here that macrophages within the urinary bladder are positioned in the connective tissue underneath the urothelium. Invading uropathogenic E.coli induced an IL-6–dependent CX3CL1 expression by urothelial cells, facilitating relocation of macrophages from the connective tissue into the urothelium. These cells phagocytosed UPECs and eliminated neutrophils to maintain barrier function of the urothelium, preventing persistent and recurrent urinary tract infection. GRAPHICAL ABSTRACT

scholarly journals Improved Algorithms for Conservative Exploration in Bandits

2020 ◽  
Vol 34 (04) ◽  
pp. 3962-3969
Author(s):  
Evrard Garcelon ◽  
Mohammad Ghavamzadeh ◽  
Alessandro Lazaric ◽  
Matteo Pirotta
Keyword(s):  
State Of The Art ◽  
Digital Marketing ◽  
Learning Problem ◽  
Online Learning Algorithms ◽  
Empirical Performance ◽  
Regret Bounds ◽  
Healthcare Finance ◽  
And Robotics ◽  
Novel Algorithm ◽  
Real World Problems

In many fields such as digital marketing, healthcare, finance, and robotics, it is common to have a well-tested and reliable baseline policy running in production (e.g., a recommender system). Nonetheless, the baseline policy is often suboptimal. In this case, it is desirable to deploy online learning algorithms (e.g., a multi-armed bandit algorithm) that interact with the system to learn a better/optimal policy under the constraint that during the learning process the performance is almost never worse than the performance of the baseline itself. In this paper, we study the conservative learning problem in the contextual linear bandit setting and introduce a novel algorithm, the Conservative Constrained LinUCB (CLUCB2). We derive regret bounds for CLUCB2 that match existing results and empirically show that it outperforms state-of-the-art conservative bandit algorithms in a number of synthetic and real-world problems. Finally, we consider a more realistic constraint where the performance is verified only at predefined checkpoints (instead of at every step) and show how this relaxed constraint favorably impacts the regret and empirical performance of CLUCB2.

scholarly journals Efficient Lifted Planning with Regression-Based Heuristics

2021 ◽  
Author(s):  
Hadi Qovaizi
Keyword(s):  
Organic Synthesis ◽  
State Of The Art ◽  
Transition System ◽  
Modern State ◽  
Synthetic Approach ◽  
Best First Search ◽  
Planning Task ◽  
International Planning ◽  
Domain Independent ◽  
Novel Algorithm

Modern state-of-the-art planners operate by generating a grounded transition system prior to performing search for a solution to a given planning task. Some tasks involve a significant number of objects or entail managing predicates and action schemas with a significant number of arguments. Hence, this instantiation procedure can exhaust all available memory and therefore prevent a planner from performing search to find a solution. This thesis explores this limitation by presenting a benchmark set of problems based on Organic Chemistry Synthesis that was submitted to the latest International Planning Competition (IPC-2018). This benchmark was constructed to gauge the performance of the competing planners given that instantiation is an issue. Furthermore, a novel algorithm, the Regression-Based Heuristic Planner (RBHP), is developed with the aim of averting this issue. RBHP was inspired by the retro-synthetic approach commonly used to solve organic synthesis problems efficiently. RBHP solves planning tasks by applying domain independent heuristics, computed by regression, and performing best-first search. In contrast to most modern planners, RBHP computes heuristics backwards by applying the goal-directed regression operator. However, the best-first search proceeds forward similar to other planners. The proposed planner is evaluated on a set of planning tasks included in previous International Planning Competitions (IPC) against a subset of the top scoring state-of-the-art planners submitted to the IPC-2018.

Multistep Flow Prediction on Car-Sharing Systems: A Multi-Graph Convolutional Neural Network with Attention Mechanism

2019 ◽  
Vol 29 (11n12) ◽  
pp. 1727-1740 ◽  
Author(s):  
Hongming Zhu ◽  
Yi Luo ◽  
Qin Liu ◽  
Hongfei Fan ◽  
Tianyou Song ◽  
...  
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Spatial Relations ◽  
Attention Mechanism ◽  
Temporal Relations ◽  
Car Sharing ◽  
Flow Prediction ◽  
Proposed Model ◽  
Decoder Architecture ◽  
Deep Learning Model

Multistep flow prediction is an essential task for the car-sharing systems. An accurate flow prediction model can help system operators to pre-allocate the cars to meet the demand of users. However, this task is challenging due to the complex spatial and temporal relations among stations. Existing works only considered temporal relations (e.g. using LSTM) or spatial relations (e.g. using CNN) independently. In this paper, we propose an attention to multi-graph convolutional sequence-to-sequence model (AMGC-Seq2Seq), which is a novel deep learning model for multistep flow prediction. The proposed model uses the encoder–decoder architecture, wherein the encoder part, spatial and temporal relations are encoded simultaneously. Then the encoded information is passed to the decoder to generate multistep outputs. In this work, specific multiple graphs are constructed to reflect spatial relations from different aspects, and we model them by using the proposed multi-graph convolution. Attention mechanism is also used to capture the important relations from previous information. Experiments on a large-scale real-world car-sharing dataset demonstrate the effectiveness of our approach over state-of-the-art methods.

scholarly journals Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Xinming Zhang ◽  
Doudou Wang ◽  
Haiyan Chen ◽  
Wentao Mao ◽  
Shangwang Liu ◽  
...  
Keyword(s):  
Firefly Algorithm ◽  
State Of The Art ◽  
Poor Performance ◽  
Quadratic Assignment ◽  
High Complexity ◽  
Computation Complexity ◽  
Swarm Optimization ◽  
Improved Firefly Algorithm ◽  
Complex Functions ◽  
Quadratic Assignment Problems

Laplacian Biogeography-Based Optimization (LxBBO) is a BBO variant which improves BBO’s performance largely. When it solves some complex problems, however, it has some drawbacks such as poor performance, weak operability, and high complexity, so an improved LxBBO (ILxBBO) is proposed. First, a two-global-best guiding operator is created for guiding the worst habitat mainly to enhance the exploitation of LxBBO. Second, a dynamic two-differential perturbing operator is proposed for the first two best habitats’ updating to improve the global search ability in the early search phase and the local one in the late search one, respectively. Third, an improved Laplace migration operator is formulated for other habitats’ updating to improve the search ability and the operability. Finally, some measures such as example learning, mutation operation removing, and greedy selection are adopted mostly to reduce the computation complexity of LxBBO. A lot of experimental results on the complex functions from the CEC-2013 test set show ILxBBO obtains better performance than LxBBO and quite a few state-of-the-art algorithms do. Also, the results on Quadratic Assignment Problems (QAPs) show that ILxBBO is more competitive compared with LxBBO, Improved Particle Swarm Optimization (IPSO), and Improved Firefly Algorithm (IFA).

scholarly journals Rate-Invariant Modeling in Lie Algebra for Activity Recognition

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1888
Author(s):  
Malek Boujebli ◽  
Hassen Drira ◽  
Makram Mestiri ◽  
Imed Riadh Farah
Keyword(s):  
Computer Vision ◽  
Lie Algebra ◽  
Activity Recognition ◽  
Human Activity ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
High Complexity ◽  
Human Motions ◽  
Inter Frame ◽  
Automatic Systems

Human activity recognition is one of the most challenging and active areas of research in the computer vision domain. However, designing automatic systems that are robust to significant variability due to object combinations and the high complexity of human motions are more challenging. In this paper, we propose to model the inter-frame rigid evolution of skeleton parts as the trajectory in the Lie group SE(3)×…×SE(3). The motion of the object is similarly modeled as an additional trajectory in the same manifold. The classification is performed based on a rate-invariant comparison of the resulting trajectories mapped to a vector space, the Lie algebra. Experimental results on three action and activity datasets show that the proposed method outperforms various state-of-the-art human activity recognition approaches.

scholarly journals Visual-Semantic Graph Reasoning for Pedestrian Attribute Recognition

2019 ◽  
Vol 33 ◽  
pp. 8634-8641 ◽  
Author(s):  
Qiaozhe Li ◽  
Xin Zhao ◽  
Ran He ◽  
Kaiqi Huang
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Relational Learning ◽  
Spatial Relations ◽  
Semantic Relations ◽  
Prediction Problem ◽  
Convolutional Network ◽  
Semantic Graph ◽  
Spatial Graph ◽  
Attribute Recognition

Pedestrian attribute recognition in surveillance is a challenging task due to poor image quality, significant appearance variations and diverse spatial distribution of different attributes. This paper treats pedestrian attribute recognition as a sequential attribute prediction problem and proposes a novel visual-semantic graph reasoning framework to address this problem. Our framework contains a spatial graph and a directed semantic graph. By performing reasoning using the Graph Convolutional Network (GCN), one graph captures spatial relations between regions and the other learns potential semantic relations between attributes. An end-to-end architecture is presented to perform mutual embedding between these two graphs to guide the relational learning for each other. We verify the proposed framework on three large scale pedestrian attribute datasets including PETA, RAP, and PA100k. Experiments show superiority of the proposed method over state-of-the-art methods and effectiveness of our joint GCN structures for sequential attribute prediction.

scholarly journals Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications

2020 ◽  
Vol 10 (4) ◽  
pp. 1540 ◽  
Author(s):  
René Kirrbach ◽  
Michael Faulwaßer ◽  
Tobias Schneider ◽  
Philipp Meißner ◽  
Alexander Noack ◽  
...  
Keyword(s):  
High Speed ◽  
State Of The Art ◽  
Power Level ◽  
Optical Power ◽  
Full Duplex ◽  
Optical Wireless ◽  
High Complexity ◽  
Optical Wireless Communications ◽  
Optical Crosstalk ◽  
Communication Links

High-speed rotary communication links exhibit high complexity and require challenging assembly tolerances. This article investigates the use of optical wireless communications (OWC) for on-axis rotary communication scenarios. First, OWC is compared with other state-of-the-art technologies. Different realization approaches for bidirectional, full-duplex links are discussed. For the most promising approach, a monolithic hybrid transmitter-receiver lens is designed by ray mapping methodology. Ray tracing simulations are used to study the alignment-depended receiver power level and to determine the effect of optical crosstalk. Over a distance of 12.5 m m , the lens achieves an optical power level at the receiver of − 16.2 dBm to − 8.7 dBm even for misalignments up to 3 m m .

Leveraging Variable Elimination for Efficiently Reasoning about Qualitative Constraints

2018 ◽  
Vol 27 (04) ◽  
pp. 1860001 ◽  
Author(s):  
Michael Sioutis ◽  
Zhiguo Long ◽  
Sanjiang Li
Keyword(s):  
Graphical Models ◽  
State Of The Art ◽  
Exact Inference ◽  
Local Consistency ◽  
Constraint Networks ◽  
Constraint Network ◽  
Variable Elimination ◽  
Region Connection Calculus ◽  
Effective Manner ◽  
Novel Algorithm

We introduce, study, and evaluate a novel algorithm in the context of qualitative constraint-based spatial and temporal reasoning that is based on the idea of variable elimination, a simple and general exact inference approach in probabilistic graphical models. Given a qualitative constraint network [Formula: see text], our algorithm utilizes a particular directional local consistency, which we denote by [Formula: see text]-consistency, in order to efficiently decide the satisfiability of [Formula: see text]. Our discussion is restricted to distributive subclasses of relations, i.e., sets of relations closed under converse, intersection, and weak composition and for which weak composition distributes over non-empty intersections for all of their relations. We demonstrate that enforcing [Formula: see text]-consistency in a given qualitative constraint network defined over a distributive subclass of relations allows us to decide its satisfiability, and obtain similar useful results for the problems of minimal labelling and redundancy. Further, we present a generic method that allows extracting a scenario from a satisfiable network, i.e., an atomic satisfiable subnetwork of that network, in a very simple and effective manner. The experimentation that we have conducted with random and real-world qualitative constraint networks defined over a distributive subclass of relations of the Region Connection Calculus and the Interval Algebra, shows that our approach exhibits unparalleled performance against state-of-the-art approaches for checking the satisfiability of such constraint networks.

Sign in / Sign up

Export Citation Format

Share Document