Deep Inference

Notes:

Deep inference is a term used to describe a method of reasoning and decision-making that is based on a deep understanding of the context and implications of a particular problem or situation. It involves the use of complex reasoning and logical thinking to arrive at a conclusion or decision, and often involves the integration of multiple sources of information or data.

Deep inference can be used in a variety of contexts, including artificial intelligence, decision-making, and problem-solving. It is often used in combination with machine learning algorithms or other computational techniques to analyze and interpret large amounts of data or information in order to make decisions or solve problems.

One example of deep inference in action is in natural language processing (NLP) systems, where it can be used to understand and interpret the meaning of written or spoken language. NLP systems that use deep inference are able to analyze the context and implications of language and use this understanding to provide more accurate and nuanced responses or translations.

AI deep inference is a method of artificial intelligence (AI) that involves the use of deep learning algorithms to analyze and interpret the meaning of written or spoken language. These algorithms are designed to learn from large amounts of language data and to identify patterns and relationships in the data that can be used to understand and interpret the meaning of words, phrases, and sentences.

To work, AI deep inference systems typically follow a series of steps:

1. Data collection: The first step in deep inference is to collect a large amount of language data, such as text documents, transcripts of spoken language, or other sources of written or spoken language.
2. Data processing: The collected language data is then processed and pre-processed, which may involve tasks such as cleaning the data, removing noise, and formatting it in a way that is suitable for analysis.
3. Training: The processed language data is then used to train a deep learning algorithm, which involves feeding the data into the algorithm and adjusting its parameters and weights to optimize its performance. The goal of this step is to enable the algorithm to identify patterns and relationships in the language data that can be used to understand and interpret the meaning of words, phrases, and sentences.
4. Inference: Once the deep learning algorithm has been trained, it can be used to perform deep inference, which involves using the algorithm to analyze and interpret the meaning of new language data. This may involve tasks such as identifying the subject, verb, and object of a sentence, determining the sentiment or emotion expressed in the language, or identifying the relationships between different words or phrases.
5. Output: The results of the deep inference process are then outputted, which may be in the form of a summary, a translation, or other form of interpreted language.

Learning semantic parsers are computer programs that are designed to learn how to interpret and analyze the meaning of written or spoken language. These parsers use machine learning algorithms to analyze large amounts of language data and to learn how to identify and interpret the meaning of words, phrases, and sentences. The goal of learning semantic parsers is to enable computers to understand and interpret human language in a more natural and accurate way.

Semantic deep learning is a subfield of artificial intelligence (AI) and machine learning that focuses on using deep learning algorithms to analyze and interpret the meaning of written or spoken language. These algorithms are designed to learn from large amounts of language data and to identify patterns and relationships in the data that can be used to understand and interpret the meaning of words, phrases, and sentences.

Word rewriting systems are computer programs that are designed to analyze and modify the language used in written or spoken text. These systems may be used to correct grammar or spelling errors, to improve the clarity or readability of text, or to adapt the language to a specific audience or purpose. Word rewriting systems often use machine learning algorithms to analyze language data and to learn how to identify and modify language patterns in order to improve the quality of the text.

Wikipedia:

• Calculus of structures
• Classical logic
• Deep inference
• Deep learning
• Minimal logic
• Modal logic
• Propositional calculus

See also:

100 Best WolframAlpha Videos

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JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …

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DeepReco: deep learning based health recommender system using collaborative filtering
AK Sahoo, C Pradhan, RK Barik, H Dubey – Computation, 2019 – mdpi.com
In today’s digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition …

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures: First International Workshop, UNSURE 2019 …
H Greenspan, R Tanno, M Erdt, T Arbel… – 2019 – books.google.com
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Model Compression with Adversarial Robustness: A Unified Optimization Framework
S Gui, HN Wang, H Yang, C Yu, Z Wang… – Advances in Neural …, 2019 – papers.nips.cc
Paper accepted and presented at the Neural Information Processing Systems Conference (http://nips.cc/).

A Study on the Detection of Cattle in UAV Images Using Deep Learning
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Generative Adversarial Networks for Failure Prediction
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… Page 6. 6 S. Zheng et al. but can be image, acoustics data as well. Label y contains a lot of non-failure label 0s and very few failure label 1s. Given a failure prediction problem, one choice is to construct a deep inference neural network and adopt weighted loss objective …

E2-Train: Training State-of-the-art CNNs with Over 80% Less Energy
Z Jiang, Y Wang, X Chen, P Xu, Y Zhao, Y Lin, Z Wang – 2019 – openreview.net
Page 1. E2-Train: Training State-of-the-art CNNs with Over 80% Less Energy Yue Wang ,?, Ziyu Jiang†,?, Xiaohan Chen†,?, Pengfei Xu , Yang Zhao , Yingyan Lin and Zhangyang Wang† †Department of Computer Science …

Rooted Hypersequent Calculus for Modal Logic S5
M Aghaei, H Mohammadi – arXiv preprint arXiv:1905.09039, 2019 – arxiv.org
… Notably, labelled sequent calculus (see eg [18]), double sequent calculus (see eg [10]), display calculus (see eg [4, 29]), deep inference system (see eg [26]), nested sequent (see [7, 22]), hypersequent calculus, which was introduced independently in [1, 16, 23] and finally …

On combinatorial proofs for logics of relevance and entailment
M Acclavio, L Straßburger – International Workshop on Logic, Language …, 2019 – Springer
… Open image in new window Fig. 2. Fig. 2. The cut-free sequent systems for formulas in NNF. We make also use of the deep inference rules in Fig. 3 (see also [6, 10]), where a context \(\varGamma \{~\} is a sequent or a formula, where a hole \(\{\ takes the place of an atom …

DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning
R Li, X Cheng – Proceedings of the 2019 Conference on Empirical …, 2019 – aclweb.org
… In this paper, we present a novel plug-and-play framework based on generative adversarial imita- tion learning (GAIL) (Ho and Ermon, 2016) for enhancing existing RL-based methods, which is referred to as DIVINE for “Deep Inference via Imitating Non-human Experts” …

The problem of proof identity, and why computer scientists should care about Hilbert’s 24th problem
L Straßburger – … Transactions of the Royal Society A, 2019 – royalsocietypublishing.org
In this short overview article, I will discuss the problem of proof identity and explain how it is related to Hilbert’s 24th problem. I will also argue that not knowing when two proofs are the same…

Deepobfuscator: Adversarial training framework for privacy-preserving image classification
A Li, J Guo, H Yang, Y Chen – arXiv preprint arXiv:1909.04126, 2019 – arxiv.org
Page 1. DeepObfuscator: Adversarial Training Framework for Privacy-Preserving Image Classification Ang Li1, Jiayi Guo2, Huanrui Yang1, Yiran Chen1 1Department of Electrical and Computer Engineering, Duke University …

Proof nets through the lens of graph theory: a compilation of remarks
LTD Nguyên – arXiv preprint arXiv:1912.10606, 2019 – arxiv.org
… Logical Methods in Computer Science, Volume 15, Issue 3, September 2019. [Tiu06] Alwen Tiu. A System of Interaction and Structure II: The Need for Deep Inference. Logical Methods in Computer Science, 2(2):4, April 2006. arXiv: cs/0512036. [Tra08] Paolo Tranquilli …

A 64-mW DNN-based visual navigation engine for autonomous Nano-drones
D Palossi, A Loquercio, F Conti… – IEEE Internet of …, 2019 – ieeexplore.ieee.org
… ARM has recently released CMSIS-NN [28], which is meant to shrink this gap by accelerating deep inference compute kernels on Cortex-M microcontroller platforms, providing the equivalent of a BLAS/CUDNN library (in Section VI-B we present a detailed SoA comparison …

Probabilistic Approximate Logic and its Implementation in the Logical Imagination Engine
MO Stehr, M Kim, CL Talcott, M Knapp… – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Probabilistic Approximate Logic and its Implementation in the Logical Imagination Engine Mark-Oliver Stehr1, Minyoung Kim1, Carolyn L. Talcott1, Merrill Knapp1, and Akos Vertes2 1 SRI International, Menlo Park, CA 94025 2 Dept …

A Delay Metric for Video Object Detection: What Average Precision Fails to Tell
H Mao, X Yang, WJ Dally – Proceedings of the IEEE …, 2019 – openaccess.thecvf.com
Page 1. A Delay Metric for Video Object Detection: What Average Precision Fails to Tell Huizi Mao Stanford University huizimao@stanford.edu Xiaodong Yang NVIDIA xiaodongy@nvidia. com William J. Dally Stanford University & NVIDIA dally@stanford.edu Abstract …

A lambda-calculus that achieves full laziness with spine duplication
D Sherratt – 2019 – researchportal.bath.ac.uk
… 10 1.1.2 Deep Inference … The main interest of this work is of the following switch rule for intuitionistic logic. (A ? B) ? C s A ? (B ? C) The switch rule we are interested in has been seen in the following deep inference proof system [Gug07] …

Task-Conditioned Variational Autoencoders for Learning Movement Primitives
M Noseworthy, R Paul, S Roy, D Park, N Roy – groups.csail.mit.edu
… arXiv preprint arXiv:1609.03499, 2016. [14] K. Liu, K. Ok, W. Vega-Brown, and N. Roy. Deep inference for covariance estimation: Learn- ing gaussian noise models for state estimation. In International Conference on Robotics and Automation (ICRA). IEEE, 2018 …

Modular Normalisation of Classical Proofs
B Ralph – 2019 – cs.bath.ac.uk
… It’s a regret that only a hint of that work has made it into this thesis. The wider deep inference community—Paola, Fanny, David, Alessio, Michel, Tom, Lutz, Kai, Sonia, and others—have been a welcoming family both in Bath and further afield …

Couper: DNN model slicing for visual analytics containers at the edge
KJ Hsu, K Bhardwaj, A Gavrilovska – Proceedings of the 4th ACM/IEEE …, 2019 – dl.acm.org
Page 1. Couper: DNN Model Slicing for Visual Analytics Containers at the Edge Ke-Jou Hsu Georgia Institute of Technology nosus_hsu@gatech.edu Ketan Bhardwaj Georgia Institute of Technology ketanbj@gatech.edu Ada …

Journal of Industrial Information Integration
RM Alguliyev, RM Aliguliyev, FJ Abdullayeva – academia.edu
Page 1. Contents lists available at ScienceDirect Journal of Industrial Information Integration journal homepage: www.elsevier.com/locate/jii Privacy-preserving deep learning algorithm for big personal data analysis Rasim M …

On learning visual odometry errors
A De Maio, S Lacroix – nikosuenderhauf.github.io
… with uncertainty measures. The work in [9] introduces DICE (Deep Inference for Covariance Estimation), which learns the covariance matrix of a VO process as a maximum-likelihood for Gaussian distributions. Nevertheless, it …

Disentangling Structural Connectives or Life Without Display Property
S Drobyshevich – Journal of Philosophical Logic, 2019 – Springer
Page 1. J Philos Logic (2019) 48:279–303 https://doi.org/10.1007/s10992-018-9466- 1 Disentangling Structural Connectives or Life Without Display Property Sergey Drobyshevich1,2 Received: 22 December 2017 / Accepted …

Sequentialising nested systems
E Pimentel, R Ramanayake, B Lellmann – International Conference on …, 2019 – Springer
… time. While this is one of the main features of nested sequent calculi and deep inference in general [9], being able to separate the left/right behaviour of the modal connectives is the key to modularity for nested calculi [14, 22] …

Social Profiling: A Review, Taxonomy, and Challenges
M Bilal, A Gani, MIU Lali, M Marjani… – … , Behavior, and Social …, 2019 – liebertpub.com
Abstract Social media has taken an important place in the routine life of people. Every single second, users from all over the world are sharing interests, emotions, and other useful information th…

SoC-based computing infrastructures for scientific applications and commercial services: Performance and economic evaluations
D D’Agostino, A Quarati, A Clematis, L Morganti… – Future Generation …, 2019 – Elsevier
Skip to main content …

Scalable fine-grained proofs for formula processing
H Barbosa, JC Blanchette, M Fleury… – Journal of Automated …, 2019 – Springer
We present a framework for processing formulas in automatic theorem provers, with generation of detailed proofs. The main components are a generic contextu.

Machine learning at the network edge: A survey
MG Murshed, C Murphy, D Hou, N Khan… – arXiv preprint arXiv …, 2019 – arxiv.org
… achieve better accuracy. Ogden and Guo designed a novel mobile deep inference platform called MODI, which provides multiple deep learning models and dynamically selects the best model at run-time [80]. Their platform …

A free energy principle for a particular physics
K Friston – arXiv preprint arXiv:1906.10184, 2019 – arxiv.org
… 108 Active inference with discrete states ….. 110 Deep inference: gradient flows or least action? …. 113 …

Generative adversarial network based image privacy protection algorithm
Y He, C Zhang, X Zhu, Y Ji – Tenth International Conference …, 2019 – spiedigitallibrary.org
SPIE Digital Library Proceedings.

Undersampled MR image reconstruction using an enhanced recursive residual network
L Bao, F Ye, C Cai, J Wu, K Zeng, PCM van Zijl… – Journal of Magnetic …, 2019 – Elsevier
… parameters require more memory. Therefore, we explore the recursive learning [15], [39] to construct a deep inference net, which is consistent with the iterative operation in optimization-based methods. In this case, all residual …

Statistical analysis and optimality of biological systems
W Mlynarski, M Hledik, TR Sokolowski, G Tkacik – BioRxiv, 2019 – biorxiv.org
Page 1. DRAFT Statistical analysis and optimality of biological systems Wiktor M?ynarski*, Michal Hledik*, Thomas R Sokolowski, Gašper Tkacik1 1Institute of Science and Technology Austria, Am Campus 1, A-3400 Klosterneuburg, Austria *Equal contribution …

Thoughts, Things and Logical Guidance
A Bobrova, AV Pietarinen – Peirce and Husserl: Mutual Insights on Logic …, 2019 – Springer
… next. 3.3 Evolution of Logical Guidance and the Theory of Existential Graphs. As a logical system, EGs provide the sound and complete rules of deductive inference in the manner of ‘deep inference’ (Ma and Pietarinen 2017) …

On-device training from sensor data on batteryless platforms
B Islam, Y Luo, S Lee, S Nirjon – … of the 18th International Conference on …, 2019 – dl.acm.org
… https://doi.org/10.1145/3302506.3312611 an action accordingly. Recently, on-device deep-inference has been demonstrated in a batteryless system [3]. However, none of these systems adapt to the changes in the environment …

Answering questions by learning to rank–Learning to rank by answering questions
GS Pîrtoac?, T Rebedea, S Ruseti – arXiv preprint arXiv:1909.00596, 2019 – arxiv.org
Page 1. Abstract Answering multiple-choice questions in a setting in which no supporting documents are explicitly provided continues to stand as a core problem in natural language processing. The contribution of this article is two-fold …

The sub-additives: A proof theory for probabilistic choice extending linear logic
R Horne – 4th International Conference on Formal Structures for …, 2019 – drops.dagstuhl.de
Page 1. The Sub-Additives: A Proof Theory for Probabilistic Choice extending Linear Logic Ross Horne Computer Science and Communications, University of Luxembourg, Esch-sur-Alzette, Luxembourg ross.horne@uni.lu Abstract …

CELLO-3D: Estimating the Covariance of ICP in the Real World
D Landry, F Pomerleau… – … Conference on Robotics …, 2019 – ieeexplore.ieee.org
Page 1. CELLO-3D: Estimating the Covariance of ICP in the Real World David Landry François Pomerleau Philippe Gigu`ere Abstract—The fusion of Iterative Closest Point (ICP) reg- istrations in existing state estimation frameworks …

Sequent Calculi for Global Modal Consequence Relations
M Ma, J Chen – Studia Logica, 2019 – Springer
… The existing sequent calculi for normal modal logics, including the stan- dard Gentzen sequent calculi, hyper-sequent calcucli, tree-sequent calculi, display caluli, deep inference systems etc., are designed for local modal con- sequence relations (cf. eg [1,15,20]) …

Predicting Personality Traits from Social Network Profiles
M Stankevich, A Latyshev, N Kiselnikova… – Russian Conference on …, 2019 – Springer
… Image Underst. 156, 34–50 (2017)CrossRefGoogle Scholar. 21. Cucurull, G., Rodríguez, P., Yazici, VO, Gonfaus, JM, Roca, FX, Gonzàlez, J.: Deep inference of personality traits by integrating image and word use in social networks. arXiv preprint arXiv:1802.06757 (2018). 22 …

Improving question answering with external knowledge
X Pan, K Sun, D Yu, J Chen, H Ji, C Cardie… – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Improving Question Answering with External Knowledge Xiaoman Pan1* Kai Sun2* Dian Yu3 Jianshu Chen3 Heng Ji1 Claire Cardie2 Dong Yu3 1University of Illinois at Urbana-Champaign, Champaign, IL, USA 2Cornell …

Two Implications and the Dual-Process Theories of Reasoning
A Bobrova, AV Pietarinen – cifma.github.io
… Studia Logica 105(3), 2017, 625–647. 14. Ma, M., Pietarinen, A.-V. A Graphical Deep Inference System for Intuitionistic Logic, Logique & Analyse 245, 73–114. 15. Osman, M. An Evaluation of Dual-Process Theories of Reasoning …

Evaluation of the effectiveness of physical exercises using the action of infrared rays
G Ko?ody?ska, A Siero? – Journal of Education, Health and Sport, 2019 – ojs.ukw.edu.pl
… 621 Page 3. additional benefit of training is pleasant feeling spill heat in the body as a result of deep inference wave [7,8]. Among the expected effects of training are mentioned: ? removing cellulite; ? increase in metabolic rate; ? acceleration of blood circulation in the skin and …

Low Power Embedded Gesture Recognition Using Novel Short-Range Radar Sensors
M Eggimann, J Erb, P Mayer, M Magno… – 2019 IEEE …, 2019 – researchgate.net
… sensors. At its heart, GAP8 is composed by an advanced RISC-V microcontroller unit coupled with a programmable octa-core accelerator with RISC-V cores enhanced for digital signal processing and embedded deep inference …

GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response
MA Rahman, SM Preum, R Williams, H Alemzadeh… – cs.virginia.edu
… tion, temporal expression detection, and value association for accurate information extraction; (iii) minimizing the effects of noisy environments and noisy data, missing data, homophones, and other realistic speech issues on information extraction; (iv) deep inference of EMS text …

Rewriting methods in higher algebra
Y Guiraud – 2019 – hal.archives-ouvertes.fr
… I have explored translations of various types of rewriting-like systems into polygraphs, and the respective computational properties of the two formalisms, for word and term rewriting systems, Petri nets, and deep-inference proofs of propositional logic and linear logic …

On the decision problem for MELL
L Straßburger – Theoretical Computer Science, 2019 – Elsevier
Skip to main content …

24th Workshop on Logic, Language, Information and Computation (WoLLIC 2017)
J Kennedy, R Queiroz, A Silva… – Logic Journal of the …, 2019 – academic.oup.com
… University of Bath, Bath, UK. Inria Palaiseau, France. E-mail: P.Bruscoli@Bath.ac.uk. Switch and medial are two inference rules that play a central role in many deep inference proof systems. In specific proof systems, the mix rule may also be present …

CrowdOS: a ubiquitous operating system for crowdsourcing and mobile crowd sensing
Y Liu, Z Yu, B Guo, Q Han, J Su, J Liao – arXiv preprint arXiv:1909.00805, 2019 – arxiv.org
… To address challenge 3), we propose a Deep Feedback Framework based on Human-Machine Interaction (DFHMI) (Section 6). A quality assessment mecha- nism and a shallow-deep inference mechanism are designed to uniformly support the implementation of strategies for …

AI-IMU dead-reckoning
M Brossard, A Barrau, S Bonnabel – arXiv preprint arXiv:1904.06064, 2019 – arxiv.org
Page 1. 1 AI-IMU Dead-Reckoning Martin BROSSARD? , Axel BARRAU† and Silv`ere BONNABEL? ?MINES ParisTech, PSL Research University, Centre for Robotics, 60 Bd Saint-Michel, 75006 Paris, France †Safran Tech …

Domain Adaptation and Privileged Information for Visual Recognition
S Motiian – 2019 – researchrepository.wvu.edu
Page 1. Graduate Theses, Dissertations, and Problem Reports 2019 Domain Adaptation and Privileged Information for Visual Recognition Saeid Motiian Follow this and additional works at: https://researchrepository.wvu.edu/etd …

A temporal epistemic logic with a non-rigid set of agents for analyzing the blockchain protocol
B Marinkovi?, P Glavan, Z Ognjanovi?… – Journal of logic and …, 2019 – academic.oup.com
… The need of |$k$|-nested implications in inference rules comes directly from our completeness proof where they give a form of deep inference that is essentially the same as nested sequents [3]. Deep inference refers to deductive systems in which rules can not only be applied …

Cognitive checkpoint: Emerging technologies for biometric-enabled watchlist screening
SN Yanushkevich, KW Sundberg, NW Twyman… – Computers & …, 2019 – Elsevier
… From this perspective, our analysis delivers the following technology landscape: (1) Dynamical probabilistic models (Pitchforth, Wu, Fookes, Mengersen, 2015, Wu, Pitchforth, Mengersen, 2014); they better reflect layered security properties; (2) Deep inference in traveler risk …

Improving Subject-Area Question Answering with External Knowledge
X Pan, K Sun, D Yu, J Chen, H Ji, C Cardie… – Proceedings of the 2nd …, 2019 – aclweb.org
Page 1. Proceedings of the Second Workshop on Machine Reading for Question Answering, pages 27–37 Hong Kong, China, November 4, 2019. c 2019 Association for Computational Linguistics 27 Improving Question Answering with External Knowledge …

Autonomous Design Space Exploration of Computing Systems for Sustainability: Opportunities and Challenges
JR Doppa, J Rosca, P Bogdan – IEEE Design & Test, 2019 – ieeexplore.ieee.org
… 9th Asian Conf. Machine Learning (ACML), Seoul, South Korea, Nov. 2017, pp. 129–144. [32] NK Jayakodi et al., “Trading-off accuracy and energy of deep inference on embedded systems: A co-design approach,” IEEE Trans. CAD Integr. Circ. Syst. (TCAD), vol. 37, no. 11, pp …

Improvement of Multi-agent Continuous Cooperative Patrolling with Learning of Activity Length
A Sugiyama, L Wu, T Sugawara – International Conference on Agents and …, 2019 – Springer
… The features of our method is that, like the method in [16], it does not require tight communication and deep inference for cooperation, meaning that frequent message exchange and the sophisticated reasoning of others’ internal intentions are not used; this makes our method …

Display to Labelled Proofs and Back Again for Tense Logics
A Ciabattoni, T Lyon, R Ramanayake, A Tiu – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. arXiv:1911.02289v1 [cs.LO] 6 Nov 2019 Display to Labelled Proofs and Back Again for Tense Logics AGATA CIABATTONI, TIM LYON, and REVANTHA RAMANAYAKE, Technische Univer- sität Wien, Austria ALWEN TIU, The Australian National University, Australia …

An ecumenical notion of entailment
E Pimentel, LC Pereira, V de Paiva – Synthese, 2019 – Springer
… theory. Nevertheless, every new wave of different proof systems (Schütte’s calculus, Display Calculi, Deep Inference, Nested systems, Labelled systems, etc) has improved our understanding of these basic systems. Regarding …

Two Is Enough–Bisequent Calculus for S5
A Indrzejczak – International Symposium on Frontiers of Combining …, 2019 – Springer
… Independently of Dos?en’s general frame (not well known either) similar ideas were extensively applied, under different names (deep inference calculi, tree-hypersequent calculi), in the field of modal and temporal logics (eg Bull [11], Kashima [27], Stouppa [43], Brünnler [10 …

Syntactic interpolation for tense logics and bi-intuitionistic logic via nested sequents
T Lyon, A Tiu, R Goré, R Clouston – arXiv preprint arXiv:1910.05215, 2019 – arxiv.org
Page 1. Syntactic Interpolation for Tense Logics and Bi-Intuitionistic Logic via Nested Sequents Tim Lyon Institut für Logic and Computation, Technische Universität Wien, Austria https://logic-cs.at/phd/students/timothy-lyon/ lyon …

Edit distance Kernelization of NP theorem proving for polynomial-time machine learning of proof heuristics
D Windridge, F Kammüller – Future of Information and Communication …, 2019 – Springer
… Log. 44(1), 36–50 (1979). https://doi.org/10.2307/2273702MathSciNetCrossRefGoogle Scholar. 3. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar. 4. Das, A.: The Complexity of Propositional Proofs in Deep …

De Morgan Dual Nominal Quantifiers Modelling Private Names in Non-Commutative Logic
R Horne, A Tiu, B Aman, G Ciobanu – ACM Transactions on …, 2019 – dl.acm.org
… conclusion. The key feature of the calculus of structures is deep inference, which is the ability to apply all rules in any context, ie, formulae with a hole of the following form: C{ } { · }C{ }? P | P ?C{ }|?x.C{ }, where ??{, ? …

Policy directions for the Work Assistance Service in South Korea: based on the characteristics of service users
JU Jeong, M Yeon, HJ Kwon – Journal of Asian Public Policy, 2019 – Taylor & Francis
… of employers and employees using the Work Assistance Service and other major businesses of KEAD through descriptive statistics and crossover analysis for each business, and x2-test, ANOVA, etc., were conducted for those requiring deep inference statistics as needed …

An infinitary treatment of full mu-calculus
B Afshari, G Jäger, GE Leigh – International Workshop on Logic, Language …, 2019 – Springer
We explore the proof theory of the modal [equation]-calculus with converse, aka the ‘full [equation]-calculus’. Building on nested sequent calculi for tense logics and infinitary proof theory of…

Why images cannot be arguments, but moving ones might
M Champagne, AV Pietarinen – Argumentation, 2019 – Springer
Some have suggested that images can be arguments. Images can certainly bolster the acceptability of individual premises. We worry, though, that the static.

An intuitionistic formula hierarchy based on high?school identities
T Brock?Nannestad, D Ilik – Mathematical Logic Quarterly, 2019 – Wiley Online Library

On a multilattice analogue of a hypersequent S5 calculus
O Grigoriev, Y Petrukhin – Logic and Logical Philosophy, 2019 – wydawnictwoumk.pl
Page 1. Logic and Logical Philosophy Volume 28 (2019), 683–730 DOI: 10.12775/LLP.2019.031 Oleg Grigoriev Yaroslav Petrukhin ON A MULTILATTICE ANALOGUE OF A HYPERSEQUENT S5 CALCULUS Abstract. In this …

Beyond Mobile Apps: A Survey of Technologies for Mental Well-being
K Woodward, E Kanjo, D Brown, TM McGinnity… – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. 1 Beyond Mobile Apps: A Survey of Technologies for Mental Well-being Kieran Woodward, Eiman Kanjo, David Brown, TM McGinnity, Becky Inkster, Donald J Macintyre & Athanasios Tsanas Abstract—Mental health …

Peirce’s Existential Graphs as a Contribution to Transcendental Logic
M Shafiei – Peirce and Husserl: Mutual Insights on Logic …, 2019 – Springer
Peirce, among his vast logical works, also invented a less known logical framework called by him Existential Graphs. It offers a diagrammatic method to represent logical expressions and logical…

3D Object Representations for Robot Perception
BCM Burchfiel – 2019 – search.proquest.com
Page 1. 3D Object Representations for Robot Perception by Benjamin CM Burchfiel Department of Computer Science Duke University Date: Approved: George Konidaris, Supervisor Carlo Tomasi, Chair Katherine Heller Stefanie Tellex …

From QBFs to MALL and back via focussing: fragments of multiplicative additive linear logic for each level of the polynomial hierarchy
A Das – arXiv preprint arXiv:1906.03611, 2019 – arxiv.org
Page 1. arXiv:1906.03611v1 [cs.LO] 9 Jun 2019 FROM QBFS TO MALL AND BACK VIA FOCUSSING FRAGMENTS OF MULTIPLICATIVE ADDITIVE LINEAR LOGIC FOR EACH LEVEL OF THE POLYNOMIAL HIERARCHY ANUPAM DAS University of Copenhagen Abstract …

Intuitionistic proofs without syntax
WB Heijltjes, DJD Hughes… – 2019 34th Annual ACM …, 2019 – ieeexplore.ieee.org
Page 1. Intuitionistic proofs without syntax Willem B. Heijltjes Dept. Computer Science University of Bath UK Dominic JD Hughes Logic Group UC Berkeley USA Lutz Straßburger Parsifal Inria France Abstract—We present Intuitionistic …

Spatiotemporal and Multimodal Analysis of Personality Traits
B Mand?ra, D Giritlioglu, SF Y?lmaz… – 15th International …, 2019 – research.utwente.nl
Page 41. ENTERFACE’19, JULY 8TH-AUGUST 2ND, ANKARA, TURKEY 1 Spatiotemporal and Multimodal Analysis of Personality Traits Burak Mand?ra (1,?), Dersu Giritlioglu (1,?), Selim F?rat Y?lmaz (2,?), Can Ufuk Ertenli …

Completeness for game logic
S Enqvist, HH Hansen, C Kupke… – 2019 34th Annual …, 2019 – ieeexplore.ieee.org
… and complete. The first of these is the system for game logic G which is a cut-free sequent calculus with deep inference rules. We show that G is complete, and that this implies completeness of Parikh’s Hilbert system. One of …

Modeling The Edge: Peer-to-Peer Reincarnated
G Yadgar, O Kolosov, MF Aktas, E Soljanin – 2nd {USENIX} Workshop …, 2019 – usenix.org
Page 1. Modeling The Edge: Peer-to-Peer Reincarnated Gala Yadgar and Oleg Kolosov Computer Science Department Technion Mehmet Fatih Aktas and Emina Soljanin Department of Electrical and Computer Engineering Rutgers University …

Modularisation of sequent calculi for normal and non-normal modalities
B Lellmann, E Pimentel – ACM Transactions on Computational Logic …, 2019 – dl.acm.org
… While this is one of the main features of nested sequent calculi and deep inference in general [25], being able to separate the left/right behaviour of the modal connectives is the key to modularity for nested and linear ACM Transactions on Computational Logic, Vol. 20, No …

The nature of the physical world: THE GIFFORD LECTURES 1927
A Eddington – 2019 – books.google.com
Page 1. Page 2. THE NATURE OF THE PHYSICAL WORLD 1 Page 3. The English astronomer and mathematician Sir Arthur Stanley Ed- dington is famous for his work concerning the theory of relativity. He is also well known a philosopher of science and a populariser of science …

Intuitionistic proofs without syntax
L Straßburger, W Heijltjes, D Hughes – 2019 – hal.inria.fr
Page 1. HAL Id: hal-02386878 https://hal.inria.fr/hal-02386878 Submitted on 29 Nov 2019 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not …

Machine Learning for Human Performance Capture from Multi-Viewpoint Video
M Trumble – 2019 – pdfs.semanticscholar.org
… human skeletal pose from PVH data. Initially a variety of machine learning approaches for skeletal joint pose estimation are explored, contrasting classical and deep inference methods. By quantizing volumetric data Page 6. vi into …

Tools for Tutoring Theoretical Computer Science Topics
M McCartin-Lim – 2019 – pdfs.semanticscholar.org
Page 1. University of Massachusetts Amherst ScholarWorks@UMass Amherst Doctoral Dissertations Dissertations and Theses 2019 Tools for Tutoring Theoretical Computer Science Topics Mark McCartin-Lim Wabash College …

Between Legacy and Revival: A Postmodern Reading of Mary Shelly’s Frankenstein and Ahmed Saadawi’s Frankenstein in Baghdad
R Reda Nasr – ???? ????? ?????? ?? ??????, 2019? – jssa.journals.ekb.eg
Page 1. ?????? ?? ?????? ????? ???? ???? ??????? ????? 9102 ?????? ????? 0 Between Legacy and Revival: A Postmodern Reading of Mary Shelly’s Frankenstein and Ahmed Saadawi’s Frankenstein in Baghdad 1 Rania Reda Nasr …

Syntactic cut-elimination and backward proof-search for tense logic via linear nested sequents (Extended version)
R Goré, B Lellmann – arXiv preprint arXiv:1907.01270, 2019 – arxiv.org
Page 1. arXiv:1907.01270v1 [cs.LO] 2 Jul 2019 Syntactic cut-elimination and backward proof-search for tense logic via linear nested sequents (Extended version)? Rajeev Goré1 and Björn Lellmann2 1 Research School of Computer …

The canonical pairs of bounded depth Frege systems
P Pudlak – arXiv preprint arXiv:1912.03013, 2019 – arxiv.org
Page 1. arXiv:1912.03013v1 [math.LO] 6 Dec 2019 The canonical pairs of bounded depth Frege systems Pavel Pudlák ? December 9, 2019 Abstract The canonical pair of a proof system P is the pair of disjoint NP sets where …

History and Applications
CS Peirce – 2019 – books.google.com
Page 1. Charles S. Peirce Logic of the Future Volume 1 Page 2. Peirceana Edited by Francesco Bellucci and Ahti-Veikko Pietarinen Volume 1 Page 3. Charles S. Peirce Logic of the Future Writings on Existential Graphs Volume …

Propositional intuitionistic multiple-conclusion calculus via proof graphs
RVB Carvalho, AG de Oliveira… – Logic Journal of the …, 2019 – academic.oup.com
… favours a more symmetrical representation. However, there are also other formalisms which try to solve the problem of bureaucracy, like deep inference [19, 29] and expansion trees [12]. After Gentzen’s seminal paper, since …

Syntactic Cut-Elimination and Backward Proof-Search for Tense Logic via Linear Nested Sequents
R Goré, B Lellmann – … Conference on Automated Reasoning with Analytic …, 2019 – Springer
We give a linear nested sequent calculus for the basic normal tense logic [equation]. We show that the calculus enables backwards proof-search, counter-model construction and syntactic…

Foundational Proof Certificates in theorem proving
MRB Martínez – pdfs.semanticscholar.org
Page 1. HAL Id: tel-01743857 https://tel.archives-ouvertes.fr/tel-01743857 Submitted on 26 Mar 2018 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not …

Unsupervised Tuning of Filter Parameters Without Ground-Truth Applied to Aerial Robots
S Li, C De Wagter… – IEEE Robotics and …, 2019 – ieeexplore.ieee.org
… 2, pp. 967–972, 2017. [14] K. Liu, K. Ok, W. Vega-Brown, and N. Roy, “Deep inference for covari- ance estimation: Learning gaussian noise models for state estimation,” in 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018, pp. 1436–1443.

Sequential sampling models with variable boundaries and non-normal noise: A comparison of six models
A Voss, V Lerche, U Mertens, J Voss – Psychonomic bulletin & review, 2019 – Springer
One of the most prominent response-time models in cognitive psychology is the diffusion model, which assumes that decision-making is based on a continuous evidence accumulation described by a Wiener…

Budget Constraint Roadside Units Placement for Traffic Flows Monitoring System with Reliability in Vehicular Networks
P Jiang, P Li, T Zhang, W Huang, H He… – 2019 IEEE 21st …, 2019 – ieeexplore.ieee.org
Page 1. Budget Constraint Roadside Units Placement for Traffic Flows Monitoring System with Reliability in Vehicular Networks Peng Jiang †‡ , Peng Li †‡? , Tao Zhang †§ , Weiyi Huang †‡ , Heng He †‡ , Lei Nie †‡ and Qin Liu ¶ …

Intelligence-based health recommendation system using big data analytics
AK Sahoo, S Mallik, C Pradhan, BSP Mishra… – Big data analytics for …, 2019 – Elsevier
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Strong Bisimulation for Control Operators
E Bonelli, D Kesner, A Viso – arXiv preprint arXiv:1906.09370, 2019 – arxiv.org
Page 1. arXiv:1906.09370v1 [cs.LO] 22 Jun 2019 STRONG BISIMULATION FOR CONTROL OPERATORS EDUARDO BONELLI, DELIA KESNER, AND ANDRÉS VISO Stevens Institute of Technology e-mail address: eabonelli@gmail.com …

The Software/Hardware Co-design and Implementation of SM2/3/4 Encryption/Decryption and Digital Signature System
X Zheng, C Xu, X Hu, Y Zhang… – IEEE Transactions on …, 2019 – ieeexplore.ieee.org
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