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Segmentation is an integral module in many visual computing applications such as virtual try-on, medical imaging, autonomous driving, and agricultural automation. These applications often involve either widespread consumer use or highly variable environments, both of which can degrade the quality of visual sensor data, whether from a common mobile phone or an expensive satellite imaging camera. In addition to external noises like user difference or weather conditions, internal noises such as variations in camera quality or lens distortion can affect the performance of segmentation models during both development and deployment. In this work, we present an efficient, adaptable, and gradient-free method to enhance the robustness of learning-based segmentation models across training. First, we introduce a novel adaptive sensitivity analysis (ASA) using Kernel Inception Distance (KID) on basis perturbations to benchmark perturbation sensitivity of pre-trained segmentation models. Then, we model the sensitivity curve using the adaptive SA and sample perturbation hyperparameter values accordingly. Finally, we conduct adversarial training with the selected perturbation values and dynamically re-evaluate robustness during online training. Our method, implemented end-to-end with minimal fine-tuning required, consistently outperforms state-of-the-art data augmentation techniques for segmentation. It shows significant improvement in both clean data evaluation and real-world adverse scenario evaluation across various segmentation datasets used in visual computing and computer graphics applications., Comment: 10 pages

We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms. Such a problem consists of two phases: named entity recognition (NER) and attribute value normalization (AVN). However, existing works only focus on the NER phase but neglect equally important AVN. To bridge this gap, this paper proposes a unified query attribute value extraction system in e-commerce search named QUEACO, which involves both two phases. Moreover, by leveraging large-scale weakly-labeled behavior data, we further improve the extraction performance with less supervision cost. Specifically, for the NER phase, QUEACO adopts a novel teacher-student network, where a teacher network that is trained on the strongly-labeled data generates pseudo-labels to refine the weakly-labeled data for training a student network. Meanwhile, the teacher network can be dynamically adapted by the feedback of the student's performance on strongly-labeled data to maximally denoise the noisy supervisions from the weak labels. For the AVN phase, we also leverage the weakly-labeled query-to-attribute behavior data to normalize surface form attribute values from queries into canonical forms from products. Extensive experiments on a real-world large-scale E-commerce dataset demonstrate the effectiveness of QUEACO. © 2021 ACM.

We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms. Such a problem consists of two phases: named entity recognition (NER) and attribute value normalization (AVN). However, existing works only focus on the NER phase but neglect equally important AVN. To bridge this gap, this paper proposes a unified query attribute value extraction system in e-commerce search named QUEACO, which involves both two phases. Moreover, by leveraging large-scale weakly-labeled behavior data, we further improve the extraction performance with less supervision cost. Specifically, for the NER phase, QUEACO adopts a novel teacher-student network, where a teacher network that is trained on the strongly-labeled data generates pseudo-labels to refine the weakly-labeled data for training a student network. Meanwhile, the teacher network can be dynamically adapted by the feedback of the student's performance on strongly-labeled data to maximally denoise the noisy supervisions from the weak labels. For the AVN phase, we also leverage the weakly-labeled query-to-attribute behavior data to normalize surface form attribute values from queries into canonical forms from products. Extensive experiments on a real-world large-scale E-commerce dataset demonstrate the effectiveness of QUEACO. © 2021 ACM.

Quantum physics experiments produce interesting phenomena such as interference or entanglement, which are core properties of numerous future quantum technologies. The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand. We present a deep generative model of quantum optics experiments where a variational autoencoder is trained on a dataset of quantum optics experimental setups. In a series of computational experiments, we investigate the learned representation of our Quantum Optics Variational Auto Encoder (QOVAE) and its internal understanding of the quantum optics world. We demonstrate that the QOVAE learns an interpretable representation of quantum optics experiments and the relationship between experiment structure and entanglement. We show the QOVAE is able to generate novel experiments for highly entangled quantum states with specific distributions that match its training data. The QOVAE can learn to generate specific entangled states and efficiently search the space of experiments that produce highly entangled quantum states. Importantly, we are able to interpret how the QOVAE structures its latent space, finding curious patterns that we can explain in terms of quantum physics. The results demonstrate how we can use and understand the internal representations of deep generative models in a complex scientific domain. The QOVAE and the insights from our investigations can be immediately applied to other physical systems., Comment: Published in Nature Machine Intelligence https://doi.org/10.1038/s42256-022-00493-5

We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms. Such a problem consists of two phases: {named entity recognition (NER)} and {attribute value normalization (AVN)}. However, existing works only focus on the NER phase but neglect equally important AVN. To bridge this gap, this paper proposes a unified query attribute value extraction system in e-commerce search named QUEACO, which involves both two phases. Moreover, by leveraging large-scale weakly-labeled behavior data, we further improve the extraction performance with less supervision cost. Specifically, for the NER phase, QUEACO adopts a novel teacher-student network, where a teacher network that is trained on the strongly-labeled data generates pseudo-labels to refine the weakly-labeled data for training a student network. Meanwhile, the teacher network can be dynamically adapted by the feedback of the student's performance on strongly-labeled data to maximally denoise the noisy supervisions from the weak labels. For the AVN phase, we also leverage the weakly-labeled query-to-attribute behavior data to normalize surface form attribute values from queries into canonical forms from products. Extensive experiments on a real-world large-scale E-commerce dataset demonstrate the effectiveness of QUEACO., Comment: The 30th ACM International Conference on Information and Knowledge Management (CIKM 2021, Applied Research Track)

Nowadays, with many e-commerce platforms conducting global business, e-commerce search systems are required to handle product retrieval under multilingual scenarios. Moreover, comparing with maintaining per-country specific e-commerce search systems, having a universal system across countries can further reduce the operational and computational costs, and facilitate business expansion to new countries. In this paper, we introduce a universal end-to-end multilingual retrieval system, and discuss our learnings and technical details when training and deploying the system to serve billion-scale product retrieval for e-commerce search. In particular, we propose a multilingual graph attention based retrieval network by leveraging recent advances in transformer-based multilingual language models and graph neural network architectures to capture the interactions between search queries and items in e-commerce search. Offline experiments on five countries data show that our algorithm outperforms the state-of-the-art baselines by 35% recall and 25% mAP on average. Moreover, the proposed model shows significant increase of conversion/revenue in online A/B experiments and has been deployed in production for multiple countries., Comment: Accepted by 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)

Singlet fission can split a high energy singlet exciton and generate two lower energy triplet excitons. This process has shown near 200 percent triplet exciton yield. Sensitizing solar cells with singlet fission material, it can potentially increase the power conversion efficiency limit from 29 percent to 35 percent. Singlet fission in the tetracene is known to be efficient, and the energy of the triplet excitons are energetically matched to the silicon bandgap. In this work, we designed an optical measurement with an external magnetic field to determine the efficiencies of triplet exciton transfer from tetracene to silicon. Using this method, we have found that a passivation layer of 8 angstroms of hafnium oxynitride on silicon allows efficient triplet exciton transfer around 133 percent.

Graph neural network have achieved impressive results in predicting molecular properties, but they do not directly account for local and hidden structures in the graph such as functional groups and molecular geometry. At each propagation step, GNNs aggregate only over first order neighbours, ignoring important information contained in subsequent neighbours as well as the relationships between those higher order connections. In this work, we generalize graph neural nets to pass messages and aggregate across higher order paths. This allows for information to propagate over various levels and substructures of the graph. We demonstrate our model on a few tasks in molecular property prediction.

Graph generation is an extremely important task, as graphs are found throughout different areas of science and engineering. In this work, we focus on the modern equivalent of the Erdos-Renyi random graph model: the graph variational autoencoder (GVAE). This model assumes edges and nodes are independent in order to generate entire graphs at a time using a multi-layer perceptron decoder. As a result of these assumptions, GVAE has difficulty matching the training distribution and relies on an expensive graph matching procedure. We improve this class of models by building a message passing neural network into GVAE's encoder and decoder. We demonstrate our model on the specific task of generating small organic molecules

This paper focuses on Bayesian Optimization in combinatorial spaces. In many applications in the natural science. Broad applications include the study of molecules, proteins, DNA, device structures and quantum circuit designs, a on optimization over combinatorial categorical spaces is needed to find optimal or pareto-optimal solutions. However, only a limited amount of methods have been proposed to tackle this problem. Many of them depend on employing Gaussian Process for combinatorial Bayesian Optimizations. Gaussian Processes suffer from scalability issues for large data sizes as their scaling is cubic with respect to the number of data points. This is often impractical for optimizing large search spaces. Here, we introduce a variational Bayesian optimization method that combines variational optimization and continuous relaxations to the optimization of the acquisition function for Bayesian optimization. Critically, this method allows for gradient-based optimization and has the capability of optimizing problems with large data size and data dimensions. We have shown the performance of our method is comparable to state-of-the-art methods while maintaining its scalability advantages. We also applied our method in molecular optimization.

We combine synthetic supramolecular chemistry and materials science to develop novel exciplexes for thermally activated delayed fluorescence. Our approach starts from a bowl-shaped acceptor molecule for which we synthesize tailor-made donors that bind in a lock-and-key fashion. The donor design is guided by extensive density functional theory calculations of three independent donor families. The investigation of a large number of custom-synthesized donors allows us to derive empirical relationships for the prediction of the exciplex emission color. Incorporated within organic light-emitting devices, the lock-and-key exciplexes yield external quantum efficiencies of up to 5.4%, with potentially tunable emission color across the blue and green visible spectrum., Air Force Office of Scientific Research (Grant FA9550-18-1-0341), Department of Energy (Grant DE-FG02-07ER46474), NIH (Grant GM112272)

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018., Cataloged from PDF version of thesis., Includes bibliographical references (pages 107-113)., Our main energy source are burning fossil fuels. Solar energy is a clean, sustainable energy source that does not emit greenhouse gas that causes global warming. In this thesis, singlet fission and triplet-triplet annihilation would be discussed and studied to increase solar cell power conversion efficiency. Singlet fission splits a singlet exciton into two lower energy triplet excitons. This energy down conversion is efficient and have shown near 200% triplet exciton yield. With energy down conversion, thermalization loss in solar cell could be reduced. We have shown singlet fission in tetracene, a suitable down conversion fission material to pair with silicon solar cell, up to 127% of internal quantum yield. To integrate tetracene as the energy down converter for silicon, we have demonstrated triplet exciton transfer from tetracene to silicon with WNx passivation. To show triplet exciton transfer, a spectrally resolved magnetic field effect measurement setup was designed and built. We have also demonstrated a metal-free, solid state optical up conversion system. This is in contrast with most optical up conversion system that uses heavy metal for triplet generation and are limited to solution fabrication based on material restrictions. The novel optical up conversion system provided cheaper material choices as well as better fabrication freedom and potentially longer device operation lifetime., by Tony Chang-Chi Wu., Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018., Cataloged from PDF version of thesis., Includes bibliographical references (pages 107-113)., Our main energy source are burning fossil fuels. Solar energy is a clean, sustainable energy source that does not emit greenhouse gas that causes global warming. In this thesis, singlet fission and triplet-triplet annihilation would be discussed and studied to increase solar cell power conversion efficiency. Singlet fission splits a singlet exciton into two lower energy triplet excitons. This energy down conversion is efficient and have shown near 200% triplet exciton yield. With energy down conversion, thermalization loss in solar cell could be reduced. We have shown singlet fission in tetracene, a suitable down conversion fission material to pair with silicon solar cell, up to 127% of internal quantum yield. To integrate tetracene as the energy down converter for silicon, we have demonstrated triplet exciton transfer from tetracene to silicon with WNx passivation. To show triplet exciton transfer, a spectrally resolved magnetic field effect measurement setup was designed and built. We have also demonstrated a metal-free, solid state optical up conversion system. This is in contrast with most optical up conversion system that uses heavy metal for triplet generation and are limited to solution fabrication based on material restrictions. The novel optical up conversion system provided cheaper material choices as well as better fabrication freedom and potentially longer device operation lifetime., by Tony Chang-Chi Wu., Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018., Cataloged from PDF version of thesis., Includes bibliographical references (pages 107-113)., Our main energy source are burning fossil fuels. Solar energy is a clean, sustainable energy source that does not emit greenhouse gas that causes global warming. In this thesis, singlet fission and triplet-triplet annihilation would be discussed and studied to increase solar cell power conversion efficiency. Singlet fission splits a singlet exciton into two lower energy triplet excitons. This energy down conversion is efficient and have shown near 200% triplet exciton yield. With energy down conversion, thermalization loss in solar cell could be reduced. We have shown singlet fission in tetracene, a suitable down conversion fission material to pair with silicon solar cell, up to 127% of internal quantum yield. To integrate tetracene as the energy down converter for silicon, we have demonstrated triplet exciton transfer from tetracene to silicon with WNx passivation. To show triplet exciton transfer, a spectrally resolved magnetic field effect measurement setup was designed and built. We have also demonstrated a metal-free, solid state optical up conversion system. This is in contrast with most optical up conversion system that uses heavy metal for triplet generation and are limited to solution fabrication based on material restrictions. The novel optical up conversion system provided cheaper material choices as well as better fabrication freedom and potentially longer device operation lifetime., by Tony Chang-Chi Wu., Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018., Cataloged from PDF version of thesis., Includes bibliographical references (pages 107-113)., Our main energy source are burning fossil fuels. Solar energy is a clean, sustainable energy source that does not emit greenhouse gas that causes global warming. In this thesis, singlet fission and triplet-triplet annihilation would be discussed and studied to increase solar cell power conversion efficiency. Singlet fission splits a singlet exciton into two lower energy triplet excitons. This energy down conversion is efficient and have shown near 200% triplet exciton yield. With energy down conversion, thermalization loss in solar cell could be reduced. We have shown singlet fission in tetracene, a suitable down conversion fission material to pair with silicon solar cell, up to 127% of internal quantum yield. To integrate tetracene as the energy down converter for silicon, we have demonstrated triplet exciton transfer from tetracene to silicon with WNx passivation. To show triplet exciton transfer, a spectrally resolved magnetic field effect measurement setup was designed and built. We have also demonstrated a metal-free, solid state optical up conversion system. This is in contrast with most optical up conversion system that uses heavy metal for triplet generation and are limited to solution fabrication based on material restrictions. The novel optical up conversion system provided cheaper material choices as well as better fabrication freedom and potentially longer device operation lifetime., by Tony Chang-Chi Wu., Ph. D.

The ability to upconvert light is useful for a range of applications, from biological imaging to solar cells. But modern technologies have struggled to upconvert incoherent incident light at low intensities. Here, we report solid state photon upconversion employing triplet-triplet exciton annihilation in an organic semiconductor, sensitized by a thermally activated-delayed fluorescence (TADF) dye. Compared to conventional phosphorescent sensitizers, the TADF dye maximizes the wavelength shift in upconversion due to its small singlet-triplet splitting. The efficiency of energy transfer from the TADF dye is 9.1%, and the conversion yield of sensitizer exciton pairs to singlet excitons in the annihilator is 1.1%. Our results demonstrate upconversion in solid state geometries and with non-heavy metal-based sensitizer materials., United States. Department of Energy. Office of Basic Energy Sciences (Award DEFG02- 07ER46474)

One viable solution for continuous reduction in energy-per-operation is to rethink functionality to cope with uncertainty by adopting computational approaches that are inherently robust to uncertainty. It requires a novel look at data representations, associated operations, and circuits, and at materials and substrates that enable them. 3D integrated nanotechnologies combined with novel brain-inspired computational paradigms that support fast learning and fault tolerance could lead the way. Recognizing the very size of the brain's circuits, hyperdimensional (HD) computing can model neural activity patterns with points in a HD space, that is, with hypervectors as large randomly generated patterns. At its very core, HD computing is about manipulating and comparing these patterns inside memory. Emerging nanotechnologies such as carbon nanotube field effect transistors (CNFETs) and resistive RAM (RRAM), and their monolithic 3D integration offer opportunities for hardware implementations of HD computing through tight integration of logic and memory, energy-efficient computation, and unique device characteristics. We experimentally demonstrate and characterize an end-to-end HD computing nanosystem built using monolithic 3D integration of CNFETs and RRAM. With our nanosystem, we experimentally demonstrate classification of 21 languages with measured accuracy of up to 98% on >20,000 sentences (6.4 million characters), training using one text sample (~100,000 characters) per language, and resilient operation (98% accuracy) despite 78% hardware errors in HD representation (outputs stuck at 0 or 1). By exploiting the unique properties of the underlying nanotechnologies, we show that HD computing, when implemented with monolithic 3D integration, can be up to 420X more energy-efficient while using 25X less area compared to traditional silicon CMOS implementations., Comment: 22 pages, 8 figures

Donor–acceptor triptycences, TPA-QNX(CN)2 and TPA-PRZ(CN)2, were synthesized and their emissive properties were studied. They exhibited a blue-green fluorescence with emission lifetimes on the order of a microsecond in cyclohexane at room temperature. The long lifetime emission is quenched by O[subscript 2] and is attributed to thermally activated delayed florescence (TADF). Unimolecular TADF is made possible by the separation and weak coupling due to homoconjugation of the HOMO and LUMO on different arms of the three-dimensional donor–acceptor triptycene. Organic light emitting devices (OLEDs) were fabricated using TPA-QNX(CN)2 and TPA-PRZ(CN)2 as emitters which displayed electroluminescence with efficiencies as high as 9.4% EQE., Samsung (Firm), Japan Society for the Promotion of Science

Discovering new OLED emitters requires many experiments to synthesize candidates and test performance in devices. Large scale computer simulation can greatly speed this search process but the problem remains challenging enough that brute force application of massive computing power is not enough to successfully identify novel structures. We report a successful High Throughput Virtual Screening study that leveraged a range of methods to optimize the search process. The generation of candidate structures was constrained to contain combinatorial explosion. Simulations were tuned to the specific problem and calibrated with experimental results. Experimentalists and theorists actively collaborated such that experimental feedback was regularly utilized to update and shape the computational search. Supervised machine learning methods prioritized candidate structures prior to quantum chemistry simulation to prevent wasting compute on likely poor performers. With this combination of techniques, each multiplying the strength of the search, this effort managed to navigate an area of molecular space and identify hundreds of promising OLED candidate structures. An experimentally validated selection of this set shows emitters with external quantum efficiencies as high as 22%.

We describe the red phosphorescence exhibited by a class of structurally simple benzo[2,1,3]thiadiazoles at room temperature. The photophysical properties of these molecules in deoxygenated cyclohexane, including their absorption spectra, steady-state photoluminescence and excitation spectra, and phosphorescence lifetimes, are presented. Time-dependent density functional theory calculations were carried out to better understand the electronic excited states of these benzo[2,1,3]thiadiazoles and why they are capable of phosphorescence., National Science Foundation (U.S.) (1122374), United States. Dept. of Energy. Office of Basic Energy Sciences (DE-FG02-07ER46474)

Hung-Wei Pan,1,2,* Hsing-Hao Su,3,4,* Chao-Wen Hsu,5,6 Guan-Jin Huang,7 Tony Tong-Lin Wu6,8 1Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, 2Department of Applied Chemistry, National Pingtung University, Pingtung, 3Department of Otorhinolaryngology-Head and Neck Surgery, 4Department of Pharmacy and Graduate Institute of Pharmaceutical Technology, Tajen University, Pingtung, 5Division of Colorectal Surgery, Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, 6Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, 7Department of Pathology, National Chung Kung University Hospital, Tainan, 8Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan *These authors contributed equally to this work Abstract: Prostate cancer is a complex disease that can be relatively harmless or extremely aggressive. Although androgen-deprivation therapy is a commonly used treatment for men with prostate cancer, the adverse effects can be detrimental to patient health and quality of life. Therefore, identifying new target genes for tumor growth will enable the development of novel therapeutic intervention. TPX2 plays a critical role in chromosome segregation machinery during mitosis. Low rates of chromosome missegregation can promote tumor development, whereas higher levels might promote cell death and suppress tumorigenesis. Hence, the strategy of promoting cell death by inducing massive chromosome missegregation has been a therapeutic application for selectively eliminating highly proliferating tumor cells. RNAi was used for TPX2 protein expression knockdown, and a clonogenic assay, immunostaining, double thymidine block, image-cytometry analysis, and tumor spheroid assay were used to analyze the role of TPX2 in tumor cell growth, cell cycle progression, multinuclearity, ploidy, and tumorigenicity, respectively; finally, Western blotting was used to analyze anticancer mechanisms

Donor–acceptor triptycences, TPA-QNX(CN)2 and TPA-PRZ(CN)2, were synthesized and their emissive properties were studied. They exhibited a blue-green fluorescence with emission lifetimes on the order of a microsecond in cyclohexane at room temperature. The long lifetime emission is quenched by O[subscript 2] and is attributed to thermally activated delayed florescence (TADF). Unimolecular TADF is made possible by the separation and weak coupling due to homoconjugation of the HOMO and LUMO on different arms of the three-dimensional donor–acceptor triptycene. Organic light emitting devices (OLEDs) were fabricated using TPA-QNX(CN)2 and TPA-PRZ(CN)2 as emitters which displayed electroluminescence with efficiencies as high as 9.4% EQE., Samsung (Firm), Japan Society for the Promotion of Science

Singlet exciton fission splits one singlet exciton into two triplet excitons. Using a joint analysis of photocurrent and fluorescence modulation under a magnetic field, we determine that the triplet yield within optimized tetracene organic photovoltaic devices is 153% ± 5% for a tetracene film thickness of 20 nm. The corresponding internal quantum efficiency is 127% ± 18%. These results are used to prove the effectiveness of a simplified triplet yield measurement that relies only on the magnetic field modulation of fluorescence. Despite its relatively slow rate of singlet fission, the measured triplet yields confirm that tetracene is presently the best candidate for use with silicon solar cells., United States. Dept. of Energy. Office of Basic Energy Sciences (Award DE-SC0001088), National Science Foundation (U.S.). Graduate Research Fellowship (Grant 1122374)

We present and evaluate a large-scale malware detection system integrating machine learning with expert reviewers, treating reviewers as a limited labeling resource. We demonstrate that even in small numbers, reviewers can vastly improve the system's ability to keep pace with evolving threats. We conduct our evaluation on a sample of VirusTotal submissions spanning 2.5 years and containing 1.1 million binaries with 778GB of raw feature data. Without reviewer assistance, we achieve 72% detection at a 0.5% false positive rate, performing comparable to the best vendors on VirusTotal. Given a budget of 80 accurate reviews daily, we improve detection to 89% and are able to detect 42% of malicious binaries undetected upon initial submission to VirusTotal. Additionally, we identify a previously unnoticed temporal inconsistency in the labeling of training datasets. We compare the impact of training labels obtained at the same time training data is first seen with training labels obtained months later. We find that using training labels obtained well after samples appear, and thus unavailable in practice for current training data, inflates measured detection by almost 20 percentage points. We release our cluster-based implementation, as well as a list of all hashes in our evaluation and 3% of our entire dataset., Comment: 20 papers, 11 figures, accepted at the 13th Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA 2016)

Cloud computing has become increasingly popular. Many options of cloud deployments are available. Testing cloud performance would enable us to choose a cloud deployment based on the requirements. In this paper, we present an innovative process, implemented in software, to allow us to assess the quality of the cloud performance data. The process combines performance data from multiple machines, spanning across user experience data, workload performance metrics, and readily available system performance data. Furthermore, we discuss the major challenges of bringing raw data into tidy data formats in order to enable subsequent analysis, and describe how our process has several layers of assessment to validate the quality of the data processing procedure. We present a case study to demonstrate the effectiveness of our proposed process, and conclude our paper with several future research directions worth investigating.

There are increasing concerns about possible malicious modifications of integrated circuits (ICs) used in critical applications. Such attacks are often referred to as hardware Trojans. While many techniques focus on hardware Trojan detection during IC testing, it is still possible for attacks to go undetected. Using a combination of new design techniques and new memory technologies, we present a new approach that detects a wide variety of hardware Trojans during IC testing and also during system operation in the field. Our approach can also prevent a wide variety of attacks during synthesis, place-and-route, and fabrication of ICs. It can be applied to any digital system, and can be tuned for both traditional and split-manufacturing methods. We demonstrate its applicability for both ASICs and FPGAs. Using fabricated test chips with Trojan emulation capabilities and also using simulations, we demonstrate: 1. The area and power costs of our approach can range between 7.4-165% and 0.07-60%, respectively, depending on the design and the attacks targeted; 2. The speed impact can be minimal (close to 0%); 3. Our approach can detect 99.998% of Trojans (emulated using test chips) that do not require detailed knowledge of the design being attacked; 4. Our approach can prevent 99.98% of specific attacks (simulated) that utilize detailed knowledge of the design being attacked (e.g., through reverse-engineering). 5. Our approach never produces any false positives, i.e., it does not report attacks when the IC operates correctly., Comment: 17 pages, 23 figures. Extended version of paper to appear in IEEE Trans. on CAD

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013., Cataloged from PDF version of thesis., Includes bibliographical references (p. 39-40)., Using singlet fission in a photovoltaic cell, the theoretical energy conversion efficiency limit is larger than the Shockley-Queisser limit due to two excitons produced with one incident photon. In a singlet fission material, an absorbed photon excites a singlet exciton and then split into two triplet excitons with energy around half of the singlet exciton. Tetracene is a potential candidate for singlet fission photovoltaic cells. Tetracene has triplet exciton energy around 1.2 electron volts (eV), which matches with silicon band gap (1.1 eV). In this study, the quantum efficiency of tetracene devices are measured. Secondly, we will build a kinetic model to calculate the triplet yield rate from magnetic field effect. By modeling and experimental data, we will show that tetracene is an efficient fission material., by Tony Chang-Chi Wu., S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013., Cataloged from PDF version of thesis., Includes bibliographical references (p. 39-40)., Using singlet fission in a photovoltaic cell, the theoretical energy conversion efficiency limit is larger than the Shockley-Queisser limit due to two excitons produced with one incident photon. In a singlet fission material, an absorbed photon excites a singlet exciton and then split into two triplet excitons with energy around half of the singlet exciton. Tetracene is a potential candidate for singlet fission photovoltaic cells. Tetracene has triplet exciton energy around 1.2 electron volts (eV), which matches with silicon band gap (1.1 eV). In this study, the quantum efficiency of tetracene devices are measured. Secondly, we will build a kinetic model to calculate the triplet yield rate from magnetic field effect. By modeling and experimental data, we will show that tetracene is an efficient fission material., by Tony Chang-Chi Wu., S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013., Cataloged from PDF version of thesis., Includes bibliographical references (p. 39-40)., Using singlet fission in a photovoltaic cell, the theoretical energy conversion efficiency limit is larger than the Shockley-Queisser limit due to two excitons produced with one incident photon. In a singlet fission material, an absorbed photon excites a singlet exciton and then split into two triplet excitons with energy around half of the singlet exciton. Tetracene is a potential candidate for singlet fission photovoltaic cells. Tetracene has triplet exciton energy around 1.2 electron volts (eV), which matches with silicon band gap (1.1 eV). In this study, the quantum efficiency of tetracene devices are measured. Secondly, we will build a kinetic model to calculate the triplet yield rate from magnetic field effect. By modeling and experimental data, we will show that tetracene is an efficient fission material., by Tony Chang-Chi Wu., S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013., Cataloged from PDF version of thesis., Includes bibliographical references (p. 39-40)., Using singlet fission in a photovoltaic cell, the theoretical energy conversion efficiency limit is larger than the Shockley-Queisser limit due to two excitons produced with one incident photon. In a singlet fission material, an absorbed photon excites a singlet exciton and then split into two triplet excitons with energy around half of the singlet exciton. Tetracene is a potential candidate for singlet fission photovoltaic cells. Tetracene has triplet exciton energy around 1.2 electron volts (eV), which matches with silicon band gap (1.1 eV). In this study, the quantum efficiency of tetracene devices are measured. Secondly, we will build a kinetic model to calculate the triplet yield rate from magnetic field effect. By modeling and experimental data, we will show that tetracene is an efficient fission material., by Tony Chang-Chi Wu., S.M.

Hung-Wei Yang,1,* Mu-Yi Hua,1,* Kun-Ju Lin,2,* Shiaw-Pyng Wey,3 Rung-Ywan Tsai,4 Siao-Yun Wu,5 Yi-Ching Lu,5 Hao-Li Liu,6 Tony Wu,7 Yunn-Hwa Ma5 1Chang Gung Molecular Medicine Research Center, Department of Chemical and Materials Engineering, 2Molecular Imaging Center, Department of Nuclear Medicine, Chang Gung Memorial Hospital, Kuei-Shan, Tao-Yuan, Taiwan, Republic of China; 3Department of Medical Imaging and Radiological Sciences, 4Electronics and Optoelectronics Research Laboratories, Industrial Technology Research Institute, Hsin-chu, Taiwan, Republic of China; 5Department of Physiology and Pharmacology and Healthy Aging Research Center, 6Department of Electrical Engineering, Chang Gung University, Kuei-Shan, Tao-Yuan, Taiwan, Republic of China; 7Department of Neurology, Chang Gung University College of Medicine and Memorial Hospital, Tao-Yuan, Taiwan, Republic of China*These authors contributed equally to this workAbstract: Low-toxicity magnetic nanocarriers (MNCs) composed of a shell of poly [aniline-co-N-(1-one-butyric acid) aniline] over a Fe3O4 magnetic nanoparticle core were developed to carry recombinant tissue plasminogen activator (rtPA) in MNC-rtPA for targeted thrombolysis. With an average diameter of 14.8 nm, the MNCs exerted superparamagnetic properties. Up to 276 µg of active rtPA was immobilized per mg of MNCs, and the stability of the immobilized rtPA was greatly improved during storage at 4°C and 25°C. In vitro thrombolysis testing with a tubing system demonstrated that magnet-guided MNC-rtPA showed significantly improved thrombolysis compared with free rtPA and reduced the clot lysis time from 39.2 ± 3.2 minutes to 10.8 ± 4.2 minutes. In addition, magnet-guided MNC-rtPA at 20% of the regular rtPA dose restored blood flow within 15–25 minutes of treatment in a rat embolism model without triggering hematological toxicity. In conclusion, this improved system is based on magnetic targeting

Hung-Wei Yang,1,* Mu-Yi Hua,1,* Kun-Ju Lin,2,* Shiaw-Pyng Wey,3 Rung-Ywan Tsai,4 Siao-Yun Wu,5 Yi-Ching Lu,5 Hao-Li Liu,6 Tony Wu,7 Yunn-Hwa Ma5 1Chang Gung Molecular Medicine Research Center, Department of Chemical and Materials Engineering, 2Molecular Imaging Center, Department of Nuclear Medicine, Chang Gung Memorial Hospital, Kuei-Shan, Tao-Yuan, Taiwan, Republic of China; 3Department of Medical Imaging and Radiological Sciences, 4Electronics and Optoelectronics Research Laboratories, Industrial Technology Research Institute, Hsin-chu, Taiwan, Republic of China; 5Department of Physiology and Pharmacology and Healthy Aging Research Center, 6Department of Electrical Engineering, Chang Gung University, Kuei-Shan, Tao-Yuan, Taiwan, Republic of China; 7Department of Neurology, Chang Gung University College of Medicine and Memorial Hospital, Tao-Yuan, Taiwan, Republic of China*These authors contributed equally to this workAbstract: Low-toxicity magnetic nanocarriers (MNCs) composed of a shell of poly [aniline-co-N-(1-one-butyric acid) aniline] over a Fe3O4 magnetic nanoparticle core were developed to carry recombinant tissue plasminogen activator (rtPA) in MNC-rtPA for targeted thrombolysis. With an average diameter of 14.8 nm, the MNCs exerted superparamagnetic properties. Up to 276 µg of active rtPA was immobilized per mg of MNCs, and the stability of the immobilized rtPA was greatly improved during storage at 4°C and 25°C. In vitro thrombolysis testing with a tubing system demonstrated that magnet-guided MNC-rtPA showed significantly improved thrombolysis compared with free rtPA and reduced the clot lysis time from 39.2 ± 3.2 minutes to 10.8 ± 4.2 minutes. In addition, magnet-guided MNC-rtPA at 20% of the regular rtPA dose restored blood flow within 15–25 minutes of treatment in a rat embolism model without triggering hematological toxicity. In conclusion, this improved system is based on magnetic targeting