101 research outputs found
The Gaussian Multiple Access Diamond Channel
In this paper, we study the capacity of the diamond channel. We focus on the
special case where the channel between the source node and the two relay nodes
are two separate links with finite capacities and the link from the two relay
nodes to the destination node is a Gaussian multiple access channel. We call
this model the Gaussian multiple access diamond channel. We first propose an
upper bound on the capacity. This upper bound is a single-letterization of an
-letter upper bound proposed by Traskov and Kramer, and is tighter than the
cut-set bound. As for the lower bound, we propose an achievability scheme based
on sending correlated codes through the multiple access channel with
superposition structure. We then specialize this achievable rate to the
Gaussian multiple access diamond channel. Noting the similarity between the
upper and lower bounds, we provide sufficient and necessary conditions that a
Gaussian multiple access diamond channel has to satisfy such that the proposed
upper and lower bounds meet. Thus, for a Gaussian multiple access diamond
channel that satisfies these conditions, we have found its capacity.Comment: submitted to IEEE Transactions on Information Theor
Asymmetric Clean Segments-Guided Self-Supervised Learning for Robust Speaker Verification
Contrastive self-supervised learning (CSL) for speaker verification (SV) has
drawn increasing interest recently due to its ability to exploit unlabeled
data. Performing data augmentation on raw waveforms, such as adding noise or
reverberation, plays a pivotal role in achieving promising results in SV. Data
augmentation, however, demands meticulous calibration to ensure intact
speaker-specific information, which is difficult to achieve without speaker
labels. To address this issue, we introduce a novel framework by incorporating
clean and augmented segments into the contrastive training pipeline. The clean
segments are repurposed to pair with noisy segments to form additional positive
and negative pairs. Moreover, the contrastive loss is weighted to increase the
difference between the clean and augmented embeddings of different speakers.
Experimental results on Voxceleb1 suggest that the proposed framework can
achieve a remarkable 19% improvement over the conventional methods, and it
surpasses many existing state-of-the-art techniques.Comment: 5 pages, 2 figures, submitted to ICASSP 202
DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment
Dialogue assessment plays a critical role in the development of open-domain
dialogue systems. Existing work are uncapable of providing an end-to-end and
human-epistemic assessment dataset, while they only provide sub-metrics like
coherence or the dialogues are conversed between annotators far from real user
settings. In this paper, we release a large-scale dialogue quality assessment
dataset (DiQAD), for automatically assessing open-domain dialogue quality.
Specifically, we (1) establish the assessment criteria based on the dimensions
conforming to human judgements on dialogue qualities, and (2) annotate
large-scale dialogues that conversed between real users based on these
annotation criteria, which contains around 100,000 dialogues. We conduct
several experiments and report the performances of the baselines as the
benchmark on DiQAD. The dataset is openly accessible at
https://github.com/yukunZhao/Dataset_Dialogue_quality_evaluation.Comment: Accepted to Findings of EMNLP 202
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method
Large Language Models (LLMs) have shown great potential in Natural Language
Processing (NLP) tasks. However, recent literature reveals that LLMs generate
nonfactual responses intermittently, which impedes the LLMs' reliability for
further utilization. In this paper, we propose a novel self-detection method to
detect which questions that a LLM does not know that are prone to generate
nonfactual results. Specifically, we first diversify the textual expressions
for a given question and collect the corresponding answers. Then we examine the
divergencies between the generated answers to identify the questions that the
model may generate falsehoods. All of the above steps can be accomplished by
prompting the LLMs themselves without referring to any other external
resources. We conduct comprehensive experiments and demonstrate the
effectiveness of our method on recently released LLMs, e.g., Vicuna, ChatGPT,
and GPT-4
Anomalous Floquet non-Hermitian skin effect in a ring resonator lattice
We present a one-dimensional coupled ring resonator lattice exhibiting a
variant of the non- Hermitian skin effect (NHSE) that we call the anomalous
Floquet NHSE. Unlike existing approaches to achieving the NHSE by engineering
gain and loss on different ring segments, our design uses fixed on-site gain or
loss in each ring. The anomalous Floquet NHSE is marked by the existence of
skin modes at every value of the Floquet quasienergy, allowing for broadband
asymmetric transmission. Varying the gain/loss induces a non-Hermitian
topological phase transition, reversing the localization direction of the skin
modes. An experimental implementation in an acoustic lattice yields good
agreement with theoretical predictions, with a very broad relative bandwidth of
around 40%.Comment: 7 pages, 3 figure
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