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:

- 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.
- 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.
- 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.
- 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.
- 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:

**A graphical deep inference system for intuitionistic logic**

M Minghui – Logique et Analyse, 2019 – research.nu.edu.kz

A graphical approach to intuitionistic propositional logic is presented. The system GrIn is a deep inference system and it is formulated in terms of Peirce’s existential graphs. GrIn is shown to be sound and complete with respect to the class of all Heyting algebras. Moreover …

**Dual dynamic inference: Enabling more efficient, adaptive and controllable deep inference**

Y Wang, J Shen, TK Hu, P Xu, T Nguyen… – arXiv preprint arXiv …, 2019 – arxiv.org

State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widely deployments in resource-constrained Internet of Things (IoT) applications. We propose a …

**Interrogating theoretical models of neural computation with deep inference**

SR Bittner, A Palmigiano, AT Piet, CA Duan, CD Brody… – bioRxiv, 2019 – biorxiv.org

A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon–whether behavioral or in terms of neural …

**Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference**

W Andrew, C Greatwood, T Burghardt – arXiv preprint arXiv:1907.05310, 2019 – arxiv.org

This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian cattle in …

**Deep Inference for Proof Search**

O Kahramanogullar? – anupamdas.com

Deep inference can benefit proof search if non-determinism in proof search is controlled, and this way the breadth of the search space remains manageable. We support this argument by demonstrating three features of deep inference on examples, survey results …

**Introduction to Deep Inference**

A Tubella, L Straßburger – 2019 – hal.inria.fr

The course will give a basic introduction to deep inference, which is a design principle for proof formalisms in which inference rules can be applied at any depth inside the proof. In this course, we will provide a clear understanding of the intuitions behind deep inference …

**Neurocomputational mechanisms underlying emotional awareness: insights afforded by deep active inference and their potential clinical relevance**

R Smith, RD Lane, T Parr, KJ Friston – Neuroscience & Biobehavioral …, 2019 – Elsevier

… of simulations. These simulations illustrate the way individual differences in (seven) distinct underlying deep inference processes result in measurable phenotypes associated with different levels of emotional awareness. Our …

**Improving Retrieval-Based Question Answering with Deep Inference Models**

GS Pîrtoac?, T Rebedea… – 2019 International Joint …, 2019 – ieeexplore.ieee.org

Question answering is one of the most important and difficult applications at the border of information retrieval and natural language processing, especially when we talk about complex questions which require some form of inference to determine the correct answer. In …

**ModiPick: SLA-aware Accuracy Optimization For Mobile Deep Inference**

SS Ogden, T Guo – arXiv preprint arXiv:1909.02053, 2019 – arxiv.org

Mobile applications are increasingly leveraging complex deep learning models to deliver features, eg, image recognition, that require high prediction accuracy. Such models can be both computation and memory-intensive, even for newer mobile devices, and are therefore …

**Embedded Deep Inference in Practice: Case for Model Partitioning**

S Dey, A Mukherjee, A Pal – Proceedings of the 1st Workshop on …, 2019 – dl.acm.org

With increased focus on in situ analytics, artificial intelligence (AI) algorithms are getting deployed on embedded devices at the network edge. Growing popularity of Deep Learning (DL) and inference largely due to minimization of feature engineering, availability of pre …

**Low-Power and High-Speed Deep FPGA Inference Engines for Weed Classification at the Edge.**

C Lammie, A Olsen, T Carrick, MR Azghadi – IEEE Access, 2019 – researchgate.net

… validation accuracy. This is a significant step toward enabling deep inference and learning on IoT edge devices, and smart portable machines such as an agricultural robot, which is the target application in this paper. INDEX …

**Extreme Few-view CT Reconstruction using Deep Inference**

H Kim, R Anirudh, KA Mohan, K Champley – arXiv preprint arXiv …, 2019 – arxiv.org

Reconstruction of few-view x-ray Computed Tomography (CT) data is a highly ill-posed problem. It is often used in applications that require low radiation dose in clinical CT, rapid industrial scanning, or fixed-gantry CT. Existing analytic or iterative algorithms generally …

**Deep Inference on Multi-sensor Data**

A Ghosh – 2019 – drum.lib.umd.edu

Computer vision-based intelligent autonomous systems engage various types of sensors to perceive the world they navigate in. Vision systems perceive their environments through inferences on entities (structures, humans) and their attributes (pose, shape, materials) that …

**EdgeSanitizer: Locally Differentially Private Deep Inference at the Edge for Mobile Data Analytics**

C Xu, J Ren, L She, Y Zhang, Z Qin… – IEEE Internet of Things …, 2019 – ieeexplore.ieee.org

Deep neural networks have been widely applied in various machine learning applications for mobile data analytics in cloud. However, this approach introduces significant data challenges, because the cloud operator can perform deep inferences on the available data …

**Deep inference and expansion trees for second-order multiplicative linear logic**

L Straßburger – Mathematical Structures in Computer Science, 2019 – cambridge.org

In this paper, we introduce the notion of expansion tree for linear logic. As in Miller’s original work, we have a shallow reading of an expansion tree that corresponds to the conclusion of the proof, and a deep reading which is a formula that can be proved by propositional rules …

**Trading-off Accuracy and Energy of Deep Inference on Embedded Systems: A Co-Design Approach**

N Kannappan Jayakodi, A Chatterjee… – arXiv preprint arXiv …, 2019 – adsabs.harvard.edu

Deep neural networks have seen tremendous success for different modalities of data including images, videos, and speech. This success has led to their deployment in mobile and embedded systems for real-time applications. However, making repeated inferences …

**Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference. In 2019 IEEE/RSJ**

W Andrew, C Greatwood, T Burghardt – research-information.bris.ac.uk

This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation. The system is able to autonomously find and visually identify by coat pattern individual Holstein Friesian …

**Characterizing the Deep Neural Networks Inference Performance of Mobile Applications**

SS Ogden, T Guo – arXiv preprint arXiv:1909.04783, 2019 – arxiv.org

… However, achieving faster deep inference with high accuracy is often constrained by mobile computation, storage and network conditions … We make the following contributions. • Performance characterization of mobile deep inference …

**Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection**

V Sanz Marco, B Taylor, Z Wang… – ACM Transactions on …, 2019 – eprints.lancs.ac.uk

… embedded devices. Numerous optimization tactics have been proposed to enable deep inference1 on embedded devices. Prior approaches are either architecture specific [53], or come with drawbacks. Model compression …

**Bottlenet: A deep learning architecture for intelligent mobile cloud computing services**

AE Eshratifar, A Esmaili… – 2019 IEEE/ACM …, 2019 – ieeexplore.ieee.org

… cloud. As a compromise between the mobile-only and the cloud-only ap- proach, recently, a body of research work has been investigat- ing the idea of splitting a deep inference network between the mobile and cloud [6]–[12] …

**Inference with Deep Generative Priors in High Dimensions**

P Pandit, M Sahraee-Ardakan, S Rangan… – arXiv preprint arXiv …, 2019 – arxiv.org

… Is is possible to design computationally efficient yet optimal methods? To answer these questions, this paper considers deep inference via approximate message passing (AMP), a powerful approach for analyzing estimation problems in certain high-dimensional …

**Occlumency: Privacy-preserving Remote Deep-learning Inference Using SGX**

T Lee, Z Lin, S Pushp, C Li, Y Liu, Y Lee, F Xu… – The 25th Annual …, 2019 – dl.acm.org

Page 1. Occlumency: Privacy-preserving Remote Deep-learning Inference Using SGX Taegyeong Lee1?, Zhiqi Lin2?, Saumay Pushp1, Caihua Li3?, Yunxin Liu4, Youngki Lee5, Fengyuan Xu6?, Chenren Xu7?, Lintao Zhang4, Junehwa Song1 …

**Training deep neural density estimators to identify mechanistic models of neural dynamics**

PJ Gonçalves, JM Lueckmann, M Deistler… – bioRxiv, 2019 – biorxiv.org

Page 1. Training deep neural density estimators to 1 identify mechanistic models of neural dynamics 2 Pedro J. Gonçalves1,2*, Jan-Matthis Lueckmann1,2*, Michael Deistler1*, Marcel Nonnenmacher1,2, 3 Kaan Öcal2,3, Giacomo …

**Towards a combinatorial proof theory**

B Ralph, L Straßburger – … on Automated Reasoning with Analytic Tableaux …, 2019 – Springer

… homomorphism. This leads us to new logics and proof systems that we call combinatorial. Keywords. Proof theory Combinatorial proofs Deep inference Fibrations Cographs. Download conference paper PDF. 1 Introduction. Combinatorial …

**EdgeServe: efficient deep learning model caching at the edge**

T Guo, RJ Walls, SS Ogden – Proceedings of the 4th ACM/IEEE …, 2019 – dl.acm.org

… [11] T. Guo. Cloud-based or on-device: An empirical study of mobile deep inference. In 2018 IEEE International Conference on Cloud Engineering (IC2E ’18) … [14] SS Ogden and T. Guo. MODI: Mobile deep inference made efficient by edge computing …

**On Combinatorial proofs for modal logic**

M Acclavio, L Straßburger – … Reasoning with Analytic Tableaux and Related …, 2019 – Springer

… The proof of soundness and completeness is based on the sequent calculus with some added features from deep inference. Keywords … 4. Fig. 4. Deep inference rules for weakening, contraction, and the \(\mathsf t\)- and \(\mathsf 4\)-axioms. Open image in new window Fig …

**Deep learning methods for likelihood-free inference: approximating the posterior distribution with convolutional neural networks**

UK Mertens – 2019 – archiv.ub.uni-heidelberg.de

Page 1. Title of the publication-based thesis Deep Learning methods for likelihood-free inference – approximating the posterior distribution with convolutional neural networks presented by Ulf Kai Mertens, M.Sc. year of submission 2019 Dean: Prof …

**Poster: EdgeServe: Efficient Deep Learning Model Caching at the Edge**

TGRJW Samuel, S Ogden – cake-lab.github.io

… Implemen- tation (NSDI’17). [7] T. Guo, “Cloud-based or on-device: An empirical study of mobile deep inference,” in 2018 IEEE International Conference on Cloud Engineering (IC2E ’18). [8] SS Ogden and T. Guo, “MODI: Mobile …

**Embedded Deep Learning**

B Moons, D Bankman, M Verhelst – 2019 – Springer

Page 1. Bert Moons · Daniel Bankman Marian Verhelst Embedded Deep Learning Algorithms, Architectures and Circuits for Always-on Neural Network Processing Page 2. Embedded Deep Learning Page 3. Bert Moons • Daniel Bankman • Marian Verhelst …

**Lightweight and Unobtrusive Privacy Preservation for Remote Inference via Edge Data Obfuscation**

D Xu, M Zheng, L Jiang, C Gu, R Tan… – arXiv preprint arXiv …, 2019 – arxiv.org

… Separation of data sources and ML compute power: With the advances of deep learning, the depth of inference models and the needed compute power to support these deep inference models increase drastically. Thus, the …

**Enabling Adaptive Intelligence in Cloud-Augmented Multiple Robots Systems**

S Zhang, Y Li, B Liu, S Fu, X Liu – 2019 IEEE International …, 2019 – ieeexplore.ieee.org

… The recently emerging cooperative deep inference can be a potential solution to enabling adaptive intelligence in cloud-augmented MRSs. Rather than transmitting all the data to the cloud and executing all computational tasks …

**What are Combinatorial Proofs?**

L Straßburger – 2019 – anupamdas.com

… However, many deep inference proof systems allow such a separation via certain strong decomposition theorems … Conjecture 1. A logic admits combinatorial proofs if and only if it has a deep inference proof system admitting a strong decomposition theorem …

**On the Logical Philosophy of Assertive Graphs**

D Chiffi, AV Pietarinen – preprint – academia.edu

… It is also shown how the deep-inference nature of transformations emerges from the transformation rules, which we take to mean that the graphical approach along the lines suggested here is the true logic of deep inference also for assertions. Section 6 concludes …

**BPNet: Branch-pruned conditional neural network for systematic time-accuracy tradeoff in DNN inference: work-in-progress**

K Park, Y Yi – Proceedings of the International Conference on …, 2019 – dl.acm.org

… [2] NK Jayakodi et al. 2018. Trading-Off Accuracy and Energy of Deep Inference on Embedded Systems: A Co-Design Approach. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 37, 11 (2018). [3] A. Mohammadi et al. 2017 …

**Challenges and Opportunities of DNN Model Execution Caching**

GR Gilman, SS Ogden, RJ Walls, T Guo – Proceedings of the Workshop …, 2019 – dl.acm.org

… tian}@wpi.edu Abstract We explore the opportunities and challenges of model exe- cution caching, a nascent research area that promises to im- prove the performance of cloud-based deep inference serving. Broadly, model …

**Embedded Deep Neural Networks**

B Moons, D Bankman, M Verhelst – Embedded Deep Learning, 2019 – Springer

Deep learning networks have recently come up as the state-of-the-art classification algorithms in artificial intelligence, achieving super-human performance in a number of perceptive tasks in…

**Privacy-preserving deep learning algorithm for big personal data analysis**

RM Alguliyev, RM Aliguliyev, FJ Abdullayeva – Journal of Industrial …, 2019 – Elsevier

Skip to main content Skip to article …

**Multivariate Uncertainty in Deep Learning**

RL Russell, C Reale – arXiv preprint arXiv:1910.14215, 2019 – arxiv.org

… 3052–3057, IEEE, 2015. [22] 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), pp. 1436–1443, IEEE, 2018 …

**EdgeInsight: Characterizing and Modeling the Performance of Machine Learning Inference on the Edge and Cloud**

P Ross, A Luckow – 2019 IEEE International Conference on Big …, 2019 – ieeexplore.ieee.org

Page 1. EdgeInsight: Characterizing and Modeling the Performance of Machine Learning Inference on the Edge and Cloud Philipp Ross BMW Group Munich, Germany philipp.p.ross@ bmw.de Andre Luckow BMW Group Munich, Germany andre.luckow@bmwgroup.com …

**Neuro. ZERO: a zero-energy neural network accelerator for embedded sensing and inference systems**

S Lee, S Nirjon – Proceedings of the 17th Conference on Embedded …, 2019 – dl.acm.org

… The de facto approach to enable deep inference on resource-constrained systems is to obtain a pre-trained model from some other sources and then to compress and/or prune the network until it fits the memory and computing capacity of the embedded platform [37, 42, 43, 80 …

**Training deep neural density estimators to**

PJ Gonçalves, JM Lueckmann, M Deistler… – biorxiv.org

Page 1. Training deep neural density estimators to 1 identify mechanistic models of neural dynamics 2 Pedro J. Gonçalves1,2*, Jan-Matthis Lueckmann1,2*, Michael Deistler1*, Marcel Nonnenmacher1,2, 3 Kaan Öcal2,3, Giacomo …

**Time-Aware Deep Intelligence on Batteryless Systems**

B Islam, S Lee, S Nirjon – Brief Presentations Proceedings (RTAS …, 2019 – 2019.rtas.org

… algorithm. We generate 1000 deep inference tasks, where a task refers to the execution of deep neural network inference for a data sample … simulation. We simulate 1000 deep inference tasks with the early exit and 10-fold cross-validation …

**E2-Train: Energy-Efficient Deep Network Training with Data-, Model-, and Algorithm-Level Saving**

Y Wang, Z Jiang, X Chen, P Xu, Y Zhao, Y Lin… – arXiv preprint arXiv …, 2019 – arxiv.org

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 …

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B Islam, Y Luo, S Nirjon – arXiv preprint arXiv:1905.03854, 2019 – arxiv.org

… adaptation techniques (eg, back propagation). Light-weight semi-supervised learning algorithms needs to be considered to evolve previously trained models with the incoming data stream. Deep Inference. Due to its non-linear …

**Elementary-base cirquent calculus II: Choice quantifiers**

G Japaridze – arXiv preprint arXiv:1902.07123, 2019 – arxiv.org

… MSC: primary: 03B47; secondary: 03B70; 03F03; 03F20; 68T15. Keywords: Proof theory; Cirquent calculus; Resource semantics; Deep inference; Computability logic 1 Preface Cirquent calculus is a family of deep inference (cf …

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B Gu, J Kong, A Munir, YG Kim – 2019 IEEE 25th International …, 2019 – ieeexplore.ieee.org

Page 1. A Framework for Distributed Deep Neural Network Training with Heterogeneous Computing Platforms Bontak Gu1, Joonho Kong1, Arslan Munir2, Young Geun Kim3 1School of Electronics Engineering, Kyungpook National …

**Depth information calculation method for unstructured objects based on deep neural network**

W Hu, J Rao, Z Xu, J Chen, T Wang… – … on Optical and …, 2019 – spiedigitallibrary.org

… The model parameter configuration and training method is adopted by Lina [14], which is trained on the host based on the platform NVIDIA GEFORCE GTX1080 Ti with 12G GPU memory. Fig.3(b) shows the results of deep inference of the test image by the trained model …

**On Combinatorial Proofs for Modal Logic**

UR Tre – Automated Reasoning with Analytic Tableaux and … – Springer

… The proof of soundness and completeness is based on the sequent calculus with some added features from deep inference … 3. Extended modal rules incorporating weakening on K. Fig. 4. Deep inference rules for weakening, contraction, and the t-and 4-axioms Fig …

**Deployment of Deep Learning Models to Mobile Devices for Spam Classification**

A Zainab, D Syed, D Al-Thani – 2019 IEEE First International …, 2019 – ieeexplore.ieee.org

… Although the field of implementing deep inference networks on mobile devices is new, researchers have active profound interest to convert the heavy and trained NN models into miniature models which can run in mobile devices …

**The Spinal Atomic ?-Calculus**

W Heijltjes, DR Sherratt – anupamdas.com

… We investigate the computational meaning of the following switch rule of intuitionistic deep- inference proof theory [11, 6]. (A ? B) < C s A ? (B < C) … The typing system is provided in open deduction [7], a formalism of deep inference …

**DEEP LEARNING ANALYSIS IN HEALTH CARE.**

F Vaz Machado… – Journal of Nursing …, 2019 – search.ebscohost.com

… Medical Imaging 2017 Deep Learning has proven to be a popular and powerful method in many areas of medical imaging diagnosis. E4 A deep inference learning framework for healthcare. Dai, Wang18 … Dai Y, Wang G. A deep inference learning framework for healthcare …

**Converged Deep Framework Assembling Principled Modules for CS-MRI**

R Liu, Y Zhang, S Cheng, Z Luo, X Fan – arXiv preprint arXiv:1910.13046, 2019 – arxiv.org

… sparse optimization. To tackle the challenge, our framework embraces imaging princi- ples, deep inference, and sparsity priors, either of which was proved to be critical for effective reconstruction in previous studies. Accordingly …

**KGIPSL: A Knowledge Graph Inference Method based on Probabilistic Soft Logic.**

Y Qiao, Y Wang, J Ma, X Luo… – International Journal of …, 2019 – search.ebscohost.com

… Page 2. 3210 Yaqiong Qiao, Yanjun Wang, Jiangtao Ma, Xiangyang Luo, and Huaiguang Wu • KGIPSL is beneficial to the deep inference of knowledge graphs and can better infer the relationship between uncertain entities …

**Parallel Computing in Deep Learning: Bioinformatics Case Studiesa**

V Giansanti, S Beretta, D Cesini… – 2019 27th Euromicro …, 2019 – ieeexplore.ieee.org

… and a fully-connected feed- forward deep neural network (DNN); [8] described a large- scale distributed system of tens of thousands of CPU cores for training large deep neural networks; [9] proposed K-Brain which performs deep learning and deep inference algorithms for …

**Towards Scalable Video Analytics at the Edge**

T Stone, N Stone, P Jain, Y Jiang… – 2019 16th Annual …, 2019 – ieeexplore.ieee.org

… accuracy. 3) Active Regions: While analyzing the time taken to make an inference on a video frame, we find that a significant portion of the time in a typical deep inference is spent performing convolutions on the input image volume …

**Defense against Adversarial Vision Perturbations via Subspace Diagnosis**

J Zhu, G Peng, W Fu, D Wang – 2019 Chinese Control …, 2019 – ieeexplore.ieee.org

… deep machines away from the right classes. to avoid this issue and protect the validity of deep inference, it could be necessary to consider the adversarial detection and diagnosis. table 1: Classification accuracy results before …

**Text Modeling with Syntax-Aware Variational Autoencoders**

Y Xiao, WY Wang – arXiv preprint arXiv:1908.09964, 2019 – arxiv.org

… Variational inference is used to approximate the true posterior distribu- tion with a deep inference network and training of the whole system is enabled through simple stochastic gradient descent (SGD) (Robbins and Monro 1985) …

**A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning**

S Dargan, M Kumar, MR Ayyagari, G Kumar – Archives of Computational …, 2019 – Springer

Page 1. Vol.:(0123456789) 1 3 Archives of Computational Methods in Engineering https://doi.org/10.1007/s11831-019-09344-w ORIGINAL PAPER A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning …

**Deep Poisoning Functions: Towards Robust Privacy-safe Image Data Sharing**

H Guo, B Dolhansky, E Hsin, P Dinh, S Wang… – arXiv preprint arXiv …, 2019 – arxiv.org

Page 1. arXiv:1912.06895v1 [cs.CV] 14 Dec 2019 Deep Poisoning Functions: Towards Robust Privacy-safe Image Data Sharing Hao Guo1, Brian Dolhansky2, Eric Hsin2, Phong Dinh2, Song Wang1, Cristian Canton Ferrer2 …

**Energy Efficient Training Task Assignment Scheme for Mobile Distributed Deep Learning Scenario Using DQN**

Y Liu, L Zhang, Y Wei, Z Wang – 2019 IEEE 7th International …, 2019 – ieeexplore.ieee.org

Page 1. Energy Efficient Training Task Assignment Scheme for Mobile Distributed Deep Learning Scenario Using DQN Yutong Liu†, Lianping Zhang ‡, Yifei Wei†, Zhaoying Wang† †School of Electronic Engineering, Beijing …

**Perseus: Characterizing Performance and Cost of Multi-Tenant Serving for CNN Models**

M LeMay, S Li, T Guo – arXiv preprint arXiv:1912.02322, 2019 – arxiv.org

Page 1. Perseus: Characterizing Performance and Cost of Multi-Tenant Serving for CNN Models Matthew LeMay mlemay@wpi.edu Worcester Polytechnic Institute Department of Computer Science Worcester, MA Shijian Li sli8 …

**Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks**

PD Dixit, E Lyashenko, M Niepel, D Vitkup – Cell Systems, 2019 – Elsevier

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

**A Subatomic Proof System for Decision Trees**

C Barrett, A Guglielmi – 2019 – cs.bath.ac.uk

… Deep Inference Deep inference is the ability to apply inference rules arbitrarily deep within a formula [2]. Thus, deriva- tions are composable horizontally by the same connectives as formulae, as well as in the usual vertical manner …

**Challenges of Privacy-Preserving Machine Learning in IoT**

M Zheng, D Xu, L Jiang, C Gu, R Tan… – Proceedings of the First …, 2019 – dl.acm.org

… the input. The training of ObfNet is as follows. After the pre-training of the deep inference model, we use the same training dataset to train the concatenation of the ObfNet and the pre-trained deep inference model. During the …

**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 …

**Towards deep iterative-reconstruction algorithms for computed tomography (CT) applications**

A Rajagopal, N Stier, J Dey, MA King… – … 2019: Physics of …, 2019 – spiedigitallibrary.org

… This is a well-known derivation, for perhaps one of the simplest EM formulations for emission CT. We will now show even this simple algorithm is capable of deep inference. 2.2. Deep Iterative-Reconstruction Algorithms for Computed Tomography (CT) …

**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

Page 1. Hayit Greenspan Ryutaro Tanno Marius Erdt et al.(Eds.) Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures First International Workshop, UNSURE2019 and …

**E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings**

Y Wang, Z Jiang, X Chen, P Xu, Y Zhao… – Advances in Neural …, 2019 – papers.nips.cc

Page 1. E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings Yue Wang ?, Ziyu Jiang†?, Xiaohan Chen†?, Pengfei Xu , Yang Zhao , Yingyan Lin ‡ and Zhangyang Wang†‡ †Department of Computer Science …

**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**

JGA Barbedo, LV Koenigkan, TT Santos, PM Santos – Sensors, 2019 – mdpi.com

Unmanned aerial vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive cattle farming, as production areas tend to be expansive and animals tend to be more …

**Generative Adversarial Networks for Failure Prediction**

S Zheng, A Farahat, C Gupta – arXiv preprint arXiv:1910.02034, 2019 – arxiv.org

… 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

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**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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