533 research outputs found
Extreme Learning Machine Based Non-Iterative and Iterative Nonlinearity Mitigation for LED Communications
This work concerns receiver design for light emitting diode (LED)
communications where the LED nonlinearity can severely degrade the performance
of communications. We propose extreme learning machine (ELM) based
non-iterative receivers and iterative receivers to effectively handle the LED
nonlinearity and memory effects. For the iterative receiver design, we also
develop a data-aided receiver, where data is used as virtual training sequence
in ELM training. It is shown that the ELM based receivers significantly
outperform conventional polynomial based receivers; iterative receivers can
achieve huge performance gain compared to non-iterative receivers; and the
data-aided receiver can reduce training overhead considerably. This work can
also be extended to radio frequency communications, e.g., to deal with the
nonlinearity of power amplifiers
Compositional Exemplars for In-context Learning
Large pretrained language models (LMs) have shown impressive In-Context
Learning (ICL) ability, where the model learns to do an unseen task via a
prompt consisting of input-output examples as the demonstration, without any
parameter updates. The performance of ICL is highly dominated by the quality of
the selected in-context examples. However, previous selection methods are
mostly based on simple heuristics, leading to sub-optimal performance. In this
work, we formulate in-context example selection as a subset selection problem.
We propose CEIL (Compositional Exemplars for In-context Learning), which is
instantiated by Determinantal Point Processes (DPPs) to model the interaction
between the given input and in-context examples, and optimized through a
carefully-designed contrastive learning objective to obtain preference from
LMs. We validate CEIL on 12 classification and generation datasets from 7
distinct NLP tasks, including sentiment analysis, paraphrase detection, natural
language inference, commonsense reasoning, open-domain question answering, code
generation, and semantic parsing. Extensive experiments demonstrate not only
the state-of-the-art performance but also the transferability and
compositionality of CEIL, shedding new light on effective and efficient
in-context learning. Our code is released at
https://github.com/HKUNLP/icl-ceil.Comment: Accepted in ICML 202
DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models
Recently, diffusion models have emerged as a new paradigm for generative
models. Despite the success in domains using continuous signals such as vision
and audio, adapting diffusion models to natural language is under-explored due
to the discrete nature of texts, especially for conditional generation. We
tackle this challenge by proposing DiffuSeq: a diffusion model designed for
sequence-to-sequence (Seq2Seq) text generation tasks. Upon extensive evaluation
over a wide range of Seq2Seq tasks, we find DiffuSeq achieving comparable or
even better performance than six established baselines, including a
state-of-the-art model that is based on pre-trained language models. Apart from
quality, an intriguing property of DiffuSeq is its high diversity during
generation, which is desired in many Seq2Seq tasks. We further include a
theoretical analysis revealing the connection between DiffuSeq and
autoregressive/non-autoregressive models. Bringing together theoretical
analysis and empirical evidence, we demonstrate the great potential of
diffusion models in complex conditional language generation tasks. Code is
available at \url{https://github.com/Shark-NLP/DiffuSeq}Comment: ICLR 2023 camera read
Text detection and recognition based on a lensless imaging system
Lensless cameras are characterized by several advantages (e.g.,
miniaturization, ease of manufacture, and low cost) as compared with
conventional cameras. However, they have not been extensively employed due to
their poor image clarity and low image resolution, especially for tasks that
have high requirements on image quality and details such as text detection and
text recognition. To address the problem, a framework of deep-learning-based
pipeline structure was built to recognize text with three steps from raw data
captured by employing lensless cameras. This pipeline structure consisted of
the lensless imaging model U-Net, the text detection model connectionist text
proposal network (CTPN), and the text recognition model convolutional recurrent
neural network (CRNN). Compared with the method focusing only on image
reconstruction, UNet in the pipeline was able to supplement the imaging details
by enhancing factors related to character categories in the reconstruction
process, so the textual information can be more effectively detected and
recognized by CTPN and CRNN with fewer artifacts and high-clarity reconstructed
lensless images. By performing experiments on datasets of different
complexities, the applicability to text detection and recognition on lensless
cameras was verified. This study reasonably demonstrates text detection and
recognition tasks in the lensless camera system,and develops a basic method for
novel applications
Exploring Reionization-Era Quasars IV: Discovery of Six New Quasars with DES, VHS and unWISE Photometry
This is the fourth paper in a series of publications aiming at discovering
quasars at the epoch of reionization. In this paper, we expand our search for
quasars to the footprint of the Dark Energy Survey (DES) Data Release
One (DR1), covering deg of new area. We select quasar
candidates using deep optical, near-infrared (near-IR) and mid-IR photometric
data from the DES DR1, the VISTA Hemisphere Survey (VHS), the VISTA Kilo-degree
Infrared Galaxy (VIKING) survey, the UKIRT InfraRed Deep Sky Surveys -- Large
Area Survey (ULAS) and the unblurred coadds from the Wide-field Infrared Survey
Explore () images (unWISE). The inclusion of DES and unWISE photometry
allows the search to reach 1 magnitude fainter, comparing to our quasar survey in the northern sky (Wang et al. 2018). We report
the initial discovery and spectroscopic confirmation of six new luminous
quasars at , including an object at , the fourth quasar yet
known at , from a small fraction of candidates observed thus far. Based on
the recent measurement of quasar luminosity function using the
quasar sample from our survey in the northern sky, we estimate that there will
be 55 quasars at at in the full DES
footprint.Comment: 8 pages, 3 figures, submitted to A
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