4,013 research outputs found
Conformal Magnetic Composite RFID for Wearable RF and Bio-Monitoring Applications
©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.10.1109/TMTT.2008.2006810This paper introduces for the first time a novel flexible magnetic composite material for RF identification (RFID) and wearable RF antennas. First, one conformal RFID tag working at 480 MHz is designed and fabricated as a benchmarking prototype and the miniaturization concept is verified. Then, the impact of the material is thoroughly investigated using a hybrid method involving electromagnetic and statistical tools. Two separate statistical experiments are performed, one for the analysis of the impact of the relative permittivity and permeability of the proposed material and the other for the evaluation of the impact of the dielectric and magnetic loss on the antenna performance. Finally, the effect of the bending of the antenna is investigated, both on the S-parameters and on the radiation pattern. The successful implementation of the flexible magnetic composite material enables the significant miniaturization of RF passives and antennas in UHF frequency bands, especially when conformal modules that can be easily fine-tuned are required in critical biomedical and pharmaceutical applications
Controllable Neural Story Plot Generation via Reinforcement Learning
Language-modeling--based approaches to story plot generation attempt to
construct a plot by sampling from a language model (LM) to predict the next
character, word, or sentence to add to the story. LM techniques lack the
ability to receive guidance from the user to achieve a specific goal, resulting
in stories that don't have a clear sense of progression and lack coherence. We
present a reward-shaping technique that analyzes a story corpus and produces
intermediate rewards that are backpropagated into a pre-trained LM in order to
guide the model towards a given goal. Automated evaluations show our technique
can create a model that generates story plots which consistently achieve a
specified goal. Human-subject studies show that the generated stories have more
plausible event ordering than baseline plot generation techniques.Comment: Published in IJCAI 201
Event Representations for Automated Story Generation with Deep Neural Nets
Automated story generation is the problem of automatically selecting a
sequence of events, actions, or words that can be told as a story. We seek to
develop a system that can generate stories by learning everything it needs to
know from textual story corpora. To date, recurrent neural networks that learn
language models at character, word, or sentence levels have had little success
generating coherent stories. We explore the question of event representations
that provide a mid-level of abstraction between words and sentences in order to
retain the semantic information of the original data while minimizing event
sparsity. We present a technique for preprocessing textual story data into
event sequences. We then present a technique for automated story generation
whereby we decompose the problem into the generation of successive events
(event2event) and the generation of natural language sentences from events
(event2sentence). We give empirical results comparing different event
representations and their effects on event successor generation and the
translation of events to natural language.Comment: Submitted to AAAI'1
CoRRPUS: Codex-Leveraged Structured Representations for Neurosymbolic Story Understanding
Story generation and understanding -- as with all NLG/NLU tasks -- has seen a
surge in neurosymbolic work. Researchers have recognized that, while large
language models (LLMs) have tremendous utility, they can be augmented with
symbolic means to be even better and to make up for any flaws that the neural
networks might have. However, symbolic methods are extremely costly in terms of
the amount of time and expertise needed to create them. In this work, we
capitalize on state-of-the-art Code-LLMs, such as Codex, to bootstrap the use
of symbolic methods for tracking the state of stories and aiding in story
understanding. We show that our CoRRPUS system and abstracted prompting
procedures can beat current state-of-the-art structured LLM techniques on
pre-existing story understanding tasks (bAbI task 2 and Re^3) with minimal hand
engineering. We hope that this work can help highlight the importance of
symbolic representations and specialized prompting for LLMs as these models
require some guidance for performing reasoning tasks properly.Comment: Accepted to Findings of ACL 202
A First Order Analytical Solution for Spacecraft Motion about (433)Eros
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76061/1/AIAA-2002-4543-167.pd
Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community
In the r/AmITheAsshole subreddit, people anonymously share first person
narratives that contain some moral dilemma or conflict and ask the community to
judge who is at fault (i.e., who is "the asshole"). In general, first person
narratives are a unique storytelling domain where the author is the narrator
(the person telling the story) but can also be a character (the person living
the story) and, thus, the author has two distinct voices presented in the
story. In this study, we identify linguistic and narrative features associated
with the author as the character or as a narrator. We use these features to
answer the following questions: (1) what makes an asshole character and (2)
what makes an asshole narrator? We extract both Author-as-Character features
(e.g., demographics, narrative event chain, and emotional arc) and
Author-as-Narrator features (i.e., the style and emotion of the story as a
whole) in order to identify which aspects of the narrative are correlated with
the final moral judgment. Our work shows that "assholes" as Characters frame
themselves as lacking agency with a more positive personal arc, while
"assholes" as Narrators will tell emotional and opinionated stories.Comment: Accepted to the 17th International AAAI Conference on Web and Social
Media (ICWSM), 202
Planar polymer waveguides with a graded-index profile resulting from intermixing of methacrylates in closed microchannels
Graded-index waveguides are known to exhibit lower losses and considerably larger bandwidths compared to step-index waveguides. The present work reports on a new concept for realizing such waveguides on a planar substrate by capillary filling microchannels (cladding) with monomer solution (core). A graded-index profile is obtained by intermi xing between the core and cladding material at the microchannel interface. To this end, various ratios of methyl methacrylate (MMA) and octafluoropentyl methacrylate (OFPMA) were evaluated as starting monomers and the results showed that the polymers P50:50 (50:50 MMA:OFPMA) and P0:100 (100% OFPMA) were suitable to be applied as waveguide core and cladding material respectively. Light guiding in the resulting P50:50/P0:100 waveguides was demonstrated and the refractive-index profile was quantified and compared with that of conventional step-index waveguides. The results for both cases were clearly different and a gradual refractive index transition between the core and cladding was found for the newly developed waveguides. Although the concept has been demonstrated in a research environment, it also has potential for upscaling by employing drop-on-demand dispensing of polymer waveguide material in pre-patterned microchannels, for example in a roll-to-roll environment
CALYPSO: LLMs as Dungeon Masters' Assistants
The role of a Dungeon Master, or DM, in the game Dungeons & Dragons is to
perform multiple tasks simultaneously. The DM must digest information about the
game setting and monsters, synthesize scenes to present to other players, and
respond to the players' interactions with the scene. Doing all of these tasks
while maintaining consistency within the narrative and story world is no small
feat of human cognition, making the task tiring and unapproachable to new
players. Large language models (LLMs) like GPT-3 and ChatGPT have shown
remarkable abilities to generate coherent natural language text. In this paper,
we conduct a formative evaluation with DMs to establish the use cases of LLMs
in D&D and tabletop gaming generally. We introduce CALYPSO, a system of
LLM-powered interfaces that support DMs with information and inspiration
specific to their own scenario. CALYPSO distills game context into bite-sized
prose and helps brainstorm ideas without distracting the DM from the game. When
given access to CALYPSO, DMs reported that it generated high-fidelity text
suitable for direct presentation to players, and low-fidelity ideas that the DM
could develop further while maintaining their creative agency. We see CALYPSO
as exemplifying a paradigm of AI-augmented tools that provide synchronous
creative assistance within established game worlds, and tabletop gaming more
broadly.Comment: 11 pages, 4 figures. AIIDE 202
Web Service Discovery in a Semantically Extended UDDI Registry: the Case of FUSION
Service-oriented computing is being adopted at an unprecedented rate, making the effectiveness of automated service discovery an increasingly important challenge. UDDI has emerged as a de facto industry standard and fundamental building block within SOA infrastructures. Nevertheless, conventional UDDI registries lack means to provide unambiguous, semantically rich representations of Web service capabilities, and the logic inference power required for facilitating automated service discovery. To overcome this important limitation, a number of approaches have been proposed towards augmenting Web service discovery with semantics. This paper discusses the benefits of semantically extending Web service descriptions and UDDI registries, and presents an overview of the approach put forward in project FUSION, towards semantically-enhanced publication and discovery of services based on SAWSDL
FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information
Dungeons & Dragons (D&D) is a tabletop roleplaying game with complex natural
language interactions between players and hidden state information. Recent work
has shown that large language models (LLMs) that have access to state
information can generate higher quality game turns than LLMs that use dialog
history alone. However, previous work used game state information that was
heuristically created and was not a true gold standard game state. We present
FIREBALL, a large dataset containing nearly 25,000 unique sessions from real
D\&D gameplay on Discord with true game state info. We recorded game play
sessions of players who used the Avrae bot, which was developed to aid people
in playing D&D online, capturing language, game commands and underlying game
state information. We demonstrate that FIREBALL can improve natural language
generation (NLG) by using Avrae state information, improving both automated
metrics and human judgments of quality. Additionally, we show that LLMs can
generate executable Avrae commands, particularly after finetuning.Comment: 21 pages, 2 figures. Accepted at ACL 202
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