Improving Situational Awareness in RoboFlag

Situational awareness in competitive games has started to attract increasing attention in the control community. It studies how a robot identifies, understands and predicts the significant factors around it, which is essential for effective decision making and performance in any complex and dynamic environment. In this thesis, we investigate the situational awareness problems in RoboFlag, a highly dynamic testbed that comprises a mixture of offense and defense games between two robotic teams. To improve situational awareness in RoboFlag, we want to solve two main problems. (1) Real-time position estimation given limited sensing capability. (2) Optimal decision-making strategy based on position estimation. Monte Carlo Localization (MCL), a statistical method based on particle representations of probability densities moving sequentially in discrete time, has been shown as an effective and time-efficient method for reliable position estimation, especially when the dynamics of the system and the environment are nonlinear and non-Gaussian, such as RoboFlag. In this thesis, a dynamic weight map, Hospitability Map (H-Map), that measures the ability of a target to move and maneuver at each location of the field, has been applied to MCL to enhance the efficiency and accuracy of MCL in resampling phase. Empirical results illustrate that H-Map based MCL method improves situational awareness in Roboflag by providing reliable position prediction and enhancing decisionmaking performance.</p

Graphene Helicoid: The Distinct Properties Promote Application of Graphene Related Materials in Thermal Management

The extremely high thermal conductivity of graphene has received great attention both in experiments and calculations. Obviously, new feature in thermal properties is of primary importance for application of graphene-based materials in thermal management in nanoscale. Here, we studied the thermal conductivity of graphene helicoid, a newly reported graphene-related nanostructure, using molecular dynamics simulation. Interestingly, in contrast to the converged cross-plane thermal conductivity in multi-layer graphene, axial thermal conductivity of graphene helicoid keeps increasing with thickness with a power law scaling relationship, which is a consequence of the divergent in-plane thermal conductivity of two-dimensional graphene. Moreover, the large overlap between adjacent layers in graphene helicoid also promotes higher thermal conductivity than multi-layer graphene. Furthermore, in the small strain regime (< 10%), compressive strain can effectively increase the thermal conductivity of graphene helicoid, while in the ultra large strain regime (~100% to 500%), tensile strain does not decrease the heat current, unlike that in generic solid-state materials. Our results reveal that the divergence in thermal conductivity, associated with the anomalous strain dependence and the unique structural flexibility, make graphene helicoid a new platform for studying fascinating phenomena of key relevance to the scientific understanding and technological applications of graphene-related materials.Comment: 7 figure

Paired-angle-rotation scanning optical coherence tomography forward-imaging probe

We report a novel forward-imaging optical coherence tomography (OCT), needle-probe paired-angle-rotation scanning OCT (PARS-OCT) probe. The probe uses two rotating angled gradient-index lenses to scan the output OCT probe beam over a wide angular arc (∼19° half-angle) of the region forward of the probe. Among other advantages, this probe design is readily amenable to miniaturization and is capable of a variety of scan modes, including volumetric scans. To demonstrate the advantages of the probe design, we have constructed a prototype probe with an outer diameter of 1.65 mm and employed it to acquire four OCT images, with a 45° angle between adjacent images, of the gill structure of a Xenopus laevis tadpole. The system sensitivity was measured to be 93 dB by using the prototype probe with an illumination power of 450 μW on the sample. Moreover, the axial and the lateral resolutions of the probe are 9.3 and 10.3-12.5 μm, respectively

ParGANDA: Making Synthetic Pedestrians A Reality For Object Detection

Object detection is the key technique to a number of Computer Vision applications, but it often requires large amounts of annotated data to achieve decent results. Moreover, for pedestrian detection specifically, the collected data might contain some personally identifiable information (PII), which is highly restricted in many countries. This label intensive and privacy concerning task has recently led to an increasing interest in training the detection models using synthetically generated pedestrian datasets collected with a photo-realistic video game engine. The engine is able to generate unlimited amounts of data with precise and consistent annotations, which gives potential for significant gains in the real-world applications. However, the use of synthetic data for training introduces a synthetic-to-real domain shift aggravating the final performance. To close the gap between the real and synthetic data, we propose to use a Generative Adversarial Network (GAN), which performsparameterized unpaired image-to-image translation to generate more realistic images. The key benefit of using the GAN is its intrinsic preference of low-level changes to geometric ones, which means annotations of a given synthetic image remain accurate even after domain translation is performed thus eliminating the need for labeling real data. We extensively experimented with the proposed method using MOTSynth dataset to train and MOT17 and MOT20 detection datasets to test, with experimental results demonstrating the effectiveness of this method. Our approach not only produces visually plausible samples but also does not require any labels of the real domain thus making it applicable to the variety of downstream tasks

Direct single-molecule dynamic detection of chemical reactions.

Single-molecule detection can reveal time trajectories and reaction pathways of individual intermediates/transition states in chemical reactions and biological processes, which is of fundamental importance to elucidate their intrinsic mechanisms. We present a reliable, label-free single-molecule approach that allows us to directly explore the dynamic process of basic chemical reactions at the single-event level by using stable graphene-molecule single-molecule junctions. These junctions are constructed by covalently connecting a single molecule with a 9-fluorenone center to nanogapped graphene electrodes. For the first time, real-time single-molecule electrical measurements unambiguously show reproducible large-amplitude two-level fluctuations that are highly dependent on solvent environments in a nucleophilic addition reaction of hydroxylamine to a carbonyl group. Both theoretical simulations and ensemble experiments prove that this observation originates from the reversible transition between the reactant and a new intermediate state within a time scale of a few microseconds. These investigations open up a new route that is able to be immediately applied to probe fast single-molecule physics or biophysics with high time resolution, making an important contribution to broad fields beyond reaction chemistry

Towards a Semantic Perceptual Image Metric

We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification. We fit the metric to a new database based on 140k unique images annotated with ground truth by human raters who received minimal instruction. The resulting metric shows competitive performance on TID 2013, a database widely used to assess image quality assessments methods. More interestingly, it shows strong responses to objects potentially carrying semantic relevance such as faces and text, which we demonstrate using a visualization technique and ablation experiments. In effect, the metric appears to model a higher influence of semantic context on judgments, which we observe particularly in untrained raters. As the vast majority of users of image processing systems are unfamiliar with Image Quality Assessment (IQA) tasks, these findings may have significant impact on real-world applications of perceptual metrics

AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions

This paper introduces a video dataset of spatio-temporally localized Atomic Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual actions in 430 15-minute video clips, where actions are localized in space and time, resulting in 1.58M action labels with multiple labels per person occurring frequently. The key characteristics of our dataset are: (1) the definition of atomic visual actions, rather than composite actions; (2) precise spatio-temporal annotations with possibly multiple annotations for each person; (3) exhaustive annotation of these atomic actions over 15-minute video clips; (4) people temporally linked across consecutive segments; and (5) using movies to gather a varied set of action representations. This departs from existing datasets for spatio-temporal action recognition, which typically provide sparse annotations for composite actions in short video clips. We will release the dataset publicly. AVA, with its realistic scene and action complexity, exposes the intrinsic difficulty of action recognition. To benchmark this, we present a novel approach for action localization that builds upon the current state-of-the-art methods, and demonstrates better performance on JHMDB and UCF101-24 categories. While setting a new state of the art on existing datasets, the overall results on AVA are low at 15.6% mAP, underscoring the need for developing new approaches for video understanding.Comment: To appear in CVPR 2018. Check dataset page https://research.google.com/ava/ for detail

Forward-cone-imaging OCT needle probe

We propose a novel forward-imaging OCT needle probe. The probe is based on the use of two angled GRIN lenses that can freely rotate with respect to each other. The probe is capable of scanning a forward cone volume ahead of the probe tip. Different scanning modes, such as the conventional OCT B-scan mode, spiral mode and starburst B-scan mode, can be obtained by adjusting the angular scan velocities of the two GRIN lenses. We develop a prototype probe and demonstrate its capability to acquire OCT images. In this paper we give the characteristics of the prototype probe and display images of different part of tadpole acquired by the probe. The longitudinal resolution, lateral resolution and the signal-to-noise ratio of the system are 10 μm, 10 μm and 93 dB, respectively

The lncRNA ADAMTS9-AS2 Regulates RPL22 to Modulate TNBC Progression via Controlling the TGF-β Signaling Pathway

BackgroundLong non-coding RNAs (lncRNAs) are key regulators of triple-negative breast cancer (TNBC) progression, but further work is needed to fully understand the functional relevance of these non-coding RNAs in this cancer type. Herein, we explored the functional role of the lncRNA ADAMTS9-AS2 in TNBC.MethodsNext-generation sequencing was conducted to compare the expression of different lncRNAs in TNBC tumor and paracancerous tissues, after which ADAMTS9-AS2differential expression in these tumor tissues was evaluated via qPCR. The functional role of this lncRNA was assessed by overexpressing it in vitro and in vivo. FISH and PCR were used to assess the localization of ADAMTS9-AS2within cells. Downstream targets of ADAMTS9-AS2 signaling were identified via RNA pulldown assays and transcriptomic sequencing.ResultsThe expression ofADAMTS9-AS2 was decreased in TNBC tumor samples (P &lt; 0.05), with such downregulation being correlated with TNM stage, age, and tumor size. Overexpressing ADAMTS9-AS2 promoted the apoptotic death and cell cycle arrest of tumor cells in vitro and inhibited tumor growth in vivo. From a mechanistic perspective, ADAMTS9-AS2 was found to control the expression of RPL22 and to thereby modulate TGF-β signaling to control TNBC progression.ConclusionADAMTS9-AS2 controls the expression of RPL22 and thereby regulates TNBC malignancy via the TGF-β signaling pathway