3,919 research outputs found
Designing a mobile augmented memory system for people with traumatic brain injuries
Augmented memory systems help people remember events in their lives. Individuals with Traumatic Brain Injury (TBI) often have memory impairments. We conducted a user study to learn about strategies individuals with TBI use to remember events in their lives. We explored what characteristics individuals with TBI expect of an augmented memory system. We then investigated these aspects in an initial mobile app design, and propose here a concept for a rehearsal application that addresses the issues found in our studies
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-Thought
Unsupervised sentence representation learning aims to transform input
sentences into fixed-length vectors enriched with intricate semantic
information while obviating the reliance on labeled data. Recent progress
within this field, propelled by contrastive learning and prompt engineering,
has significantly bridged the gap between unsupervised and supervised
strategies. Nonetheless, the potential utilization of Chain-of-Thought, remains
largely untapped within this trajectory. To unlock latent capabilities within
pre-trained models, such as BERT, we propose a two-stage approach for sentence
representation: comprehension and summarization. Subsequently, the output of
the latter phase is harnessed as the vectorized representation of the input
sentence. For further performance enhancement, we meticulously refine both the
contrastive learning loss function and the template denoising technique for
prompt engineering. Rigorous experimentation substantiates our method,
CoT-BERT, transcending a suite of robust baselines without necessitating other
text representation models or external databases
Zero-shot Text-to-SQL Learning with Auxiliary Task
Recent years have seen great success in the use of neural seq2seq models on
the text-to-SQL task. However, little work has paid attention to how these
models generalize to realistic unseen data, which naturally raises a question:
does this impressive performance signify a perfect generalization model, or are
there still some limitations?
In this paper, we first diagnose the bottleneck of text-to-SQL task by
providing a new testbed, in which we observe that existing models present poor
generalization ability on rarely-seen data. The above analysis encourages us to
design a simple but effective auxiliary task, which serves as a supportive
model as well as a regularization term to the generation task to increase the
models generalization. Experimentally, We evaluate our models on a large
text-to-SQL dataset WikiSQL. Compared to a strong baseline coarse-to-fine
model, our models improve over the baseline by more than 3% absolute in
accuracy on the whole dataset. More interestingly, on a zero-shot subset test
of WikiSQL, our models achieve 5% absolute accuracy gain over the baseline,
clearly demonstrating its superior generalizability
Modulation Design and Optimization for RIS-Assisted Symbiotic Radios
In reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR),
the RIS acts as a secondary transmitter by modulating its information bits over
the incident primary signal and simultaneously assists the primary
transmission, then a cooperative receiver is used to jointly decode the primary
and secondary signals. Most existing works of SR focus on using RIS to enhance
the reflecting link while ignoring the ambiguity problem for the joint
detection caused by the multiplication relationship of the primary and
secondary signals. Particularly, in case of a blocked direct link, joint
detection will suffer from severe performance loss due to the ambiguity, when
using the conventional on-off keying and binary phase shift keying modulation
schemes for RIS. To address this issue, we propose a novel modulation scheme
for RIS-assisted SR that divides the phase-shift matrix into two components:
the symbol-invariant and symbol-varying components, which are used to assist
the primary transmission and carry the secondary signal, respectively. To
design these two components, we focus on the detection of the composite signal
formed by the primary and secondary signals, through which a problem of
minimizing the bit error rate (BER) of the composite signal is formulated to
improve both the BER performance of the primary and secondary ones. By solving
the problem, we derive the closed-form solution of the optimal symbol-invariant
and symbol-varying components, which is related to the channel strength ratio
of the direct link to the reflecting link. Moreover, theoretical BER
performance is analyzed. Finally, simulation results show the superiority of
the proposed modulation scheme over its conventional counterpart.Comment: 16 pages,15 figure
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