109 research outputs found
Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations
This research focuses on assessing the ability of large language models
(LLMs) in representing geometries and their spatial relations. We utilize LLMs
including GPT-2 and BERT to encode the well-known text (WKT) format of
geometries and then feed their embeddings into classifiers and regressors to
evaluate the effectiveness of the LLMs-generated embeddings for geometric
attributes. The experiments demonstrate that while the LLMs-generated
embeddings can preserve geometry types and capture some spatial relations (up
to 73% accuracy), challenges remain in estimating numeric values and retrieving
spatially related objects. This research highlights the need for improvement in
terms of capturing the nuances and complexities of the underlying geospatial
data and integrating domain knowledge to support various GeoAI applications
using foundation models
Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations (Short Paper)
This research focuses on assessing the ability of large language models (LLMs) in representing geometries and their spatial relations. We utilize LLMs including GPT-2 and BERT to encode the well-known text (WKT) format of geometries and then feed their embeddings into classifiers and regressors to evaluate the effectiveness of the LLMs-generated embeddings for geometric attributes. The experiments demonstrate that while the LLMs-generated embeddings can preserve geometry types and capture some spatial relations (up to 73% accuracy), challenges remain in estimating numeric values and retrieving spatially related objects. This research highlights the need for improvement in terms of capturing the nuances and complexities of the underlying geospatial data and integrating domain knowledge to support various GeoAI applications using foundation models
Eating your way to integration: the making of a diverse community at LSE
This research paper examines the relationship between the diversity of food consumption and the degree of students' integration within LSE. We found through statistical analysis that there is a strong, positive correlation between the diversity of students’ choice of food and their degree of integration into the student community
Eating your way to integration: the making of a diverse community at LSE
This research paper examines the relationship between the diversity of food consumption and the degree of students' integration within LSE. We found through statistical analysis that there is a strong, positive correlation between the diversity of students’ choice of food and their degree of integration into the student community
Situating Media Infrastructure: Understand the Role of Public Space Characteristics in Influencing Public Interaction with Media Infrastructure
Media Architecture scholars have outlined the importance of considering the urban design perspective in informing the deployment of digital media in public space. In this paper, we build on their work and provide a detailed account based on the knowledge from urban design theories coupled with literature from Human-computer Interaction research. Specifically, we address the role of location- its physical and spatial characteristics and situated human activities- in influencing public interaction with media infrastructure. We aim to provide a framework for understanding the complex relationship between media infrastructure and urban public spaces, and explore the impact of locations on how people interact with media infrastructure by: 1) developing an initial framework of public space characteristics based on urban design knowledge, 2) conducting a case study of InLinkUK network with detailed field study and analysis on 3 selected sites in London. We discuss the initial outcome of the case study analysis and report on the next stages of this research. This paper addresses the question: how media architecture can contribute to a sense of place and provide a detailed account based on a case study in London. It attempts to broaden and extend existing calls by media architecture scholars to consider urban design knowledge in informing the deployment of digital media infrastructure in public spaces
Multi-Modal Wireless Flexible Gel-Free Sensors with Edge Deep Learning for Detecting and Alerting Freezing of Gait in Parkinson's Patients
Freezing of gait (FoG) is a debilitating symptom of Parkinson's disease (PD).
This work develops flexible wearable sensors that can detect FoG and alert
patients and companions to help prevent falls. FoG is detected on the sensors
using a deep learning (DL) model with multi-modal sensory inputs collected from
distributed wireless sensors. Two types of wireless sensors are developed,
including: (1) a C-shape central node placed around the patient's ears, which
collects electroencephalogram (EEG), detects FoG using an on-device DL model,
and generates auditory alerts when FoG is detected; (2) a stretchable
patch-type sensor attached to the patient's legs, which collects
electromyography (EMG) and movement information from accelerometers. The
patch-type sensors wirelessly send collected data to the central node through
low-power ultra-wideband (UWB) transceivers. All sensors are fabricated on
flexible printed circuit boards. Adhesive gel-free acetylene carbon black and
polydimethylsiloxane electrodes are fabricated on the flexible substrate to
allow conformal wear over the long term. Custom integrated circuits (IC) are
developed in 180 nm CMOS technology and used in both types of sensors for
signal acquisition, digitization, and wireless communication. A novel
lightweight DL model is trained using multi-modal sensory data. The inference
of the DL model is performed on a low-power microcontroller in the central
node. The DL model achieves a high detection sensitivity of 0.81 and a
specificity of 0.88. The developed wearable sensors are ready for clinical
experiments and hold great promise in improving the quality of life of patients
with PD. The proposed design methodologies can be used in wearable medical
devices for the monitoring and treatment of a wide range of neurodegenerative
diseases
Bringing Spatial Interaction Measures into Multi-Criteria Assessment of Redistricting Plans Using Interactive Web Mapping
Redistricting is the process by which electoral district boundaries are
drawn, and a common normative assumption in this process is that districts
should be drawn so as to capture coherent communities of interest (COIs). While
states rely on various proxies for community illustration, such as compactness
metrics and municipal split counts, to guide redistricting, recent legal
challenges and scholarly works have shown the failings of such proxy measures
and the difficulty of balancing multiple criteria in district plan creation. To
address these issues, we propose the use of spatial interaction communities to
directly quantify the degree to which districts capture the underlying COIs.
Using large-scale human mobility flow data, we condense spatial interaction
community capture for a set of districts into a single number, the interaction
ratio (IR), which can be used for redistricting plan evaluation. To compare the
IR to traditional redistricting criteria (compactness and fairness), and to
explore the range of IR values found in valid districting plans, we employ a
Markov chain-based regionalization algorithm (ReCom) to produce ensembles of
valid plans, and calculate the degree to which they capture spatial interaction
communities. Furthermore, we propose two methods for biasing the ReCom
algorithm towards different IR values. We perform a multi-criteria assessment
of the space of valid maps, and present the results in an interactive web map.
The experiments on Wisconsin congressional districting plans demonstrate the
effectiveness of our methods for biasing sampling towards higher or lower IR
values. Furthermore, the analysis of the districts produced with these methods
suggests that districts with higher IR and compactness values tend to produce
district plans that are more proportional with regards to seats allocated to
each of the two major parties.Comment: 12 figure
DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations
In open-domain dialogue generation tasks, contexts and responses in most
datasets are one-to-one mapped, violating an important many-to-many
characteristic: a context leads to various responses, and a response answers
multiple contexts. Without such patterns, models poorly generalize and prefer
responding safely. Many attempts have been made in either multi-turn settings
from a one-to-many perspective or in a many-to-many perspective but limited to
single-turn settings. The major challenge to many-to-many augment multi-turn
dialogues is that discretely replacing each turn with semantic similarity
breaks fragile context coherence. In this paper, we propose DialoGue Path
Sampling (DialoGPS) method in continuous semantic space, the first many-to-many
augmentation method for multi-turn dialogues. Specifically, we map a dialogue
to our extended Brownian Bridge, a special Gaussian process. We sample latent
variables to form coherent dialogue paths in the continuous space. A dialogue
path corresponds to a new multi-turn dialogue and is used as augmented training
data. We show the effect of DialoGPS with both automatic and human evaluation.Comment: ACL 2023 mai
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