scholarly journals CJing: Combining Live Coding and VJing for Live Visual Performance

2021 ◽  
Author(s):  
◽  
Jack Voldemars Purvis
Keyword(s):  
Real Time ◽  
User Interfaces ◽  
Graphical User Interfaces ◽  
Visual Performance ◽  
Low Level ◽  
Fine Grained ◽  
High Level

<p>Live coding focuses on improvising content by coding in textual interfaces, but this reliance on low level text editing impairs usability by not allowing for high level manipulation of content. VJing focuses on remixing existing content with graphical user interfaces and hardware controllers, but this focus on high level manipulation does not allow for fine-grained control where content can be improvised from scratch or manipulated at a low level. This thesis proposes the code jockey practice (CJing), a new hybrid practice that combines aspects of live coding and VJing practice. In CJing, a performer known as a code jockey (CJ) interacts with code, graphical user interfaces and hardware controllers to create or manipulate real-time visuals. CJing harnesses the strengths of live coding and VJing to enable flexible performances where content can be controlled at both low and high levels. Live coding provides fine-grained control where content can be improvised from scratch or manipulated at a low level while VJing provides high level manipulation where content can be organised, remixed and interacted with. To illustrate CJing, this thesis contributes Visor, a new environment for live visual performance that embodies the practice. Visor's design is based on key ideas of CJing and a study of live coders and VJs in practice. To evaluate CJing and Visor, this thesis reflects on the usage of Visor in live performances and feedback gathered from creative coders, live coders, and VJs who experimented with the environment.</p>

scholarly journals CJing: Combining Live Coding and VJing for Live Visual Performance

2021 ◽  
Author(s):  
◽  
Jack Voldemars Purvis
Keyword(s):  
Real Time ◽  
User Interfaces ◽  
Graphical User Interfaces ◽  
Visual Performance ◽  
Low Level ◽  
Fine Grained ◽  
High Level

<p>Live coding focuses on improvising content by coding in textual interfaces, but this reliance on low level text editing impairs usability by not allowing for high level manipulation of content. VJing focuses on remixing existing content with graphical user interfaces and hardware controllers, but this focus on high level manipulation does not allow for fine-grained control where content can be improvised from scratch or manipulated at a low level. This thesis proposes the code jockey practice (CJing), a new hybrid practice that combines aspects of live coding and VJing practice. In CJing, a performer known as a code jockey (CJ) interacts with code, graphical user interfaces and hardware controllers to create or manipulate real-time visuals. CJing harnesses the strengths of live coding and VJing to enable flexible performances where content can be controlled at both low and high levels. Live coding provides fine-grained control where content can be improvised from scratch or manipulated at a low level while VJing provides high level manipulation where content can be organised, remixed and interacted with. To illustrate CJing, this thesis contributes Visor, a new environment for live visual performance that embodies the practice. Visor's design is based on key ideas of CJing and a study of live coders and VJs in practice. To evaluate CJing and Visor, this thesis reflects on the usage of Visor in live performances and feedback gathered from creative coders, live coders, and VJs who experimented with the environment.</p>

Distractor-Aware Tracking with Multi-Task and Dynamic Feature Learning

2020 ◽  
pp. 2150031
Author(s):  
Weichun Liu ◽  
Xiaoan Tang ◽  
Chenglin Zhao
Keyword(s):  
Correlation Filter ◽  
Coarse Grained ◽  
Dynamic Feature ◽  
Semantic Features ◽  
Low Level ◽  
Fine Grained ◽  
Semantic Embedding ◽  
Training Stage ◽  
Online Tracking ◽  
High Level

Recently, deep trackers based on the siamese networking are enjoying increasing popularity in the tracking community. Generally, those trackers learn a high-level semantic embedding space for feature representation but lose low-level fine-grained details. Meanwhile, the learned high-level semantic features are not updated during online tracking, which results in tracking drift in presence of target appearance variation and similar distractors. In this paper, we present a novel end-to-end trainable Convolutional Neural Network (CNN) based on the siamese network for distractor-aware tracking. It enhances target appearance representation in both the offline training stage and online tracking stage. In the offline training stage, this network learns both the low-level fine-grained details and high-level coarse-grained semantics simultaneously in a multi-task learning framework. The low-level features with better resolution are complementary to semantic features and able to distinguish the foreground target from background distractors. In the online stage, the learned low-level features are fed into a correlation filter layer and updated in an interpolated manner to encode target appearance variation adaptively. The learned high-level features are fed into a cross-correlation layer without online update. Therefore, the proposed tracker benefits from both the adaptability of the fine-grained correlation filter and the generalization capability of the semantic embedding. Extensive experiments are conducted on the public OTB100 and UAV123 benchmark datasets. Our tracker achieves state-of-the-art performance while running with a real-time frame-rate.

Adaptive user interfaces for relating high-level concepts to low-level photographic parameters

2011 ◽  
Author(s):  
Edward Scott ◽  
Pubudu Madhawa Silva ◽  
Bryan Pardo ◽  
Thrasyvoulos N. Pappas
Keyword(s):  
User Interfaces ◽  
Low Level ◽  
Adaptive User Interfaces ◽  
High Level

iQ-Check Listeria spp. Real-Time PCR Test Kit

2013 ◽  
Vol 96 (3) ◽  
pp. 508-515
Author(s):  
Wendy F Lauer ◽  
Jean-Philippe Tourniaire
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Reference Method ◽  
Sampling Area ◽  
Fermented Sausage ◽  
Low Level ◽  
Test Kit ◽  
Significant Difference ◽  
High Level ◽  
Health Canada

Abstract A comparative evaluation study of the Bio-Rad® iQ-Check™Listeria species Kit (Bio-Rad Laboratories, Hercules, CA) was conducted at Q Laboratories, Inc., Cincinnati, OH. iQ-Check is a rapid method based on real-time PCR amplification and detection of all species of Listeria, including L. grayi, in food and environmental samples. The iQ-Check method was compared to the Health Canada MFHPB-30 reference method for the analysis of five ready-to-eat meats—deli turkey, hot dogs, liver paté, raw fermented sausage, and deli ham—and one stainless steel surface. Each food matrix was analyzed at two contamination levels: a low level at 0.2–2 CFU/25 g and a high level at 2–5 CFU/25 g. The environmental surfaces were analyzed at a low level of 0.2–2 CFU/5 cm2 sampling area and a high level of 2–5 CFU/5 cm2 sampling area. There were 20 replicates per contamination level and five control replicates at 0 CFU/25 g or 0 CFU/5 cm2 sampling area (uninoculated). All samples that were detected by iQ-Check were subsequently confirmed by reference method protocol. There was no significant difference in the number of positive samples detected by the iQ-Check Listeria spp. Kit in comparison to the Health Canada MFHPB-30 method for all matrixes tested.

scholarly journals Affective Multimodal Control of Virtual Characters

2020 ◽  
Vol 6 (4) ◽  
pp. 43-54 ◽  
Author(s):  
Martin Klesen ◽  
Patrick Gebhard
Keyword(s):  
Real Time ◽  
The Body ◽  
Temporal Constraints ◽  
Virtual Characters ◽  
Fine Grained ◽  
Novel Approach ◽  
Control Body ◽  
High Level ◽  
The Mind ◽  
Selection Of

In this paper we report about the use of computer generated affect to control body and mind of cognitively modeled virtual characters. We use the computational model of affect ALMA that is able to simulate three different affect types in real-time. The computation of affect is based on a novel approach of an appraisal language. Both the use of elements of the appraisal language and the simulation of different affect types has been evaluated. Affect is used to control facial expressions, facial complexions, affective animations, posture, and idle behavior on the body layer and the selection of dialogue strategies on the mind layer. To enable a fine-grained control of these aspects a Player Markup Language (PML) has been developed. The PML is player-independent and allows a sophisticated control of character actions coordinated by high-level temporal constraints. An Action Encoder module maps the output of ALMA to PML actions using affect display rules. These actions drive the real-time rendering of affect, gesture and speech parameters of virtual characters, which we call Virtual Humans. 

Low-level copy gain versus amplification of myc oncogenes in medulloblastoma: utility in predicting prognosis and survival

2009 ◽  
Vol 3 (1) ◽  
pp. 61-65 ◽  
Author(s):  
Hidehiro Takei ◽  
Yummy Nguyen ◽  
Vidya Mehta ◽  
Murali Chintagumpala ◽  
Robert C. Dauser ◽  
...  
Keyword(s):  
In Situ Hybridization ◽  
Real Time ◽  
Real Time Pcr ◽  
Large Cell ◽  
Histological Subtype ◽  
Quantitative Real Time Pcr ◽  
Low Level ◽  
Kaplan Meier ◽  
High Level

Object Medulloblastoma (MB) is a malignant embryonal tumor of the cerebellum. Amplification of c-myc or N-myc is infrequently identified and, when present, is often associated with the large cell/anaplastic (LC/A) phenotype. The frequency of low-level copy gain of myc oncogenes and its relationship to prognosis of MB has not been explored. Methods Archival cases of MB were histologically reviewed and classified into 3 major subtypes: classic, nodular, and LC/A. Using quantitative real-time polymerase chain reaction (PCR), the authors analyzed 58 cases with a pure histological subtype for the copy number (CN) of myc (c-myc and N-myc) oncogenes. Cases with > 5-fold CN were further analyzed using the fluorescent in situ hybridization (FISH) assay. Kaplan-Meier survival analysis was performed. Results A > 5-fold myc CN was noted in 5 (20.8%) of 24 LC/A, 1 (5.3%) of 19 classic, and 2 (13.3%) of 15 nodular subtypes. In a significant number of tumors (14 [56%] of 24 LC/A, 13 [68%] of 19 classic, and 10 [67%] of 15 nodular MBs) the CN was > 2-fold but < 5-fold. High-level amplification, defined as > 10-fold CN, was only seen in the LC/A subtype (5 cases), although moderate amplification (> 5-fold but < 10-fold) could be detected in other histological subtypes. Fluorescence in situ hybridization readily detected most cases corresponding to tumors with > 5-fold amplicon CN by quantitative real-time PCR, and could detect all 5 cases with > 10-fold CN by quantitative real-time PCR. The group of patients with > 5-fold myc amplicon CN showed significantly shorter survival than those with < 5-fold CN (p = 0.045), independent of histological subtype. Conclusions Since FISH could easily detect most cases in the moderate-to-high myc gene amplification (> 5-fold CN) group, the FISH assay has utility in detecting subsets of MB with poorer prognosis.

scholarly journals Spatiotemporal Prediction of Theft Risk with Deep Inception-Residual Networks

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 204-216
Author(s):  
Xinyue Ye ◽  
Lian Duan ◽  
Qiong Peng
Keyword(s):  
New York ◽  
New York City ◽  
Emergency Service ◽  
Prediction Models ◽  
Smart Cities ◽  
Low Level ◽  
Fine Grained ◽  
Crime Prediction ◽  
High Level ◽  
Better Than

Spatiotemporal prediction of crime is crucial for public safety and smart cities operation. As crime incidents are distributed sparsely across space and time, existing deep-learning methods constrained by coarse spatial scale offer only limited values in prediction of crime density. This paper proposes the use of deep inception-residual networks (DIRNet) to conduct fine-grained, theft-related crime prediction based on non-emergency service request data (311 events). Specifically, it outlines the employment of inception units comprising asymmetrical convolution layers to draw low-level spatiotemporal dependencies hidden in crime events and complaint records in the 311 dataset. Afterward, this paper details how residual units can be applied to capture high-level spatiotemporal features from low-level spatiotemporal dependencies for the final prediction. The effectiveness of the proposed DIRNet is evaluated based on theft-related crime data and 311 data in New York City from 2010 to 2015. The results confirm that the DIRNet obtains an average F1 of 71%, which is better than other prediction models.

IoT-based framework for performance measurement

2017 ◽  
Vol 117 (4) ◽  
pp. 688-712 ◽  
Author(s):  
Mahdi Rezaei ◽  
Mohsen Akbarpour Shirazi ◽  
Behrooz Karimi
Keyword(s):  
Supply Chain ◽  
Performance Measurement ◽  
Real Time ◽  
Machine Intelligence ◽  
Strategic Decision ◽  
Operational Decisions ◽  
Content Type ◽  
Low Level ◽  
Multi Objective ◽  
High Level

Purpose The purpose of this paper is to develop an Internet of Things (IoT)-based framework for supply chain (SC) performance measurement and real-time decision alignment. The aims of the proposed model are to optimize the performance indicator based on integrated supply chain operations reference metrics. Design/methodology/approach The SC multi-dimensional structure is modeled by multi-objective optimization methods. The operational presented model considers important SC features thoroughly such as multi-echelons, several suppliers, several manufacturers and several products during multiple periods. A multi-objective mathematical programming model is then developed to yield the operational decisions with Pareto efficient performance values and solved using a well-known meta-heuristic algorithm, i.e., non-dominated sorting genetic algorithm II. Afterward, Technique for Order of Preference by Similarity to Ideal Solution method is used to determine the best operational solution based on the strategic decision maker’s idea. Findings This paper proposes a dynamic integrated solution for three main problems: strategic decisions in high level, operational decisions in low level and alignment of these two decision levels. Originality/value The authors propose a human intelligence-based process for high level decision and machine intelligence-based decision support systems for low level decision using a novel approach. High level and low level decisions are aligned by a machine intelligence model as well. The presented framework is based on change detection, event driven planning and real-time decision alignment.

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