Browsing by Author "Lee, Chia-Jung"

Now showing 1 - 14 of 14

A Robust and Flexible Access Control Scheme for Cloud-IoT Paradigm with Application to Remote Mobile Medical Monitoring
(2015)
Ren, Wei
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Ren, Yi
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Wu, Mu-En
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Lee, Chia-Jung
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資訊工程學系
Cloud computing paradigm is becoming very popular these days. However, it does not include wireless sensors and mobile phones which are needed to enable new emerging applications such as remote home medical monitoring. Therefore, a combined Cloud-Internet of Things (IoT) paradigm provides scalable on-demand data storage and resilient computation power at the cloud side as well as anytime, anywhere health data monitoring at the IoT side. As both the privacy of personal medical data and flexible data access should be provided, the data in the Cloud are always encrypted and access control must be operated upon encrypted data together with being fine-grained to support diverse accessibility. Since a plain combination of encryption before access control is not robust and flexible, we propose a scheme with tailored design. The scheme makes use of cipher-policy attributes based encryption to empower robustness and flexibility. The scheme describes a general framework to solve the secure requirements, and leaves the flexibility of concrete constructions intentionally.
Alternating Runs of Geometrically Distributed Random Variables
(2011-05-01)
Lee, Chia-Jung
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Tsai, Shi-Chun
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資訊工程學系
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Department of Computer Science
Run statistics about a sequence of independent geometrically distributed random variables has attracted some attention recently in many areas such as applied probability, reliability, statistical process control, and computer science. In this paper, we first study the mean and variance of the number of alternating runs in a sequence of independent geometrically distributed random variables. Then, using the relation between the model of geometrically distributed random variables and the model of random permutation, we can obtain the variance in a random permutation, which is difficult to derive directly. Moreover, using the central limit theorem for dependent random variables, we can obtain the distribution of the number of alternating runs in a random permutation.
Decoding permutation arrays with ternary vectors
(2011-10-01)
Lee, Chia-Jung
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Lin, Te-Tsung
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Shieh, Min-Zheng
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Tsai, Shi-Chun
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Wu, Hsin-Lung
We give an explicit decoding scheme for the permutation arrays under Hamming distance metric, where the encoding is constructed via a distance-preserving mapping from ternary vectors to permutations (3-DPM).
Deterministic extractors for independent-symbol sources
(2006)
Lee, Chia-Jung
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Lu, Chi-Jen
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Tsai, Shi-Chun
;
資訊工程學系
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Department of Computer Science
In this paper, we consider the task of deterministically extracting randomness from sources consisting of a sequence of n independent symbols from {0, 1}(d). The only randomness guarantee on such a source is that the whole source has min-entropy k. We give an explicit deterministic extractor which can extract Omega(log k-log d-log log(1/epsilon)) bits with error epsilon, for any n, d, k is an element of N and epsilon G (0, 1). For sources with a larger min-entropy, we can extract even more randomness. When k >= n(1/2+gamma) for any constant gamma is an element of (0, 1/2), we can extract m = k - O(d log(1/epsilon)) bits with any error epsilon >= 2(-Omega)(n(gamma)). When k >= log(c)n, for some constant c > 0, we can extract m = k - d(1/epsilon)(O(1)) bits with any error epsilon >= k(-Omega(1)). Our results generalize those of Kamp & Zuckerman and Gabizon et al. which only work for bit-fixing sources (with d = I and each bit of the source being either fixed or perfectly random). Moreover, we show the existence of a non-explicit deterministic extractor which can extract m = k-O(log(1/epsilon)) bits whenever k = w(d+log(n/epsilon)). Finally, we show that even to extract from bit-fixing sources, any extractor, seeded or not, must suffer an entropy loss k-m = Q (log (1/epsilon)). This generalizes a lower bound of Radhakrishnan & Ta-Shma with respect to general sources.
Deterministic Extractors for Independent-Symbol Sources
(2010-12-01)
Lee, Chia-Jung
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Lu, Chi-Jen
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Tsai, Shi-Chun
;
資訊工程學系
;
Department of Computer Science
In this paper, we consider the task of deterministically extracting randomness from sources consisting of a sequence of n independent symbols from {0, 1}(d). The only randomness guarantee on such a source is that the whole source has min-entropy k. We give an explicit deterministic extractor which extract Omega(log k - log log(1/epsilon)) bits with error epsilon, for any, n, d, k is an element of N and epsilon is an element of (0, 1). For sources with a larger min-entropy, we can extract even more randomness. When k >= n(1/2+gamma), for any constant gamma is an element of (1, 1/2), we can extract m = k - O(d log(1/epsilon)) bits with any error epsilon >= 2(-Omega(n gamma)). When k >= log(c) n, for some constant c > 0 , we can extract m = k - (1/epsilon)(O(1)) bits with any error epsilon >= k(-Omega(1)). Our results generalize those of Kamp and Zuckerman and Gabizon et al. which only work for bit-fixing sources (with d = 1 and each bit of the source being either fixed or perfectly random). Moreover, we show the existence of a nonexplicit deterministic extractor which can extract m = k - O(log(1/epsilon)) bits whenever k = omega(d + log(n/epsilon)). Finally, we show that even to extract from bit-fixing sources, any extractor, seeded or not, must suffer an entropy loss k - m = Omega(log(1/epsilon)). This generalizes a lower bound of Radhakrishnan and Ta-Shma on extracting from general sources.
Development of an inflammatory tissue-selective chimeric TNF receptor
(2019-01-01)
Lee, Chia-Jung
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Wang, Chao-Ching
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Chen, Michael
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Chuang, Kuo-Hsiang
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Cheng, Tian-Lu
Background: Inhibiting TNF-alpha is an effective therapy for inflammatory diseases such as rheumatoid arthritis. However, systemic, nondiscriminatory neutralization of TNF-alpha is associated with considerable adverse effects. Methods: Here, we developed a trimeric chimeric TNF receptor by linking an N-terminal mouse Acrp30 trimerization domain and an MMP-2/9 substrate sequence to the mouse extracellular domain of TNF receptor 2 followed by a C-terminal mouse tetranectin coiled-coil domain (mouse Acrp-MMP-TNFR-Tn). Results: Here, we show that the Acrp30 trimerization domain inhibited the binding activity of TNFR, possibly by closing the binding site of the trimeric receptor. Cleavage of the substrate sequence by MMP-9, an enzyme highly expressed in inflammatory sites, restored the binding activity of the mouse TNF receptor. We also constructed a recombinant human chimeric TNF receptor (human Acrp-MMP-TNFR-Tn) in which an MMP-13 substrate sequence was used to link the human Acrp and the human TNF receptor 2. Human Acrp-MMP-TNFR-Tn showed reduced binding activity, and MMP-13 digestion recovered its binding activity with TNF-alpha. Conclusion: Acrp-masked chimeric TNF receptors may be able to be used for inflammatory tissue-selective neutralization of TNF-alpha to reduce the adverse effects associated with systemic neutralization of TNF-alpha.
Elucidating the Physical Property of the InGaN Nanorod Light-Emitting Diode: Large Tunneling Effect
(2011-07-01)
Lee, Ya-Ju
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Lee, Chia-Jung
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Chen, Chih-Hao
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Lu, Tien-Chang
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Kuo, Hao-Chung
The current-voltage (I-V) characteristics of the InGaN nanorod light-emitting diode (LED) are evaluated using nanoprobes installed on a field-emission scanning electron microscope. The saturated current of the InGaN nanorod LED (I(O) = 2 x 10(-9) A) is found to be orders of magnitude higher than those obtained by downscaling a conventional InGaN LED to a chip size of 300 x 300 mu m(2) (I(O1) = 1 x 10(-25) A; I(O2) = 1 x 10(-14) A). This observation is explained by the fact that the nanorod LED is associated with enhanced tunneling of injected carriers and, therefore, reduction of the defect-assisted leakage current and the diffusion-recombination process that normally occurs in InGaN LEDs.
Extracting Computational Entropy and Learning Noisy Linear Functions
(2009)
Lee, Chia-Jung
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Lu, Chi-Jen
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Tsai, Shi-Chun
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資訊工程學系
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Department of Computer Science
We study the task of deterministically extracting randomness from sources containing computational entropy. The sources we consider have the form of a conditional distribution (f(X)vertical bar X), for some function f and some distribution X, and we say that such a source has computational min-entropy k if any circuit of size 2(k) can only predict f(x) correctly with probability at most 2(-k) given input x sampled from X. We first show that it is impossible to have a seedless extractor to extract from one single source of this kind. Then we show that it becomes possible if we are allowed a seed which is weakly random (instead of perfectly random) but contains some statistical min-entropy, or even a seed which is not random at all but contains some computational min-entropy. This can be seen as a step toward extending the study of multi-source extractors from the traditional, statistical setting to a computational setting. We reduce the task of constructing such extractors to a problem in learning theory: learning linear functions under arbitrary distribution with adversarial noise. For this problem, we provide a learning algorithm, which may have interest of its own.
Extracting Computational Entropy and Learning Noisy Linear Functions
(2011-08-01)
Lee, Chia-Jung
;
Lu, Chi-Jen
;
Tsai, Shi-Chun
;
資訊工程學系
;
Department of Computer Science
We study the task of deterministically extracting randomness from sources containing computational entropy. The sources we consider have the form of a conditional distribution (f(X)vertical bar X), for some function f and some distribution X, and we say that such a source has computational min-entropy if any circuit of size 2(k) can only predict f(x) correctly with probability at most 2(-k) given input sampled from X. We first show that it is impossible to have a seedless extractor to extract from one single source of this kind. Then we show that it becomes possible if we are allowed a seed which is weakly random (instead of perfectly random) but contains some statistical min-entropy, or even a seed which is not random at all but contains some computational min-entropy. This can be seen as a step toward extending the study of multisource extractors from the traditional, statistical setting to a computational setting. We reduce the task of constructing such extractors to a problem in computational learning theory: learning linear functions under arbitrary distribution with adversarial noise, and we provide a learning algorithm for this problem. In fact, this problem is a well-recognized one in computational learning theory and variants of this problem have been studied intensively before. Thus, in addition to its application to extractors, our learning algorithm also has independent interest of its own, and it can be considered as the main technical contribution of this paper.
In Situ Activation of Pituitary-Infiltrating T Lymphocytes in Autoimmune Hypophysitis
(2017-03-06)
Lin, Han-Huei
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Gutenberg, Angelika
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Chen, Tzu-Yu
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Tsai, Nu-Man
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Lee, Chia-Jung
Autoimmune hypophysitis (AH) is a chronic inflammatory disease characterized by infiltration of T and B lymphocytes in the pituitary gland. The mechanisms through which infiltrating lymphocytes cause disease remain unknown. Using a mouse model of AH we assessed whether T lymphocytes undergo activation in the pituitary gland. Infiltrating T cells co-localized with dendritic cells in the pituitary and produced increased levels of interferon-gamma and interleukin-17 upon stimulation in vitro. Assessing proliferation of CD3- and B220-postive lymphocytes by double immunohistochemistry (PCNA-staining) and flow cytometry (BrdU incorporation) revealed that a discrete proportion of infiltrating T cells and B cells underwent proliferation within the pituitary parenchyma. This proliferation persisted into the late disease stage (day 56 post-immunization), indicating the presence of a continuous generation of autoreactive T and B cells within the pituitary gland. T cell proliferation in the pituitary was confirmed in patients affected by autoimmune hypophysitis. In conclusion, we show that pituitary-infiltrating lymphocytes proliferate in situ during AH, providing a previously unknown pathogenic mechanism and new avenues for treatment.
miRTarBase: a database curates experimentally validated microRNA-target interactions
(2011-01-01)
Hsu, Sheng-Da
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Lin, Feng-Mao
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Wu, Wei-Yun
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Liang, Chao
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Huang, Wei-Chih
MicroRNAs (miRNAs), i.e. small non-coding RNA molecules (similar to 22 nt), can bind to one or more target sites on a gene transcript to negatively regulate protein expression, subsequently controlling many cellular mechanisms. A current and curated collection of miRNA-target interactions (MTIs) with experimental support is essential to thoroughly elucidating miRNA functions under different conditions and in different species. As a database, miRTarBase has accumulated more than 3500 MTIs by manually surveying pertinent literature after data mining of the text systematically to filter research articles related to functional studies of miRNAs. Generally, the collected MTIs are validated experimentally by reporter assays, western blot, or microarray experiments with overexpression or knockdown of miRNAs. miRTarBase curates 3576 experimentally verified MTIs between 657 miRNAs and 2297 target genes among 17 species. miRTarBase contains the largest amount of validated MTIs by comparing with other similar, previously developed databases. The MTIs collected in the miRTarBase can also provide a large amount of positive samples to develop computational methods capable of identifying miRNA-target interactions. miRTarBase is now available on http://miRTarBase.mbc.nctu.edu.tw/, and is updated frequently by continuously surveying research articles.
Online Prediction Problems with Variation
(2014-01-01)
Lee, Chia-Jung
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Tsai, Shi-Chun
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Yang, Ming-Chuan
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資訊工程學系
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Department of Computer Science
We study the prediction with expert advice problem, where in each round, the player selects one of N actions and incurs the corresponding loss according to an N-dimensional linear loss vector, and aim to minimize the regret. In this paper, we consider a new measure of the loss functions, which we call L-infinity-variation. Consider the loss functions with small L-infinity-variation, if the player is allowed to have some information related to the variation in each round, we can obtain an online bandit algorithm for the problem without using the self-concordance methodology, which conditionally answers an open problem in [8]. Another related problem is the combinatorial prediction game, in which the set of actions is a subset of {0, 1}(d), and the loss function is in [-1, 1](d). We provide an online algorithm in the semi-bandit setting when the loss functions have small L-infinity-variation.
Restrictions of Nondegenerate Boolean Functions and Degree Lower Bounds over Different Rings
(2015)
Lee, Chia-Jung
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Lokam, Satyanarayana V.
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Tsai, Shi-Chun
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Yang, Ming-Chuan
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資訊工程學系
A Boolean function f : {0; 1}(n) -> {0, 1} is called nondegenerate if f depends on all its n variables. We show that, for any nondegenerate function f, there exists a variable x(i) such that at least one of the restrictions f vertical bar(xi) = 0 or f vertical bar(xi) = 1 must depend on all the remaining n - 1 variables. We also consider lower bounds on the degrees of polynomials representing a Boolean function over different rings. Let d (q)(f) be the degree of the unique) polynomial over the ring Z(q) exactly representing f. For distinct primes p(i) let m = Pi(r)(i=1) p(i)(ei). Then, we show that any nondegenerate symmetric Boolean function f must have m . d(p1)(e1) (f)...d(pr)(er)(f) > n. We use the existence of nondegenerate subfunctions to prove degree lower bounds on random functions. Specifically, we show that m .d(p)(e1) (f)...d(pr)(er) (f) > l g n - 1 holds for almost all f when f is chosen uniformly at random from all n - variate Boolean functions. Our proof uses the second moment method to show that a random f must almost always contain a nondegenerate symmetric subfunction on at least l g n - 1 variables. It follows that an n-variate nondegenerate symmetric Boolean function can have degree o(root n) over at most one finite field and that almost all f can have degree o(root lg n) over at most one finite field.
Stable Temperature Characteristics and Suppression of Efficiency Droop in InGaN Green Light-Emitting Diodes Using Pre-TMIn Flow Treatment
(2010-09-01)
Lee, Ya-Ju
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Chen, Yi-Ching
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Lee, Chia-Jung
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Cheng, Chun-Mao
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Chen, Shih-Wei
We present experimental results on the improved performance and high stable temperature characteristics of the InGaN green light-emitting diode (LED) with pre-trimethlyindium (pre-TMIn) flow treatment. By using pre-TMIn flow treatment, a relatively large radiative coefficient (B = 3.34 x 10(-11) cm(3).s(-1))corresponding to a 9.2% enhancement in the internal quantum efficiency, as well as a significant reduction of leakage paths for injected carriers, was obtained. Most important, the pre-TMIn flow treatment evidently reduces the dependence of the external quantum efficiency on temperature and efficiency droop of green LEDs. The improvement is thought to be attributable to the preferential formation of In-rich dots upon pre-TMIn flow treatment, which effectively suppresses the trapping of excitons by threading dislocations and the overflowing of injected carriers outside the active regions at elevated temperatures.