Shape Anchor Guided Holistic Indoor Scene Understanding

This paper proposes a shape anchor guided learning strategy (AncLearn) for robust holistic indoor scene understanding. We observe that the search space constructed by current methods for proposal feature grouping and instance point sampling often introduces massive noise to instance detection and mesh reconstruction. Accordingly, we develop AncLearn to generate anchors that dynamically fit instance surfaces to (i) unmix noise and target-related features for offering reliable proposals at the detection stage, and (ii) reduce outliers in object point sampling for directly providing well-structured geometry priors without segmentation during reconstruction. We embed AncLearn into a reconstruction-from-detection learning system (AncRec) to generate high-quality semantic scene models in a purely instance-oriented manner. Experiments conducted on the challenging ScanNetv2 dataset demonstrate that our shape anchor-based method consistently achieves state-of-the-art performance in terms of 3D object detection, layout estimation, and shape reconstruction. The code will be available at https://github.com/Geo-Tell/AncRec

A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition

We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition. Measuring perplexity and frame-level classification accuracy, kernel-based acoustic models are as effective as their DNN counterparts. However, on token-error-rates DNN models can be significantly better. We have discovered that this might be attributed to DNN's unique strength in reducing both the perplexity and the entropy of the predicted posterior probabilities. Motivated by our findings, we propose a new technique, entropy regularized perplexity, for model selection. This technique can noticeably improve the recognition performance of both types of models, and reduces the gap between them. While effective on Broadcast News, this technique could be also applicable to other tasks.Comment: arXiv admin note: text overlap with arXiv:1411.400

A Simple Graphene NH₃ Gas Sensor via Laser Direct Writing.

Ammonia gas sensors are very essential in many industries and everyday life. However, their complicated fabrication process, severe environmental fabrication requirements and desorption of residual ammonia molecules result in high cost and hinder their market acceptance. Here, laser direct writing is used to fabricate three parallel porous 3D graphene lines on a polyimide (PI) tape to simply construct an ammonia gas sensor. The middle one works as an ammonia sensing element and the other two on both sides work as heaters to improve the desorption performance of the sensing element to ammonia gas molecules. The graphene lines were characterized by scanning electron microscopy and Raman spectroscopy. The response and recovery time of the sensor without heating are 214 s and 222 s with a sensitivity of 0.087% ppm-1 for sensing 75 ppm ammonia gas, respectively. The experimental results prove that under the optimized heating temperature of about 70 °C the heaters successfully help implement complete desorption of residual NH₃ showing a good sensitivity and cyclic stability

Acoustic waves for active reduction of droplet impact contact time

Minimizing droplet impact contact time is critical for applications such as self-cleaning, antierosion or anti-icing. Recent studies have used the texturing of surfaces to split droplets during impact or inducing asymmetric spreading, but these require specifically designed substrates that cannot be easily reconfigured. A key challenge is to realize an effective reduction in contact time during droplet impingement on a smooth surface without texturing but with active and programmable control. Our experimental results show that surface acoustic waves (SAWs), generated at a location distant from a point of droplet impact, can be used to minimize contact time by as much as 35% without requiring a textured surface. Additionally, the ability to switch on and off the SAWs means that a reduction in droplet impact contact time on a surface can be controlled in a programmable manner. Moreover, our results show that, by applying acoustic waves, the impact regime of the droplet on the solid surface can be changed from deposition or partial rebound to complete rebound. To study the dynamics of droplet impact, we develop a numerical model for multiphase flow and simulate different droplet impingement scenarios. Numerical results reveal that the acoustic waves can be used to modify and control the internal velocity fields inside the droplet. By breaking the symmetry of the internal recirculation patterns inside the droplet, the kinetic energy recovered from interfacial energy during the retraction process is increased, and the droplet can be fully separated from the surface with a much shorter contact time. Our work opens up opportunities to use SAW devices to minimize the contact time, change the droplet impact regime, and program or control the droplet’s rebounding on smooth or planar and curved surfaces, as well as rough or textured surfaces

Wide range of droplet jetting angles by thin-film based surface acoustic waves

Nozzleless jetting of droplets with different jetting angles is a crucial requirement for 2D and 3D printing/bioprinting applications, and Rayleigh mode surface acoustic waves (SAWs) could be a potential technique for achieving this purpose. Currently, it is critical to vary the jetting angles of liquid droplets induced by SAWs and control the liquid jet directions. Generally, the direction of the liquid jet induced by SAWs generated from a bulk piezoelectric substrate such as LiNbO3 is along the theoretical Rayleigh angle of ~22o. In this study, we designed and manufactured thin-film SAW devices by depositing ZnO films on different substrates (including silicon and aluminium) to realize a wide range of jetting angles from ~16o to 55o using propagating waves generated from one interdigital transducer (IDT). We then systematically investigated different factors affecting the jetting angles, including liquid properties, applied SAW power and SAW device resonant frequency. Finally, we proposed various methods using thin-film SAW devices together with different transducer designs for realizing a wide range of jetting angles within the 3D domain

A highly sensitive silicon nanowire array sensor for joint detection of tumor markers CEA and AFP

Liver cancer is one of the malignant tumors with the highest fatality rate and increasing incidence, which has no effective treatment plan. Early diagnosis and early treatment of liver cancer play a vital role in prolonging the survival period of patients and improving the cure rate. Carcinoembryonic antigen (CEA) and alpha-fetoprotein (AFP) are two crucial tumor markers for liver cancer diagnosis. In this work, we firstly proposed a wafer-level, highly controlled silicon nanowire (SiNW) field-effect transistor (FET) joint detection sensor for highly sensitive and selective detection of CEA and AFP. The SiNWs-FET joint detection sensor possesses 4 sensing regions. Each sensing region consists of 120 SiNWs arranged in a 15 × 8 array. The SiNW sensor was developed by using a wafer-level and highly controllable top-down manufacturing technology to achieve the repeatability and controllability of device preparation. To identify and detect CEA/AFP, we modified the corresponding CEA antibodies/AFP antibodies to the sensing region surface after a series of surface modification processes, including O2 plasma treatment, soaking in 3-aminopropyltriethoxysilane (APTES) solution, and soaking in glutaraldehyde (GA) solution. The experimental results showed that the SiNW array sensor has superior sensitivity with a real-time ultralow detection limit of 0.1 fg ml−1 (AFP in 0.1× PBS) and 1 fg ml−1 (CEA in 0.1× PBS). Also, the logarithms of the concentration of CEA (from 1 fg ml−1 to 10 pg ml−1) and AFP (from 0.1 fg ml−1 to 100 pg ml−1) achieved conspicuously linear relationships with normalized current changes. The R2 of AFP in 0.1× PBS and R2 of CEA in 0.1× PBS were 0.99885 and 0.99677, respectively. Furthermore, the sensor could distinguish CEA/AFP from interferents at high concentrations. Importantly, even in serum samples, our sensor could successfully detect CEA/AFP. This demonstrates the promising clinical development of our sensor

Kernel Approximation Methods for Speech Recognition

International audienceWe study the performance of kernel methods on the acoustic modeling task for automatic speech recognition, and compare their performance to deep neural networks (DNNs). To scale the kernel methods to large data sets, we use the random Fourier feature method of Rahimi and Recht (2007). We propose two novel techniques for improving the performance of kernel acoustic models. First, we propose a simple but effective feature selection method which reduces the number of random features required to attain a fixed level of performance. Second, we present a number of metrics which correlate strongly with speech recognition performance when computed on the heldout set; we attain improved performance by using these metrics to decide when to stop training. Additionally, we show that the linear bottleneck method of Sainath et al. (2013a) improves the performance of our kernel models significantly, in addition to speeding up training and making the models more compact. Leveraging these three methods, the kernel methods attain token error rates between 0.5% better and 0.1% worse than fully-connected DNNs across four speech recognition data sets, including the TIMIT and Broadcast News benchmark tasks

A supersensitive silicon nanowire array biosensor for quantitating tumor marker ctDNA

Cancer has become one of the major diseases threatening human health and life. Circulating tumor DNA (ctDNA) testing, as a practical liquid biopsy technique, is a promising method for cancer diagnosis, targeted therapy and prognosis. Here, for the first time, a field effect transistor (FET) biosensor based on uniformly sized high-response silicon nanowire (SiNW) array was studied for real-time, label-free, super-sensitive detection of PIK3CA E542K ctDNA. High-response 120-SiNWs array was fabricated on a (111) silicon-on-insulator (SOI) by the complementary metal oxide semiconductor (CMOS)-compatible microfabrication technology. To detecting ctDNA, we modified the DNA probe on the SiNWs array through silanization. The experimental results demonstrated that the as-fabricated biosensor had significant superiority in ctDNA detection, which achieved ultralow detection limit of 10 aM and had a good linearity under the ctDNA concentration range from 0.1 fM to 100 pM. This biosensor can recognize complementary target ctDNA from one/two/full-base mismatched DNA with high selectivity. Furthermore, the fabricated SiNW-array FET biosensor successfully detected target ctDNA in human serum samples, indicating a good potential in clinical applications in the future

Entropic Interactions in Semiflexible Polymer Nanocomposite Melts

By employing molecular dynamics simulations, we explored the effective depletion zone for nanoparticles (NP) immersed in semiflexible polymer melts and calculated the entropic depletion interactions between a pair of NPs in semiflexible polymer nanocomposite melts. The average depletion zone volumes rely mainly on polymer chain stiffness and increase with chain stiffness increasing. In the semiflexible polymer nanocomposite melts, the entropic depletion interactions are attractive and anisotropic, and increase with chain stiffness increasing. Meanwhile, the attractive interactions between NPs and polymers can also affect strongly the entropic depletion interactions. For the semiflexible polymer nanocomposite melts in the athermal system, the entropic depletion interactions change from anisotropic to isotropic when the NP/polymer interactions increase. For NPs in the rodlike polymer melts, a mixture structure of contact/“bridging” aggregations for NPs is formed at a strong attractive NP/polymer interaction. Our calculations can provide an effective framework to predict the morphology of NPs immersed in semiflexible polymer melts