143 research outputs found

    On determinism versus nondeterminism for restarting automata

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    AbstractA restarting automaton processes a given word by executing a sequence of local simplifications until a simple word is obtained that the automaton then accepts. Such a computation is expressed as a sequence of cycles. A nondeterministic restarting automaton M is called correctness preserving, if, for each cycle uโŠขMcv, the string v belongs to the characteristic language LC(M) of M, if the string u does. Our first result states that for each type of restarting automaton Xโˆˆ{R,RW,RWW,RL,RLW,RLWW}, if M is a nondeterministic X-automaton that is correctness preserving, then there exists a deterministic X-automaton M1 such that the characteristic languages LC(M1) and LC(M) coincide. When a restarting automaton M executes a cycle that transforms a string from the language LC(M) into a string not belonging to LC(M), then this can be interpreted as an error of M. By counting the number of cycles it may take M to detect this error, we obtain a measure for the influence that errors have on computations. Accordingly, this measure is called error detection distance. It turns out, however, that an X-automaton with bounded error detection distance is equivalent to a correctness preserving X-automaton, and therewith to a deterministic X-automaton. This means that nondeterminism increases the expressive power of X-automata only in combination with an unbounded error detection distance

    26. Theorietag Automaten und Formale Sprachen 23. Jahrestagung Logik in der Informatik: Tagungsband

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    Der Theorietag ist die Jahrestagung der Fachgruppe Automaten und Formale Sprachen der Gesellschaft fรผr Informatik und fand erstmals 1991 in Magdeburg statt. Seit dem Jahr 1996 wird der Theorietag von einem eintรคgigen Workshop mit eingeladenen Vortrรคgen begleitet. Die Jahrestagung der Fachgruppe Logik in der Informatik der Gesellschaft fรผr Informatik fand erstmals 1993 in Leipzig statt. Im Laufe beider Jahrestagungen finden auch die jรคhrliche Fachgruppensitzungen statt. In diesem Jahr wird der Theorietag der Fachgruppe Automaten und Formale Sprachen erstmalig zusammen mit der Jahrestagung der Fachgruppe Logik in der Informatik abgehalten. Organisiert wurde die gemeinsame Veranstaltung von der Arbeitsgruppe Zuverlรคssige Systeme des Instituts fรผr Informatik an der Christian-Albrechts-Universitรคt Kiel vom 4. bis 7. Oktober im Tagungshotel Tannenfelde bei Neumรผnster. Wรคhrend des Treโ†ตens wird ein Workshop fรผr alle Interessierten statt finden. In Tannenfelde werden โ€ข Christoph Lรถding (Aachen) โ€ข Tomรกs Masopust (Dresden) โ€ข Henning Schnoor (Kiel) โ€ข Nicole Schweikardt (Berlin) โ€ข Georg Zetzsche (Paris) eingeladene Vortrรคge zu ihrer aktuellen Arbeit halten. Darรผber hinaus werden 26 Vortrรคge von Teilnehmern und Teilnehmerinnen gehalten, 17 auf dem Theorietag Automaten und formale Sprachen und neun auf der Jahrestagung Logik in der Informatik. Der vorliegende Band enthรคlt Kurzfassungen aller Beitrรคge. Wir danken der Gesellschaft fรผr Informatik, der Christian-Albrechts-Universitรคt zu Kiel und dem Tagungshotel Tannenfelde fรผr die Unterstรผtzung dieses Theorietags. Ein besonderer Dank geht an das Organisationsteam: Maike Bradler, Philipp Sieweck, Joel Day. Kiel, Oktober 2016 Florin Manea, Dirk Nowotka und Thomas Wilk

    For differential equations with r parameters, 2r+1 experiments are enough for identification

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    Given a set of differential equations whose description involves unknown parameters, such as reaction constants in chemical kinetics, and supposing that one may at any time measure the values of some of the variables and possibly apply external inputs to help excite the system, how many experiments are sufficient in order to obtain all the information that is potentially available about the parameters? This paper shows that the best possible answer (assuming exact measurements) is 2r+1 experiments, where r is the number of parameters.Comment: This is a minor revision of the previously submitted report; a couple of typos have been fixed, and some comments and two new references have been added. Please see http://www.math.rutgers.edu/~sontag for related wor

    ๊ฐ•์ธํ•œ ๋Œ€ํ™”ํ˜• ์˜์ƒ ๋ถ„ํ•  ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์œ„ํ•œ ์‹œ๋“œ ์ •๋ณด ํ™•์žฅ ๊ธฐ๋ฒ•์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021. 2. ์ด๊ฒฝ๋ฌด.Segmentation of an area corresponding to a desired object in an image is essential to computer vision problems. This is because most algorithms are performed in semantic units when interpreting or analyzing images. However, segmenting the desired object from a given image is an ambiguous issue. The target object varies depending on user and purpose. To solve this problem, an interactive segmentation technique has been proposed. In this approach, segmentation was performed in the desired direction according to interaction with the user. In this case, seed information provided by the user plays an important role. If the seed provided by a user contain abundant information, the accuracy of segmentation increases. However, providing rich seed information places much burden on the users. Therefore, the main goal of the present study was to obtain satisfactory segmentation results using simple seed information. We primarily focused on converting the provided sparse seed information to a rich state so that accurate segmentation results can be derived. To this end, a minimum user input was taken and enriched it through various seed enrichment techniques. A total of three interactive segmentation techniques was proposed based on: (1) Seed Expansion, (2) Seed Generation, (3) Seed Attention. Our seed enriching type comprised expansion of area around a seed, generation of new seed in a new position, and attention to semantic information. First, in seed expansion, we expanded the scope of the seed. We integrated reliable pixels around the initial seed into the seed set through an expansion step composed of two stages. Through the extended seed covering a wider area than the initial seed, the seed's scarcity and imbalance problems was resolved. Next, in seed generation, we created a seed at a new point, but not around the seed. We trained the system by imitating the user behavior through providing a new seed point in the erroneous region. By learning the user's intention, our model could e ciently create a new seed point. The generated seed helped segmentation and could be used as additional information for weakly supervised learning. Finally, through seed attention, we put semantic information in the seed. Unlike the previous models, we integrated both the segmentation process and seed enrichment process. We reinforced the seed information by adding semantic information to the seed instead of spatial expansion. The seed information was enriched through mutual attention with feature maps generated during the segmentation process. The proposed models show superiority compared to the existing techniques through various experiments. To note, even with sparse seed information, our proposed seed enrichment technique gave by far more accurate segmentation results than the other existing methods.์˜์ƒ์—์„œ ์›ํ•˜๋Š” ๋ฌผ์ฒด ์˜์—ญ์„ ์ž˜๋ผ๋‚ด๋Š” ๊ฒƒ์€ ์ปดํ“จํ„ฐ ๋น„์ „ ๋ฌธ์ œ์—์„œ ํ•„์ˆ˜์ ์ธ ์š”์†Œ์ด๋‹ค. ์˜์ƒ์„ ํ•ด์„ํ•˜๊ฑฐ๋‚˜ ๋ถ„์„ํ•  ๋•Œ, ๋Œ€๋ถ€๋ถ„์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค์ด ์˜๋ฏธ๋ก ์ ์ธ ๋‹จ์œ„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋™์ž‘ํ•˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์˜์ƒ์—์„œ ๋ฌผ์ฒด ์˜์—ญ์„ ๋ถ„ํ• ํ•˜๋Š” ๊ฒƒ์€ ๋ชจํ˜ธํ•œ ๋ฌธ์ œ์ด๋‹ค. ์‚ฌ์šฉ์ž์™€ ๋ชฉ์ ์— ๋”ฐ๋ผ ์›ํ•˜๋Š” ๋ฌผ์ฒด ์˜์—ญ์ด ๋‹ฌ๋ผ์ง€๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ์ž์™€์˜ ๊ต๋ฅ˜๋ฅผ ํ†ตํ•ด ์›ํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ์˜์ƒ ๋ถ„ํ• ์„ ์ง„ํ–‰ํ•˜๋Š” ๋Œ€ํ™”ํ˜• ์˜์ƒ ๋ถ„ํ•  ๊ธฐ๋ฒ•์ด ์‚ฌ์šฉ๋œ๋‹ค. ์—ฌ๊ธฐ์„œ ์‚ฌ์šฉ์ž๊ฐ€ ์ œ๊ณตํ•˜๋Š” ์‹œ๋“œ ์ •๋ณด๊ฐ€ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ์‚ฌ์šฉ์ž์˜ ์˜๋„๋ฅผ ๋‹ด๊ณ  ์žˆ๋Š” ์‹œ๋“œ ์ •๋ณด๊ฐ€ ์ •ํ™•ํ• ์ˆ˜๋ก ์˜์ƒ ๋ถ„ํ• ์˜ ์ •ํ™•๋„๋„ ์ฆ๊ฐ€ํ•˜๊ฒŒ ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ’๋ถ€ํ•œ ์‹œ๋“œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์€ ์‚ฌ์šฉ์ž์—๊ฒŒ ๋งŽ์€ ๋ถ€๋‹ด์„ ์ฃผ๊ฒŒ ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๊ฐ„๋‹จํ•œ ์‹œ๋“œ ์ •๋ณด๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋งŒ์กฑํ• ๋งŒํ•œ ๋ถ„ํ•  ๊ฒฐ๊ณผ๋ฅผ ์–ป๋Š” ๊ฒƒ์ด ์ฃผ์š” ๋ชฉ์ ์ด ๋œ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ œ๊ณต๋œ ํฌ์†Œํ•œ ์‹œ๋“œ ์ •๋ณด๋ฅผ ๋ณ€ํ™˜ํ•˜๋Š” ์ž‘์—…์— ์ดˆ์ ์„ ๋‘์—ˆ๋‹ค. ๋งŒ์•ฝ ์‹œ๋“œ ์ •๋ณด๊ฐ€ ํ’๋ถ€ํ•˜๊ฒŒ ๋ณ€ํ™˜๋œ๋‹ค๋ฉด ์ •ํ™•ํ•œ ์˜์ƒ ๋ถ„ํ•  ๊ฒฐ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ์‹œ๋“œ ์ •๋ณด๋ฅผ ํ’๋ถ€ํ•˜๊ฒŒ ํ•˜๋Š” ๊ธฐ๋ฒ•๋“ค์„ ์ œ์•ˆํ•œ๋‹ค. ์ตœ์†Œํ•œ์˜ ์‚ฌ์šฉ์ž ์ž…๋ ฅ์„ ๊ฐ€์ •ํ•˜๊ณ  ์ด๋ฅผ ๋‹ค์–‘ํ•œ ์‹œ๋“œ ํ™•์žฅ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ๋ณ€ํ™˜ํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ์‹œ๋“œ ํ™•๋Œ€, ์‹œ๋“œ ์ƒ์„ฑ, ์‹œ๋“œ ์ฃผ์˜ ์ง‘์ค‘์— ๊ธฐ๋ฐ˜ํ•œ ์ด ์„ธ ๊ฐ€์ง€์˜ ๋Œ€ํ™”ํ˜• ์˜์ƒ ๋ถ„ํ•  ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๊ฐ๊ฐ ์‹œ๋“œ ์ฃผ๋ณ€์œผ๋กœ์˜ ์˜์—ญ ํ™•๋Œ€, ์ƒˆ๋กœ์šด ์ง€์ ์— ์‹œ๋“œ ์ƒ์„ฑ, ์˜๋ฏธ๋ก ์  ์ •๋ณด์— ์ฃผ๋ชฉํ•˜๋Š” ํ˜•ํƒœ์˜ ์‹œ๋“œ ํ™•์žฅ ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•œ๋‹ค. ๋จผ์ € ์‹œ๋“œ ํ™•๋Œ€์— ๊ธฐ๋ฐ˜ํ•œ ๊ธฐ๋ฒ•์—์„œ ์šฐ๋ฆฌ๋Š” ์‹œ๋“œ์˜ ์˜์—ญ ํ™•์žฅ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๋‘ ๋‹จ๊ณ„๋กœ ๊ตฌ์„ฑ๋œ ํ™•๋Œ€ ๊ณผ์ •์„ ํ†ตํ•ด ์ฒ˜์Œ ์‹œ๋“œ ์ฃผ๋ณ€์˜ ๋น„์Šทํ•œ ํ”ฝ์…€๋“ค์„ ์‹œ๋“œ ์˜์—ญ์œผ๋กœ ํŽธ์ž…ํ•œ๋‹ค. ์ด๋ ‡๊ฒŒ ํ™•์žฅ๋œ ์‹œ๋“œ๋ฅผ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ์‹œ๋“œ์˜ ํฌ์†Œํ•จ๊ณผ ๋ถˆ๊ท ํ˜•์œผ๋กœ ์ธํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ ์‹œ๋“œ ์ƒ์„ฑ์— ๊ธฐ๋ฐ˜ํ•œ ๊ธฐ๋ฒ•์—์„œ ์šฐ๋ฆฌ๋Š” ์‹œ๋“œ ์ฃผ๋ณ€์ด ์•„๋‹Œ ์ƒˆ๋กœ์šด ์ง€์ ์— ์‹œ๋“œ๋ฅผ ์ƒ์„ฑํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ์˜ค์ฐจ๊ฐ€ ๋ฐœ์ƒํ•œ ์˜์—ญ์— ์‚ฌ์šฉ์ž๊ฐ€ ์ƒˆ๋กœ์šด ์‹œ๋“œ๋ฅผ ์ œ๊ณตํ•˜๋Š” ๋™์ž‘์„ ๋ชจ๋ฐฉํ•˜์—ฌ ์‹œ์Šคํ…œ์„ ํ•™์Šตํ•˜์˜€๋‹ค. ์‚ฌ์šฉ์ž์˜ ์˜๋„๋ฅผ ํ•™์Šตํ•จ์œผ๋กœ์จ ํšจ๊ณผ์ ์œผ๋กœ ์‹œ๋“œ๋ฅผ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค. ์ƒ์„ฑ๋œ ์‹œ๋“œ๋Š” ์˜์ƒ ๋ถ„ํ• ์˜ ์ •ํ™•๋„๋ฅผ ๋†’์ผ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์•ฝ์ง€๋„ํ•™์Šต์„ ์œ„ํ•œ ๋ฐ์ดํ„ฐ๋กœ์จ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์‹œ๋“œ ์ฃผ์˜ ์ง‘์ค‘์„ ํ™œ์šฉํ•œ ๊ธฐ๋ฒ•์—์„œ ์šฐ๋ฆฌ๋Š” ์˜๋ฏธ๋ก ์  ์ •๋ณด๋ฅผ ์‹œ๋“œ์— ๋‹ด๋Š”๋‹ค. ๊ธฐ์กด์— ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•๋“ค๊ณผ ๋‹ฌ๋ฆฌ ์˜์ƒ ๋ถ„ํ•  ๋™์ž‘๊ณผ ์‹œ๋“œ ํ™•์žฅ ๋™์ž‘์ด ํ†ตํ•ฉ๋œ ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ์‹œ๋“œ ์ •๋ณด๋Š” ์˜์ƒ ๋ถ„ํ•  ๋„คํŠธ์›Œํฌ์˜ ํŠน์ง•๋งต๊ณผ ์ƒํ˜ธ ๊ต๋ฅ˜ํ•˜๋ฉฐ ๊ทธ ์ •๋ณด๊ฐ€ ํ’๋ถ€ํ•ด์ง„๋‹ค. ์ œ์•ˆํ•œ ๋ชจ๋ธ๋“ค์€ ๋‹ค์–‘ํ•œ ์‹คํ—˜์„ ํ†ตํ•ด ๊ธฐ์กด ๊ธฐ๋ฒ• ๋Œ€๋น„ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๊ธฐ๋กํ•˜์˜€๋‹ค. ํŠนํžˆ ์‹œ๋“œ๊ฐ€ ๋ถ€์กฑํ•œ ์ƒํ™ฉ์—์„œ ์‹œ๋“œ ํ™•์žฅ ๊ธฐ๋ฒ•๋“ค์€ ํ›Œ๋ฅญํ•œ ๋Œ€ํ™”ํ˜• ์˜์ƒ ๋ถ„ํ•  ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค.1 Introduction 1 1.1 Previous Works 2 1.2 Proposed Methods 4 2 Interactive Segmentation with Seed Expansion 9 2.1 Introduction 9 2.2 Proposed Method 12 2.2.1 Background 13 2.2.2 Pyramidal RWR 16 2.2.3 Seed Expansion 19 2.2.4 Re nement with Global Information 24 2.3 Experiments 27 2.3.1 Dataset 27 2.3.2 Implement Details 28 2.3.3 Performance 29 2.3.4 Contribution of Each Part 30 2.3.5 Seed Consistency 31 2.3.6 Running Time 33 2.4 Summary 34 3 Interactive Segmentation with Seed Generation 37 3.1 Introduction 37 3.2 Related Works 40 3.3 Proposed Method 41 3.3.1 System Overview 41 3.3.2 Markov Decision Process 42 3.3.3 Deep Q-Network 46 3.3.4 Model Architecture 47 3.4 Experiments 48 3.4.1 Implement Details 48 3.4.2 Performance 49 3.4.3 Ablation Study 53 3.4.4 Other Datasets 55 3.5 Summary 58 4 Interactive Segmentation with Seed Attention 61 4.1 Introduction 61 4.2 Related Works 64 4.3 Proposed Method 65 4.3.1 Interactive Segmentation Network 65 4.3.2 Bi-directional Seed Attention Module 67 4.4 Experiments 70 4.4.1 Datasets 70 4.4.2 Metrics 70 4.4.3 Implement Details 71 4.4.4 Performance 71 4.4.5 Ablation Study 76 4.4.6 Seed enrichment methods 79 4.5 Summary 82 5 Conclusions 87 5.1 Summary 89 Bibliography 90 ๊ตญ๋ฌธ์ดˆ๋ก 103Docto

    Safety and security of cyber-physical systems

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    The number of embedded controllers in charge of physical systems has rapidly increased over the past years. Embedded controllers are present in every aspect of our lives, from our homes to our vehicles and factories. The complexity of these systems is also more than ever. These systems are expected to deliver many features and high performance without trading off in robustness and assurance. As systems increase in complexity, however, the cost of formally verifying their correctness and eliminating security vulnerabilities can quickly explode. On top of the unintentional bugs and problems, malicious attacks on cyber-physical systems (CPS) can also lead to adverse outcomes on physical plants. Some of the recent attacks on CPS are focused on causing physical damage to the plants or the environment. Such intruders make their way into the system using cyber exploits but then initiate actions that can destabilize and even damage the underlying (physical) systems. Given the reality mentioned above and the reliability standards of the industry, there is a need to embrace new CPS design paradigms where faults and security vulnerabilities are the norms rather than an anomaly. Such imperfections must be assumed to exist in every system and component unless it is formally verified and scanned. Faults and vulnerabilities should be safely handled and the CPS must be able to recover from them at run-time. Our goal in this work is to introduce and investigate a few designs compatible with this paradigm. The architectures and techniques proposed in this dissertation do not rely on the testing and complete system verification. Instead, they enforce safety at the highest level of the system and extend guaranteed safety from a few certified components to the entire system. These solutions are carefully curated to utilize unverified components and provide guaranteed performance
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