User experiments with the Eurovision cross-language image retrieval system

In this paper we present Eurovision, a text-based system for cross-language (CL) image retrieval. The system is evaluated by multilingual users for two search tasks with the system configured in English and five other languages. To our knowledge this is the first published set of user experiments for CL image retrieval. We show that: (1) it is possible to create a usable multilingual search engine using little knowledge of any language other than English, (2) categorizing images assists the user's search, and (3) there are differences in the way users search between the proposed search tasks. Based on the two search tasks and user feedback, we describe important aspects of any CL image retrieval system

Measurement of charged particle yields from therapeutic beams in view of the design of an innovative hadrontherapy dose monitor

Particle Therapy (PT) is an emerging technique, which makes use of charged particles to efficiently cure different kinds of solid tumors. The high precision in the hadrons dose deposition requires an accurate monitoring to prevent the risk of under-dosage of the cancer region or of over-dosage of healthy tissues. Monitoring techniques are currently being developed and are based on the detection of particles produced by the beam interaction into the target, in particular: charged particles, result of target and/or projectile fragmentation, prompt photons coming from nucleus de-excitation and back-to-back γ s, produced in the positron annihilation from β + emitters created in the beam interaction with the target. It has been showed that the hadron beam dose release peak can be spatially correlated with the emission pattern of these secondary particles. Here we report about secondary particles production (charged fragments and prompt γ s) performed at different beam and energies that have a particular relevance for PT applications: 12C beam of 80 MeV/u at LNS, 12C beam 220 MeV/u at GSI, and 12C, 4He, 16O beams with energy in the 50–300 MeV/u range at HIT. Finally, a project for a multimodal dose-monitor device exploiting the prompt photons and charged particles emission will be presented

Community Health Indicators in Southern Nevada

Community design and access to services are essential components of healthy and sustainable communities. The purpose of this manuscript is to evaluate Southern Nevada with respect to community design and access, including both positive and negative traits, and to suggest realistic changes that could be made to improve these conditions. The region’s network of parks and open space recreation areas is one of its strongest assets. Clark County enjoys over 42 million acres of federal and state lands which offer a large variety of recreational opportunities. The region has an extensive trail system, with a total of 179 miles of off road and multiuse trails, as well as over 300 miles of biking infrastructure. There are 39 recreational facilities and 24 libraries throughout the region. There are, however, fewer park acres per capita than the nationally recommended level and disparate access to those parks for low income census tracts. Southern Nevada has some significant issues related to food access, with 16 food deserts in Clark County and over 17% of the population, and 26.9% of children, experiencing food insecurity. There are a total of 289 grocery stores, supermarkets, and club stores, 593 convenience stores, and 1,089 fast food outlets (USDA ERS, 2012). Of all restaurants in Clark County, 59% are classified as fast food. In 2012 Nevada ranked second in the nation for violent crimes and Clark County ranked third within the state. Based on the existing conditions, a number of goals and strategies aimed at creating a healthy and sustainable community were developed as part of the Southern Nevada Regional Plan for Sustainable Development (SNvRPSD); a single, integrated and consolidated plan that will promote and guide sustainable regional development in Southern Nevada over the next 20 years

Unmasking Deception: Empowering Deepfake Detection with Vision Transformer Network

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project no. (IFKSUOR3–057-3).Peer reviewedPublisher PD

Relativizing truth of future-tensed sentences

International audienceIn MacFarlane (2003, 2008) an argument is presented to the effect that a context of assessment should be recognized in its own right besides the context of utterance. His argument is based on certain intuitions we are said to have about the truth-status of future contingent statements such as (1), uttered in a context where physical symmetry of the coin is presupposed. (1) The coin will land heads up. MacFarlane's first claim is that we have an "indeterminacy" intuition, according to which our utterance of (1) is neither true nor false at the time that we make it. His further claim is that we have a "determinacy" intuition, according to which the same utterance of (1) is taken as determinately true (or determinately false) once the actual course of events has settled the issue. On this basis, MacFarlane argues that the standard Kaplanian notion of truth-in-context should be revised so as to allow for its relativization to a context of assessment, besides its original relativization to the context of utterance. Bonomi and Del Prete (2008) agree with MacFarlane on the two contrasting intuitions about the truth-status of future contingents, but they develop a framework in which both intuitions are accounted for without relativizing utterance-truth to contexts of assessment, as constructs theoretically distinct from Kaplanian contexts of utterance. In these notes, I take up one of their philosophical points against relativistic semantics in the style of MacFarlane, and elaborate it in further details. I also propose a hypothesis for a semantic analysis of the English future tense auxiliary 'will'

Cyber-Physical Codesign of Distributed Structural Health Monitoring with Wireless Sensor Networks

Our Deteriorating Civil Infrastructure Faces the Critical Challenge of Long-Term Structural Health Monitoring for Damage Detection and Localization. in Contrast to Existing Research that Often Separates the Designs of Wireless Sensor Networks and Structural Engineering Algorithms, This Paper Proposes a Cyber-Physical Co-Design Approach to Structural Health Monitoring based on Wireless Sensor Networks. Our Approach Closely Integrates (1) Flexibility-Based Damage Localization Methods that Allow a Tradeoff between the Number of Sensors and the Resolution of Damage Localization, and (2) an Energy-Efficient, Multi-Level Computing Architecture Specifically Designed to Leverage the Multi-Resolution Feature of the Flexibility-Based Approach. the Proposed Approach Has Been Implemented on the Intel Imote2 Platform. Experiments on a Physical Beam and Simulations of a Truss Structure Demonstrate the System\u27s Efficacy in Damage Localization and Energy Efficiency. © 2010 ACM

ADD: An Automatic Desensitization Fisheye Dataset for Autonomous Driving

Autonomous driving systems require many images for analyzing the surrounding environment. However, there is fewer data protection for private information among these captured images, such as pedestrian faces or vehicle license plates, which has become a significant issue. In this paper, in response to the call for data security laws and regulations and based on the advantages of large Field of View(FoV) of the fisheye camera, we build the first Autopilot Desensitization Dataset, called ADD, and formulate the first deep-learning-based image desensitization framework, to promote the study of image desensitization in autonomous driving scenarios. The compiled dataset consists of 650K images, including different face and vehicle license plate information captured by the surround-view fisheye camera. It covers various autonomous driving scenarios, including diverse facial characteristics and license plate colors. Then, we propose an efficient multitask desensitization network called DesCenterNet as a benchmark on the ADD dataset, which can perform face and vehicle license plate detection and desensitization tasks. Based on ADD, we further provide an evaluation criterion for desensitization performance, and extensive comparison experiments have verified the effectiveness and superiority of our method on image desensitization