arXiv subjects

Thanh Nguyen

Publications and source records attributed to Thanh Nguyen.

At least 91 records · Page 5Linked to original sources

arXiv2020

A new multi-beam apparatus for the study of surface chemistry routes to formation of complex organic molecules in space

A multi-beam ultra-high vacuum apparatus is presented. In this article we describe the design and construction of a new laboratory astrophysics experiment -- VErs de NoUvelles Synthèses (VENUS) -- that recreates the solid-state non-energetic formation conditions of complex organic molecules in dark clouds and circumstellar environments. The novel implementation of four operational differentially-pumped beam lines will be used to determine the feasibility and the rates for the various reactions that contribute to formation of molecules containing more than six atoms. Data are collected by means of Fourier transform infrared spectroscopy and quadrupole mass spectrometry. The gold-coated sample holder reaches temperatures between 7 and 400 K. The apparatus was carefully calibrated and the acquisition system was developed to ensure that experimental parameters are recorded as accurately as possible. A great effort has been made to have the beam lines converge towards the sample. Experiments have been developed to check the beam alignment using reacting systems of neutral species (NH$_3$, H$_2$CO). Preliminary original results were obtained for the NO+H system, which shows that chemistry occurs only in the very first outer layer of the deposited species, that is the chemical layer and the physical layer coincide. This article illustrates the characteristics, performance, and future potential of the new apparatus in view of the forthcoming launch of the James Webb Space Telescope. We show that VENUS will have a major impact through its contributions to surface science and astrochemistry.

astro-ph.IM↗

arXiv2020

Quantized Thermoelectric Hall Effect Induces Giant Power Factor in a Topological Semimetal

Thermoelectrics are promising by directly generating electricity from waste heat. However, (sub-)room-temperature thermoelectrics have been a long-standing challenge due to vanishing electronic entropy at low temperatures. Topological materials offer a new avenue for energy harvesting applications. Recent theories predicted that topological semimetals at the quantum limit can lead to a large, non-saturating thermopower and a quantized thermoelectric Hall conductivity approaching a universal value. Here, we experimentally demonstrate the non-saturating thermopower and quantized thermoelectric Hall effect in the topological Weyl semimetal (WSM) tantalum phosphide (TaP). An ultrahigh longitudinal thermopower Sxx= 1.1x10^3 muV/K and giant power factor ~525 muW/cm/K^2 are observed at ~40K, which is largely attributed to the quantized thermoelectric Hall effect. Our work highlights the unique quantized thermoelectric Hall effect realized in a WSM toward low-temperature energy harvesting applications.

cond-mat.mes-hall↗

arXiv2020

Market Equilibrium in Multi-tier Supply Chain Networks

We consider a sequential decision model over multi-tier supply chain networks and show that in particular, for series parallel networks, there is a unique equilibrium. We provide a linear time algorithm to compute the equilibrium and study the impact and invariant of the network structure to the total trade flow and social welfare.

cs.GT↗

arXiv2020

On Variants of Network Flow Stability

We consider a general stable flow problem in a directed and capacitated network, where each vertex has a strict preference list over the incoming and outgoing edges. A flow is stable if no group of vertices forming a path can mutually benefit by rerouting the flow. Motivated by applications in supply chain networks, we generalize the traditional Kirchhoff's law, requiring the outflow is equal to the inflow at every nonterminal node, to a monotone piecewise linear relationship between the inflows and the outflows. We show the existence of a stable flow using Scarf's Lemma, and provide a polynomial time algorithm to find such a stable flow. We further show that finding a minimum cost generalized stable network is NP-hard, while the problem is polynomial time solvable for the traditional stable flow satisfying Kirchhoff's law.

cs.GT↗

arXiv2020

BUZz: BUffer Zones for defending adversarial examples in image classification

We propose a novel defense against all existing gradient based adversarial attacks on deep neural networks for image classification problems. Our defense is based on a combination of deep neural networks and simple image transformations. While straightforward in implementation, this defense yields a unique security property which we term buffer zones. We argue that our defense based on buffer zones offers significant improvements over state-of-the-art defenses. We are able to achieve this improvement even when the adversary has access to the {\em entire} original training data set and unlimited query access to the defense. We verify our claim through experimentation using Fashion-MNIST and CIFAR-10: We demonstrate $<11\%$ attack success rate -- significantly lower than what other well-known state-of-the-art defenses offer -- at only a price of a $11-18\%$ drop in clean accuracy. By using a new intuitive metric, we explain why this trade-off offers a significant improvement over prior work.

cs.LG↗

arXiv2020

Topological Singularity Induced Chiral Kohn Anomaly in a Weyl Semimetal

The electron-phonon interaction (EPI) is instrumental in a wide variety of phenomena in solid-state physics, such as electrical resistivity in metals, carrier mobility, optical transition and polaron effects in semiconductors, lifetime of hot carriers, transition temperature in BCS superconductors, and even spin relaxation in diamond nitrogen-vacancy centers for quantum information processing. However, due to the weak EPI strength, most phenomena have focused on electronic properties rather than on phonon properties. One prominent exception is the Kohn anomaly, where phonon softening can emerge when the phonon wavevector nests the Fermi surface of metals. Here we report a new class of Kohn anomaly in a topological Weyl semimetal (WSM), predicted by field-theoretical calculations, and experimentally observed through inelastic x-ray and neutron scattering on WSM tantalum phosphide (TaP). Compared to the conventional Kohn anomaly, the Fermi surface in a WSM exhibits multiple topological singularities of Weyl nodes, leading to a distinct nesting condition with chiral selection, a power-law divergence, and non-negligible dynamical effects. Our work brings the concept of Kohn anomaly into WSMs and sheds light on elucidating the EPI mechanism in emergent topological materials.

cond-mat.mtrl-sci↗

arXiv2020

Bayesian Learning of Probabilistic Dipole Inversion for Quantitative Susceptibility Mapping

A learning-based posterior distribution estimation method, Probabilistic Dipole Inversion (PDI), is proposed to solve quantitative susceptibility mapping (QSM) inverse problem in MRI with uncertainty estimation. A deep convolutional neural network (CNN) is used to represent the multivariate Gaussian distribution as the approximated posterior distribution of susceptibility given the input measured field. In PDI, such CNN is firstly trained on healthy subjects dataset with labels by maximizing the posterior Gaussian distribution loss function as used in Bayesian deep learning. When tested on new dataset without any label, PDI updates the pre-trained network in an unsupervised fashion by minimizing the KL divergence between the approximated posterior distribution represented by CNN and the true posterior distribution given the likelihood distribution from known physical model and prior distribution. Based on our experiments, PDI provides additional uncertainty estimation compared to the conventional MAP approach, meanwhile addressing the potential discrepancy issue of CNN when test data deviates from training dataset.

eess.IV↗

arXiv2019

Bayesian Optimization with Unknown Search Space

Applying Bayesian optimization in problems wherein the search space is unknown is challenging. To address this problem, we propose a systematic volume expansion strategy for the Bayesian optimization. We devise a strategy to guarantee that in iterative expansions of the search space, our method can find a point whose function value within epsilon of the objective function maximum. Without the need to specify any parameters, our algorithm automatically triggers a minimal expansion required iteratively. We derive analytic expressions for when to trigger the expansion and by how much to expand. We also provide theoretical analysis to show that our method achieves epsilon-accuracy after a finite number of iterations. We demonstrate our method on both benchmark test functions and machine learning hyper-parameter tuning tasks and demonstrate that our method outperforms baselines.

stat.ML↗

arXiv2019

Virtual organelle self-coding for fluorescence imaging via adversarial learning

Fluorescence microscopy plays a vital role in understanding the subcellular structures of living cells. However, it requires considerable effort in sample preparation related to chemical fixation, staining, cost, and time. To reduce those factors, we present a virtual fluorescence staining method based on deep neural networks (VirFluoNet) to transform fluorescence images of molecular labels into other molecular fluorescence labels in the same field-of-view. To achieve this goal, we develop and train a conditional generative adversarial network (cGAN) to perform digital fluorescence imaging demonstrated on human osteosarcoma U2OS cell fluorescence images captured under Cell Painting staining protocol. A detailed comparative analysis is also conducted on the performance of the cGAN network between predicting fluorescence channels based on phase contrast or based on another fluorescence channel using human breast cancer MDA-MB-231 cell line as a test case. In addition, we implement a deep learning model to perform autofocusing on another human U2OS fluorescence dataset as a preprocessing step to defocus an out-focus channel in U2OS dataset. A quantitative index of image prediction error is introduced based on signal pixel-wise spatial and intensity differences with ground truth to evaluate the performance of prediction to high-complex and throughput fluorescence. This index provides a rational way to perform image segmentation on error signals and to understand the likelihood of mis-interpreting biology from the predicted image. In total, these findings contribute to the utility of deep learning image regression for fluorescence microscopy datasets of biological cells, balanced against savings of cost, time, and experimental effort. Furthermore, the approach introduced here holds promise for modeling the internal relationships between organelles and biomolecules within living cells.

eess.IV↗

arXiv2019

Spectroscopic Signatures of Nonlocal Interfacial Coupling in Superconducting FeSe/SrTiO3 Heterostructures

The mechanism of enhanced superconductivity in the one unit-cell (1UC) FeSe film on a SrTiO3 (STO) substrate has stimulated significant research interest but remains elusive. Using low-temperature, voltage-gated Raman spectroscopy and low-temperature valence electron energy loss spectroscopy (VEELS), we characterize the phonon behavior and interfacial charge transfer in single- and few-layer FeSe films on STO. Raman measurements reveal ambipolar softening of the FeSe vibrational modes, mimicking that of the underlying STO substrate. We attribute this behavior to an interfacial coupling effect of STO on FeSe lattice dynamics. This interfacial coupling effect is further supported by local electron effective mass enhancement, which is determined from the red-shift in the FeSe VEELS spectrum near the FeSe/STO interface. Our work sheds light on the possible interfacial mechanisms contributing to the enhanced superconductivity across the FeSe/STO interface and further unveils the potential of low-temperature gated Raman spectroscopy and VEELS in clarifying a broad category of quantum materials.

cond-mat.mes-hall↗

arXiv2019

A Review of Situation Awareness Assessment Approaches in Aviation Environments

Situation awareness (SA) is an important constituent in human information processing and essential in pilots' decision-making processes. Acquiring and maintaining appropriate levels of SA is critical in aviation environments as it affects all decisions and actions taking place in flights and air traffic control. This paper provides an overview of recent measurement models and approaches to establishing and enhancing SA in aviation environments. Many aspects of SA are examined including the classification of SA techniques into six categories, and different theoretical SA models from individual, to shared or team, and to distributed or system levels. Quantitative and qualitative perspectives pertaining to SA methods and issues of SA for unmanned vehicles are also addressed. Furthermore, future research directions regarding SA assessment approaches are raised to deal with shortcomings of the existing state-of-the-art methods in the literature.

cs.HC↗

arXiv2019

Multi-Agent Deep Reinforcement Learning with Human Strategies

Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of learning agents. In this paper, we introduce an approach that integrates human strategies to increase the exploration capacity of multiple deep reinforcement learning agents. We also report the development of our own multi-agent environment called Multiple Tank Defence to simulate the proposed approach. The results show the significant performance improvement of multiple agents that have learned cooperatively with human strategies. This implies that there is a critical need for human intellect teamed with machines to solve complex problems. In addition, the success of this simulation indicates that our multi-agent environment can be used as a testbed platform to develop and validate other multi-agent control algorithms.

cs.LG↗

arXiv2019

Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgery

In robotic surgery, pattern cutting through a deformable material is a challenging research field. The cutting procedure requires a robot to concurrently manipulate a scissor and a gripper to cut through a predefined contour trajectory on the deformable sheet. The gripper ensures the cutting accuracy by nailing a point on the sheet and continuously tensioning the pinch point to different directions while the scissor is in action. The goal is to find a pinch point and a corresponding tensioning policy to minimize damage to the material and increase cutting accuracy measured by the symmetric difference between the predefined contour and the cut contour. Previous study considers finding one fixed pinch point during the course of cutting, which is inaccurate and unsafe when the contour trajectory is complex. In this paper, we examine the soft tissue cutting task by using multiple pinch points, which imitates human operations while cutting. This approach, however, does not require the use of a multi-gripper robot. We use a deep reinforcement learning algorithm to find an optimal tensioning policy of a pinch point. Simulation results show that the multi-point approach outperforms the state-of-the-art method in soft pattern cutting task with respect to both accuracy and reliability.

cs.RO↗

arXiv2018

Locating the avalanche structure and the origin of breakdown generating charge carriers in silicon photomultipliers by using the bias dependent breakdown probability

We present characterization results of two silicon photomultipliers; the Hamamatsu LVR-6050-CN and the Ketek PM3325 WB. With our measurements of the bias dependence of the breakdown probability we are able to draw conclusions about the location and spatial extension of the avalanche region. For the KETEK SiPM we find that the avalanche region is located close to the surface. In the Hamamatsu SiPM the high-field region is located $0.5\,μ$m below the surface, while the volume above is depleted almost until the surface. Furthermore, for the Hamamatsu SiPM we find that charge carriers produced by optical-crosstalk photons enter a cell below the avalanche region as opposed to an earlier device where most of the photons enter a cell from above. In the here tested Hamamatsu device the crosstalk photons probably absorb in the bulk and the generated holes diffuse into the active volume of the cell within 2\,ns and initiate a breakdown. The present paper is an attempt to spur further interest in the use of the bias dependence of the breakdown probability and establish it as a standard tool not only to determine the location of the high-field region but also to determine the origin of charge carriers relative to the high-field region. With the knowledge of where the charges come from it should be possible to further improve the optical crosstalk, dark count, and afterpulsing characteristics of SiPM.

physics.ins-det↗

arXiv2018

Deep learning approach to Fourier ptychographic microscopy

Convolutional neural networks (CNNs) have gained tremendous success in solving complex inverse problems. The aim of this work is to develop a novel CNN framework to reconstruct video sequence of dynamic live cells captured using a computational microscopy technique, Fourier ptychographic microscopy (FPM). The unique feature of the FPM is its capability to reconstruct images with both wide field-of-view (FOV) and high resolution, i.e. a large space-bandwidth-product (SBP), by taking a series of low resolution intensity images. For live cell imaging, a single FPM frame contains thousands of cell samples with different morphological features. Our idea is to fully exploit the statistical information provided by this large spatial ensemble so as to make predictions in a sequential measurement, without using any additional temporal dataset. Specifically, we show that it is possible to reconstruct high-SBP dynamic cell videos by a CNN trained only on the first FPM dataset captured at the beginning of a time-series experiment. Our CNN approach reconstructs a 12800X10800 pixels phase image using only ~25 seconds, a 50X speedup compared to the model-based FPM algorithm. In addition, the CNN further reduces the required number of images in each time frame by ~6X. Overall, this significantly improves the imaging throughput by reducing both the acquisition and computational times. The proposed CNN is based on the conditional generative adversarial network (cGAN) framework. Additionally, we also exploit transfer learning so that our pre-trained CNN can be further optimized to image other cell types. Our technique demonstrates a promising deep learning approach to continuously monitor large live-cell populations over an extended time and gather useful spatial and temporal information with sub-cellular resolution.

cs.CV↗

arXiv2018

A Human Mixed Strategy Approach to Deep Reinforcement Learning

In 2015, Google's DeepMind announced an advancement in creating an autonomous agent based on deep reinforcement learning (DRL) that could beat a professional player in a series of 49 Atari games. However, the current manifestation of DRL is still immature, and has significant drawbacks. One of DRL's imperfections is its lack of "exploration" during the training process, especially when working with high-dimensional problems. In this paper, we propose a mixed strategy approach that mimics behaviors of human when interacting with environment, and create a "thinking" agent that allows for more efficient exploration in the DRL training process. The simulation results based on the Breakout game show that our scheme achieves a higher probability of obtaining a maximum score than does the baseline DRL algorithm, i.e., the asynchronous advantage actor-critic method. The proposed scheme therefore can be applied effectively to solving a complicated task in a real-world application.

cs.LG↗

arXiv2017

Deuteration of ammonia with D atoms on oxidized partly ASW covered graphite surface

The deuteration of ammonia by D atoms has been investigated experimentally in the sub-monolayer regime on realistic analogues of interstellar dust grain surfaces. About 0.8 monolayer of solid NH3 was deposited on top of an oxidized graphite surface held at 10 K, partly covered with ASW ice. Ammonia ice is subsequently exposed to D atoms for different exposure times using a differentially pumped beam-line. The deuteration experiments of ammonia were carried out by mass spectroscopy and temperature programmed desorption (TPD) technique. The experimental results showed the formation of three isotopologue ammonia species by direct exothermic H-D substitution surface reactions: NH3+D->NH2D+H, NH2D+D->NHD2+H, and NHD2+D->ND3+H. The formation of the deuterated isotopologues NH2D, NHD2, and ND3 at low surface temperature (10 K) is likely to occur through quantum tunneling process on the oxidized graphite surface. A kinetic model taking into account the diffusion of D atoms on the surface is developed to estimate the width and the hight of the activation energy barriers for the successive deuteration reactions of ammonia species by D atoms. Identical control experiments were performed using CH3OH and D atoms. The deuteration process of solid methanol is ruled by H abstraction and D addition mechanism, and is almost five orders of magnitude faster than ammonia deuteration process.

astro-ph.IM↗

arXiv2017

The Value of Sharing Intermittent Spectrum

Recent initiatives by regulatory agencies to increase spectrum resources available for broadband access include rules for sharing spectrum with high-priority incumbents. We study a model in which wireless Service Providers (SPs) charge for access to their own exclusive-use (licensed) band along with access to an additional shared band. The total, or delivered price in each band is the announced price plus a congestion cost, which depends on the load, or total users normalized by the bandwidth. The shared band is intermittently available with some probability, due to incumbent activity, and when unavailable, any traffic carried on that band must be shifted to licensed bands. The SPs then compete for quantity of users. We show that the value of the shared band depends on the relative sizes of the SPs: large SPs with more bandwidth are better able to absorb the variability caused by intermittency than smaller SPs. However, as the amount of shared spectrum increases, the large SPs may not make use of it. In that scenario shared spectrum creates more value than splitting it among the SPs for exclusive use. We also show that fixing the average amount of available shared bandwidth, increasing the reliability of the band is preferable to increasing the bandwidth.

cs.GT↗