111 research outputs found
The Multiverse: A Unique Movement towards Absolute Truth
There is a distinct relationship between the multiverse theory and the perception of truth when viewed through the lens of German Idealism. At their core, both theories are concerned with movement, or a constant state of flux. This paper draws two analogies between the multiverse theory and German Idealism. First, Kant’s theory of “the thing-in-itself” is posited as a rejection of the idea of a unified universe. Second, similarities are drawn between how the multiverse can be seen as layers of shifting reality and Hegel’s description of the road towards truth, which is filled with movements toward negation and advancement. Finally, the paper discusses how these analogies constitute a critique of scientism: the multiverse theory, as well as German Idealism’s concept of truth, both stand in direct contrast to how modernity places dogmatic and exaggerated trust in empirical science
Prediction of Overall Survival in Gastric Cancer by a Six-Gene Cox Proportional Hazards Model
This study aims to investigate prognostic genes that are correlated with overall survival in gastric cancer. A list of 43 critical genes is first selected from literature review and is narrowed down using marginal analysis, false discovery rate (FDR). This study used the 16 differential genes selected by FDR to create a cox proportional hazards regression model. Using a stepwise approach, the cox model is refined. From the 16 genes, 6 genes significantly correlated with overall survival are chosen and kept in the cox model. A principal component regression model was also constructed based on the principal component analysis results. The concordance of the principal component regression model is then compared to the concordance of the cox proportional hazards regression model. The final 6 identified genes are NRP1, STK11, MCM2, MARCKS, CTS6, C5. Of the 6 genes, MARCKS, NRP1, STK11, MCM2 are in line with previous research. CTS6 and C5, although studied in other cancers, are comparatively novel in the field of gastric cancer
Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs
Multiple convolutional neural network (CNN) classifiers have been proposed
for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However,
CNN models have been found vulnerable to universal adversarial perturbations
(UAPs), which are small and example-independent, yet powerful enough to degrade
the performance of a CNN model, when added to a benign example. This paper
proposes a novel total loss minimization (TLM) approach to generate UAPs for
EEG-based BCIs. Experimental results demonstrated the effectiveness of TLM on
three popular CNN classifiers for both target and non-target attacks. We also
verified the transferability of UAPs in EEG-based BCI systems. To our
knowledge, this is the first study on UAPs of CNN classifiers in EEG-based
BCIs. UAPs are easy to construct, and can attack BCIs in real-time, exposing a
potentially critical security concern of BCIs
CITB: A Benchmark for Continual Instruction Tuning
Continual learning (CL) is a paradigm that aims to replicate the human
ability to learn and accumulate knowledge continually without forgetting
previous knowledge and transferring it to new tasks. Recent instruction tuning
(IT) involves fine-tuning models to make them more adaptable to solving NLP
tasks in general. However, it is still uncertain how instruction tuning works
in the context of CL tasks. This challenging yet practical problem is
formulated as Continual Instruction Tuning (CIT). In this work, we establish a
CIT benchmark consisting of learning and evaluation protocols. We curate two
long dialogue task streams of different types, InstrDialog and InstrDialog++,
to study various CL methods systematically. Our experiments show that existing
CL methods do not effectively leverage the rich natural language instructions,
and fine-tuning an instruction-tuned model sequentially can yield similar or
better results. We further explore different aspects that might affect the
learning of CIT. We hope this benchmark will facilitate more research in this
direction.Comment: EMNLP 2023 Finding
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
The Segment Anything Model (SAM) stands as a foundational framework for image
segmentation. While it exhibits remarkable zero-shot generalization in typical
scenarios, its advantage diminishes when applied to specialized domains like
medical imagery and remote sensing. To address this limitation, this paper
introduces Conv-LoRA, a simple yet effective parameter-efficient fine-tuning
approach. By integrating ultra-lightweight convolutional parameters into
Low-Rank Adaptation (LoRA), Conv-LoRA can inject image-related inductive biases
into the plain ViT encoder, further reinforcing SAM's local prior assumption.
Notably, Conv-LoRA not only preserves SAM's extensive segmentation knowledge
but also revives its capacity of learning high-level image semantics, which is
constrained by SAM's foreground-background segmentation pretraining.
Comprehensive experimentation across diverse benchmarks spanning multiple
domains underscores Conv-LoRA's superiority in adapting SAM to real-world
semantic segmentation tasks.Comment: Accepted at ICLR 2024 Conferenc
A Cross-Cultural Perspective on the Preference for Potential Effect: An Individual Participant Data (IPD) Meta-Analysis Approach
A recent paper [Tormala ZL, Jia JS, Norton MI (2012). The preference for potential. Journal of personality and social psychology, 103:567-583] demonstrated that persons often prefer potential rather than achievement when evaluating others, because information regarding potential evokes greater interest and processing, resulting in more favorable evaluations. This research aimed to expand on this finding by asking two questions: (a) Is the preference for potential effect replicable in other cultures? (b) Is there any other mechanism that accounts for this preference for potential? To answer these two questions, we replicated Tormala et al.'s study in multiple cities (17 studies with 1,128 participants) in China using an individual participant data (IPD) meta-analysis approach to test our hypothesis. Our results showed that the preference for potential effect found in the US is also robust in China. Moreover, we also found a pro-youth bias behind the preference for potential effect. To be specific, persons prefer a potential-oriented applicant rather than an achievement-oriented applicant, partially because they believe that the former is younger than the latter
Turn-Level Active Learning for Dialogue State Tracking
Dialogue state tracking (DST) plays an important role in task-oriented
dialogue systems. However, collecting a large amount of turn-by-turn annotated
dialogue data is costly and inefficient. In this paper, we propose a novel
turn-level active learning framework for DST to actively select turns in
dialogues to annotate. Given the limited labelling budget, experimental results
demonstrate the effectiveness of selective annotation of dialogue turns.
Additionally, our approach can effectively achieve comparable DST performance
to traditional training approaches with significantly less annotated data,
which provides a more efficient way to annotate new dialogue data.Comment: EMNLP 2023 Main Conferenc
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