181 research outputs found
Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education
Developing models to automatically score students' written responses to
science problems is critical for science education. However, collecting and
labeling sufficient student responses for training models is time and
cost-consuming. Recent studies suggest that pre-trained language models (PLMs)
can be adapted to downstream tasks without fine-tuning with prompts. However,
no research has employed such a prompt approach in science education. As
student responses are presented with natural language, aligning the scoring
procedure as the next sentence prediction task using prompts can skip the
costly fine-tuning stage. In this study, we developed a zero-shot approach to
automatically score student responses via Matching Exemplars as Next Sentence
Prediction (MeNSP). This approach employs no training samples. We first apply
MeNSP in scoring three assessment tasks of scientific argumentation and found
machine-human scoring agreements, Cohen's Kappa ranges from 0.30 to 0.57, and
F1 score ranges from 0.54 to 0.81. To improve the performance, we extend our
research to the few-shots setting, either randomly selecting labeled student
responses or manually constructing responses to fine-tune the models. We find
that one task's performance is improved with more samples, Cohen's Kappa from
0.30 to 0.38, and F1 score from 0.54 to 0.59; for the two others, scoring
performance is not improved. We also find that randomly selected few-shots
perform better than the human expert-crafted approach. This study suggests that
MeNSP can yield referable automatic scoring for student responses while
significantly reducing the cost of model training. This method can benefit
low-stakes classroom assessment practices in science education. Future research
should further explore the applicability of the MeNSP in different types of
assessment tasks in science education and improve the model performance.Comment: 10+3 page
Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain
Linking computational natural language processing (NLP) models and neural
responses to language in the human brain on the one hand facilitates the effort
towards disentangling the neural representations underpinning language
perception, on the other hand provides neurolinguistics evidence to evaluate
and improve NLP models. Mappings of an NLP model's representations of and the
brain activities evoked by linguistic input are typically deployed to reveal
this symbiosis. However, two critical problems limit its advancement: 1) The
model's representations (artificial neurons, ANs) rely on layer-level
embeddings and thus lack fine-granularity; 2) The brain activities (biological
neurons, BNs) are limited to neural recordings of isolated cortical unit (i.e.,
voxel/region) and thus lack integrations and interactions among brain
functions. To address those problems, in this study, we 1) define ANs with
fine-granularity in transformer-based NLP models (BERT in this study) and
measure their temporal activations to input text sequences; 2) define BNs as
functional brain networks (FBNs) extracted from functional magnetic resonance
imaging (fMRI) data to capture functional interactions in the brain; 3) couple
ANs and BNs by maximizing the synchronization of their temporal activations.
Our experimental results demonstrate 1) The activations of ANs and BNs are
significantly synchronized; 2) the ANs carry meaningful linguistic/semantic
information and anchor to their BN signatures; 3) the anchored BNs are
interpretable in a neurolinguistic context. Overall, our study introduces a
novel, general, and effective framework to link transformer-based NLP models
and neural activities in response to language and may provide novel insights
for future studies such as brain-inspired evaluation and development of NLP
models
Core-Periphery Principle Guided Redesign of Self-Attention in Transformers
Designing more efficient, reliable, and explainable neural network
architectures is critical to studies that are based on artificial intelligence
(AI) techniques. Previous studies, by post-hoc analysis, have found that the
best-performing ANNs surprisingly resemble biological neural networks (BNN),
which indicates that ANNs and BNNs may share some common principles to achieve
optimal performance in either machine learning or cognitive/behavior tasks.
Inspired by this phenomenon, we proactively instill organizational principles
of BNNs to guide the redesign of ANNs. We leverage the Core-Periphery (CP)
organization, which is widely found in human brain networks, to guide the
information communication mechanism in the self-attention of vision transformer
(ViT) and name this novel framework as CP-ViT. In CP-ViT, the attention
operation between nodes is defined by a sparse graph with a Core-Periphery
structure (CP graph), where the core nodes are redesigned and reorganized to
play an integrative role and serve as a center for other periphery nodes to
exchange information. We evaluated the proposed CP-ViT on multiple public
datasets, including medical image datasets (INbreast) and natural image
datasets. Interestingly, by incorporating the BNN-derived principle (CP
structure) into the redesign of ViT, our CP-ViT outperforms other
state-of-the-art ANNs. In general, our work advances the state of the art in
three aspects: 1) This work provides novel insights for brain-inspired AI: we
can utilize the principles found in BNNs to guide and improve our ANN
architecture design; 2) We show that there exist sweet spots of CP graphs that
lead to CP-ViTs with significantly improved performance; and 3) The core nodes
in CP-ViT correspond to task-related meaningful and important image patches,
which can significantly enhance the interpretability of the trained deep model.Comment: Core-periphery, functional brain networks, Vi
An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning
As a noninvasive and "task-free" technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients
Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification
With the popularity of deep neural networks (DNNs), model interpretability is
becoming a critical concern. Many approaches have been developed to tackle the
problem through post-hoc analysis, such as explaining how predictions are made
or understanding the meaning of neurons in middle layers. Nevertheless, these
methods can only discover the patterns or rules that naturally exist in models.
In this work, rather than relying on post-hoc schemes, we proactively instill
knowledge to alter the representation of human-understandable concepts in
hidden layers. Specifically, we use a hierarchical tree of semantic concepts to
store the knowledge, which is leveraged to regularize the representations of
image data instances while training deep models. The axes of the latent space
are aligned with the semantic concepts, where the hierarchical relations
between concepts are also preserved. Experiments on real-world image datasets
show that our method improves model interpretability, showing better
disentanglement of semantic concepts, without negatively affecting model
classification performance
Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities
Recent advances in artificial general intelligence (AGI), particularly large
language models and creative image generation systems have demonstrated
impressive capabilities on diverse tasks spanning the arts and humanities.
However, the swift evolution of AGI has also raised critical questions about
its responsible deployment in these culturally significant domains
traditionally seen as profoundly human. This paper provides a comprehensive
analysis of the applications and implications of AGI for text, graphics, audio,
and video pertaining to arts and the humanities. We survey cutting-edge systems
and their usage in areas ranging from poetry to history, marketing to film, and
communication to classical art. We outline substantial concerns pertaining to
factuality, toxicity, biases, and public safety in AGI systems, and propose
mitigation strategies. The paper argues for multi-stakeholder collaboration to
ensure AGI promotes creativity, knowledge, and cultural values without
undermining truth or human dignity. Our timely contribution summarizes a
rapidly developing field, highlighting promising directions while advocating
for responsible progress centering on human flourishing. The analysis lays the
groundwork for further research on aligning AGI's technological capacities with
enduring social goods
Strong Synergism of Palmatine and Fluconazole/Itraconazole Against Planktonic and Biofilm Cells of Candida Species and Efflux-Associated Antifungal Mechanism
Fungal infections caused by Candida albicans and non-albicans Candida [NAC] species are becoming a growing threat in immunodeficient population, people with long-term antibiotic treatment and patients enduring kinds of catheter intervention. The resistance to one or more than one conventional antifungal agents contributes greatly to the widespread propagation of Candida infections. The severity of fungal infection requires the discovery of novel antimycotics and the extensive application of combination strategy. In this study, a group of Candida standard and clinical strains including C. albicans as well as several NAC species were employed to evaluate the antifungal potentials of palmatine (PAL) alone and in combination with fluconazole (FLC)/itraconazole (ITR) by microdilution method, checkerboard assay, gram staining, spot assay, and rhodamine 6G efflux test. Subsequently, the expressions of transporter-related genes, namely CDR1, CDR2, MDR1, and FLU1 for C. albicans, CDR1 and MDR1 for Candida tropicalis and Candida parapsilosis, ABC1 and ABC2 for Candida krusei, CDR1, CDR2, and SNQ2 for Candida glabrata were analyzed by qRT-PCR. The susceptibility test showed that PAL presented strong synergism with FLC and ITR with fractional inhibitory concentration index (FICI) in a range of 0.0049–0.75 for PAL+FLC and 0.0059–0.3125 for PAL+ITR in planktonic cells, 0.125–0.375 for PAL+FLC and 0.0938–0.3125 for PAL+ITR in biofilms. The susceptibility results were also confirmed by gram staining and spot assay. After combinations, a vast quantity of rhodamine 6G could not be pumped out as considerably intracellular red fluorescence was accumulated. Meanwhile, the expressions of efflux-associated genes were evaluated and presented varying degrees of inhibition. These results indicated that PAL was a decent antifungal synergist to promote the antifungal efficacy of azoles (such as FLC and ITR), and the underlying antifungal mechanism might be linked with the inhibition of efflux pumps and the elevation of intracellular drug content
Extraction of Extracellular Matrix in Static and Dynamic Candida Biofilms Using Cation Exchange Resin and Untargeted Analysis of Matrix Metabolites by Ultra-High-Performance Liquid Chromatography-Tandem Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-TOF-MS)
Fungal infections caused by Candida albicans poses a great threat to human health. The ability of biofilm formation is believed to be associated with resistance-related Candida infections. Currently, knowledge on extracellular matrix (EM) of C. albicans biofilm is limited. In this study, we introduced ion exchange resin, i.e., cation exchange resin (CER) and anion exchange resin (AER), in EM extraction of C. albicans biofilm as well as several non-albicans Candida (NAC) biofilms under static and dynamic states in combination with vortexing and ultrasonication (VU). The metabolites extracted from the dynamic C. albicans biofilm matrix using the CER-VU and VU were identified with ultra-high-performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) via untargeted filtration. Compared with other physical and chemical extraction methods, CER-VU was demonstrated to be an ideal approach with high-yield acquisitions of EM constituents including proteins, triglycerides and carbohydrates and low-level damages on fungal cell viability and integrity. The untargeted MS analysis further showed the high efficacy of CER-VU, as a large quantity of metabolites (217 versus 198) was matched comprising a great number of lipids, carbohydrates, amino acids, nucleic acids and their derivatives together with a high involvement of signaling pathways compared with the VU alone. However, combining the results from both the CER-VU and VU methods could generate more metabolites. In summary, the EM analysis of the dynamic C. albicans biofilm expands our understanding upon a comprehensive depiction of matrix components and provides another effective approach for EM extraction
Segment Anything Model (SAM) for Radiation Oncology
In this study, we evaluate the performance of the Segment Anything Model
(SAM) model in clinical radiotherapy. We collected real clinical cases from
four regions at the Mayo Clinic: prostate, lung, gastrointestinal, and head \&
neck, which are typical treatment sites in radiation oncology. For each case,
we selected the OARs of concern in radiotherapy planning and compared the Dice
and Jaccard outcomes between clinical manual delineation, automatic
segmentation using SAM's "segment anything" mode, and automatic segmentation
using SAM with box prompt. Our results indicate that SAM performs better in
automatic segmentation for the prostate and lung regions, while its performance
in the gastrointestinal and head \& neck regions was relatively inferior. When
considering the size of the organ and the clarity of its boundary, SAM displays
better performance for larger organs with clear boundaries, such as the lung
and liver, and worse for smaller organs with unclear boundaries, like the
parotid and cochlea. These findings align with the generally accepted
variations in difficulty level associated with manual delineation of different
organs at different sites in clinical radiotherapy. Given that SAM, a single
trained model, could handle the delineation of OARs in four regions, these
results also demonstrate SAM's robust generalization capabilities in automatic
segmentation for radiotherapy, i.e., achieving delineation of different
radiotherapy OARs using a generic automatic segmentation model. SAM's
generalization capabilities across different regions make it technically
feasible to develop a generic model for automatic segmentation in radiotherapy
PharmacyGPT: The AI Pharmacist
In this study, we introduce PharmacyGPT, a novel framework to assess the
capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in
emulating the role of clinical pharmacists. Our methodology encompasses the
utilization of LLMs to generate comprehensible patient clusters, formulate
medication plans, and forecast patient outcomes. We conduct our investigation
using real data acquired from the intensive care unit (ICU) at the University
of North Carolina Chapel Hill (UNC) Hospital. Our analysis offers valuable
insights into the potential applications and limitations of LLMs in the field
of clinical pharmacy, with implications for both patient care and the
development of future AI-driven healthcare solutions. By evaluating the
performance of PharmacyGPT, we aim to contribute to the ongoing discourse
surrounding the integration of artificial intelligence in healthcare settings,
ultimately promoting the responsible and efficacious use of such technologies
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