132 research outputs found
Adaptive frequency-domain equalization for the transmission of the fundamental mode in a few-mode fiber
We propose and experimentally demonstrate single-carrier adaptive frequency-domain equalization (SC-FDE) to mitigate multipath interference (MPI) for the transmission of the fundamental mode in a few-mode fiber. The FDE approach reduces computational complexity significantly compared to the time-domain equalization (TDE) approach while maintaining the same performance. Both FDE and TDE methods are evaluated by simulating long-haul fundamental-mode transmission using a few-mode fiber. For the fundamental mode operation, the required tap length of the equalizer depends on the differential mode group delay (DMGD) of a single span rather than DMGD of the entire link
The Science of Detecting LLM-Generated Texts
The emergence of large language models (LLMs) has resulted in the production
of LLM-generated texts that is highly sophisticated and almost
indistinguishable from texts written by humans. However, this has also sparked
concerns about the potential misuse of such texts, such as spreading
misinformation and causing disruptions in the education system. Although many
detection approaches have been proposed, a comprehensive understanding of the
achievements and challenges is still lacking. This survey aims to provide an
overview of existing LLM-generated text detection techniques and enhance the
control and regulation of language generation models. Furthermore, we emphasize
crucial considerations for future research, including the development of
comprehensive evaluation metrics and the threat posed by open-source LLMs, to
drive progress in the area of LLM-generated text detection
SPeC: A Soft Prompt-Based Calibration on Mitigating Performance Variability in Clinical Notes Summarization
Electronic health records (EHRs) store an extensive array of patient
information, encompassing medical histories, diagnoses, treatments, and test
outcomes. These records are crucial for enabling healthcare providers to make
well-informed decisions regarding patient care. Summarizing clinical notes
further assists healthcare professionals in pinpointing potential health risks
and making better-informed decisions. This process contributes to reducing
errors and enhancing patient outcomes by ensuring providers have access to the
most pertinent and current patient data. Recent research has shown that
incorporating prompts with large language models (LLMs) substantially boosts
the efficacy of summarization tasks. However, we show that this approach also
leads to increased output variance, resulting in notably divergent outputs even
when prompts share similar meanings. To tackle this challenge, we introduce a
model-agnostic Soft Prompt-Based Calibration (SPeC) pipeline that employs soft
prompts to diminish variance while preserving the advantages of prompt-based
summarization. Experimental findings on multiple clinical note tasks and LLMs
indicate that our method not only bolsters performance but also effectively
curbs variance for various LLMs, providing a more uniform and dependable
solution for summarizing vital medical information
Supermodes for optical transmission
In this paper, the concept of supermode is introduced for long-distance optical transmission systems. The supermodes exploit coupling between the cores of a multi-core fiber, in which the core-to-core distance is much shorter than that in conventional multi-core fiber. The use of supermodes leads to a larger mode effective area and higher mode density than the conventional multi-core fiber. Through simulations, we show that the proposed coupled multi-core fiber allows lower modal dependent loss, mode coupling and differential modal group delay than few-mode fibers. These properties suggest that the coupled multi-core fiber could be a good candidate for both spatial division multiplexing and single-mode operation
Efficient XAI Techniques: A Taxonomic Survey
Recently, there has been a growing demand for the deployment of Explainable
Artificial Intelligence (XAI) algorithms in real-world applications. However,
traditional XAI methods typically suffer from a high computational complexity
problem, which discourages the deployment of real-time systems to meet the
time-demanding requirements of real-world scenarios. Although many approaches
have been proposed to improve the efficiency of XAI methods, a comprehensive
understanding of the achievements and challenges is still needed. To this end,
in this paper we provide a review of efficient XAI. Specifically, we categorize
existing techniques of XAI acceleration into efficient non-amortized and
efficient amortized methods. The efficient non-amortized methods focus on
data-centric or model-centric acceleration upon each individual instance. In
contrast, amortized methods focus on learning a unified distribution of model
explanations, following the predictive, generative, or reinforcement
frameworks, to rapidly derive multiple model explanations. We also analyze the
limitations of an efficient XAI pipeline from the perspectives of the training
phase, the deployment phase, and the use scenarios. Finally, we summarize the
challenges of deploying XAI acceleration methods to real-world scenarios,
overcoming the trade-off between faithfulness and efficiency, and the selection
of different acceleration methods.Comment: 15 pages, 3 figure
Towards Assumption-free Bias Mitigation
Despite the impressive prediction ability, machine learning models show
discrimination towards certain demographics and suffer from unfair prediction
behaviors. To alleviate the discrimination, extensive studies focus on
eliminating the unequal distribution of sensitive attributes via multiple
approaches. However, due to privacy concerns, sensitive attributes are often
either unavailable or missing in real-world scenarios. Therefore, several
existing works alleviate the bias without sensitive attributes. Those studies
face challenges, either in inaccurate predictions of sensitive attributes or
the need to mitigate unequal distribution of manually defined non-sensitive
attributes related to bias. The latter requires strong assumptions about the
correlation between sensitive and non-sensitive attributes. As data
distribution and task goals vary, the strong assumption on non-sensitive
attributes may not be valid and require domain expertise. In this work, we
propose an assumption-free framework to detect the related attributes
automatically by modeling feature interaction for bias mitigation. The proposed
framework aims to mitigate the unfair impact of identified biased feature
interactions. Experimental results on four real-world datasets demonstrate that
our proposed framework can significantly alleviate unfair prediction behaviors
by considering biased feature interactions
- …