The Conditional Mode in Parametric Frontier Models

We survey formulations of the conditional mode estimator for technical inefficiency in parametric stochastic frontier models with normal errors and introduce new formulations for models with Laplace errors. We prove the conditional mode estimator converges pointwise to the true inefficiency value as the noise variance goes to zero. We also prove that the conditional mode estimator in the normal-exponential model achieves near-minimax optimality. Our minimax theorem implies that the worst-case risk occurs when many firms are nearly efficient, and the conditional mode estimator minimizes estimation risk in this case by estimating these small inefficiency firms as efficient. Unlike the conditional expectation estimator, the conditional mode estimator produces multiple firms with inefficiency estimates exactly equal to zero, suggesting a rule for selecting a subset of maximally efficient firms. Our simulation results show that this “zero-mode subset” has reasonably high probability of containing the most efficient firm, particularly when inefficiency is exponentially distributed. The rule is easy to apply and interpret for practitioners. We include an empirical example demonstrating the merits of the conditional mode estimator

Stretchable elastic synaptic transistors for neurologically integrated soft engineering systems

Artificial synaptic devices that can be stretched similar to those appearing in soft-bodied animals, such as earthworms, could be seamlessly integrated onto soft machines toward enabled neurological functions. Here, we report a stretchable synaptic transistor fully based on elastomeric electronic materials, which exhibits a full set of synaptic characteristics. These characteristics retained even the rubbery synapse that is stretched by 50%. By implementing stretchable synaptic transistor with mechanoreceptor in an array format, we developed a deformable sensory skin, where the mechanoreceptors interface the external stimulations and generate presynaptic pulses and then the synaptic transistors render postsynaptic potentials. Furthermore, we demonstrated a soft adaptive neurorobot that is able to perform adaptive locomotion based on robotic memory in a programmable manner upon physically tapping the skin. Our rubbery synaptic transistor and neurologically integrated devices pave the way toward enabled neurological functions in soft machines and other applications

The Conditional Mode in Parametric Frontier Models

We survey formulations of the conditional mode estimator for technical inefficiency in parametric stochastic frontier models with normal errors and introduce new formulations for models with Laplace errors. We prove the conditional mode estimator converges pointwise to the true inefficiency value as the noise variance goes to zero. We also prove that the conditional mode estimator in the normal-exponential model achieves near-minimax optimality. Our minimax theorem implies that the worst-case risk occurs when many firms are nearly efficient, and the conditional mode estimator minimizes estimation risk in this case by estimating these small inefficiency firms as efficient. Unlike the conditional expectation estimator, the conditional mode estimator produces multiple firms with inefficiency estimates exactly equal to zero, suggesting a rule for selecting a subset of maximally efficient firms. Our simulation results show that this “zero-mode subset” has reasonably high probability of containing the most efficient firm, particularly when inefficiency is exponentially distributed. The rule is easy to apply and interpret for practitioners. We include an empirical example demonstrating the merits of the conditional mode estimator

ENHANCED AIR-GAP CONTROL FOR HIGH-SPEED PLASMONIC LITHOGRAPHY USING SOLID IMMERSION LENS WITH SHARP-RIDGE NANOAPERTURE

INTRODUCTION Recently, plasmonic nanolithography is studied by many researchers (1, 2 and 3). This presented a low-cost and highthroughput approach to maskless nanolithography technique that uses a metallic sharp-ridge nanoaperture with a high strong nanometersized optical spot induced by surface plasmon resonance. However, these nanometer-scale spots generated by metallic nanoapertures are formed in only the near-field region, which makes it very difficult to pattern above the photoresist surface at high-speeds. To overcome this problem, we have designed and developed another type plasmonic nanolithography method that uses a metallic sharp-ridge nanometer-scale aperture and a solid immersion lens (SIL) based near-field recording technology. A plasmonic SIL optical head that consists of a metallic nanoaperture deposited on a SIL can fly ~20 nm above a photoresist (PR)-coated Si-wafer that moves in the linear direction at high speed that is several hundred times faster than existing plasmonic nanolithography methods. Because wafer size is small with several inches, moving velocity and acceleration of lateral stage should be fast to achieve high-speed plasmonic lithography. However, as moving velocity and acceleration are higher, disturbances are dramatically greater. This leads that maintaining gap between SIL and PR coated wafer and line-width is unstable. With existing control method, the residual gap error is over 8 nm at 200 mm/s. To maintain stable line-width, the residual gap error is should be under 2 nm. In this paper, to maintain stable gap and line-width at several hundred mm/s in linear direction, we propose enhanced air-gap control for high-speed plasmonic lithography using SIL. Firstly, we designed the base controller (lead and lag compensator), disturbance observer (DOB) and narrow band disturbance filter (NBDF) that is used to enhance the performance of the air-gap controller under repeatable and nonrepeatable disturbances (4, 5). And, through experiments, the feasibility of the proposed air-gap controller has been verified and checked that the stable gap and line-width are maintained at several hundred mm/s. EXPERIMENTAL SETUP AND CONTROLLER DESIGN EXPERIMENTAL RESULTS CONCLUSIONS To improve the control performance for high-speed plasmonic lithography using solid immersion lens, the control algorithm was designed with narrow-band disturbance filter (NBDF). As the experimental results indicate, the dominant frequency components in the system, which are due to vibration and resonance, are sufficiently eliminated by the proposed integrated control algorithm for the highspeed plasmonic lithography using SIL. Additionally, the nano-gap controller with the NBDF and the double DOB is proposed. In cases where the NBDF-based controller was used together with the double DOB, the air-gap controller performance was improved to avoid disturbance. ACKNOWLEDGMENT

Evaluation of Electropolishing Characteristics of 316L Stainless Steel Tube in Contaminated Electrolyte

In the electropolishing process, the polishing quality of the metal surface varies according to the contamination of the electrolyte. In this study, the electrolyte was evaluated according to the usage time, and the effect of each factor on electropolishing was investigated. As the electrolyte is contaminated, the concentration of metal ions in the electrolyte increases and the ion conductivity decreases. In addition, the pH and specific gravity of the electrolyte increase due to the metal sludge formed as the metal ion concentration increases. When the electrolyte usage time was more than 5 days, many scratches remained on the surface of 316L stainless steel, and relatively high surface roughness was measured. The surface roughness improvement rate compared to the initial specimen was 30% for the unused electrolyte, 26% on the 3rd day, 19% on the 5th day, and 17.5% on the 13th day. Since the low current density due to electrolyte contamination causes a decrease in polishing efficiency, initial scratches on the metal surface still exist on the polished surface. Therefore, it is necessary to manage the electrolyte to maintain the quality of electropolishing

Hybrid integrated photomedical devices for wearable vital sign tracking

In light of the importance of and challenges inherent in realizing a wearable healthcare platform for simultaneously recognizing, preventing, and treating diseases while tracking vital signs, the development of simple and customized functional devices has been required. Here, we suggest a new approach to make a stretchable light waveguide which can be combined with integrated functional devices, such as organic photodetectors and nanowire-based heaters, for multifunctional healthcare monitoring. Controlling the reflection condition of the medium gave us a solid design rule for strong light emission in our stretchable waveguides. Based on this rule, the stretchable light waveguide (up to 50% strain) made of polydimethylsiloxane was successfully demonstrated with strong emissions. We also incorporated highly sensitive organic photodetectors and silver nanowire-based heaters with the stretchable waveguide for the detection of vital signs, including heart rate, deep breathing, coughs, and blood oxygen saturation. Through these multifunctional performances, we have successfully demonstrated that our stretchable light waveguide has a strong potential for multifunctional healthcare monitoring

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures