Class-wise Calibration: A Case Study on COVID-19 Hate Speech

Proper calibration of deep-learning models is critical for many high-stakes problems. In this paper, we show that existing calibration metrics fail to pay attention to miscalibration on individual classes, hence overlooking minority classes and causing significant issues on imbalanced classification problems. Using a COVID-19 hate-speech dataset, we first discover that in imbalanced datasets, miscalibration error on an individual class varies greatly, and error on minority classes can be magnitude times worse than what is suggested by the overall calibration performance. To address this issue, we propose a new metric based on expected miscalibration error, named as Contraharmonic Expected Calibration Error (CECE), which punishes severe miscalibration on individual classes. We further devise a novel variant of temperature scaling for imbalanced data to improve class-wise miscalibration, which re-weights the loss function by the inverse class count to tune the scaling parameter to reduce worst-case minority calibration error. Our case study on a benchmarking COVID-19 hate speech task shows the effectiveness of our calibration metric and our temperature scaling strategy

Effectiveness of Data Augmentation for Parameter Efficient Tuning with Limited Data

Recent work has demonstrated that using parameter efficient tuning techniques such as prefix tuning (or P-tuning) on pretrained language models can yield performance that is comparable or superior to fine-tuning while dramatically reducing trainable parameters. Nevertheless, the effectiveness of such methods under the context of data augmentation, a common strategy to improve learning under low data regimes, has not been fully explored. In this paper, we examine the effectiveness of several popular task-agnostic data augmentation techniques, i.e., EDA, Back Translation, and Mixup, when using two general parameter efficient tuning methods, P-tuning v2 and LoRA, under data scarcity. We show that data augmentation can be used to boost the performance of P-tuning and LoRA models, but the effectiveness of each technique varies and certain methods can lead to a notable degradation in performance, particularly when using larger models and on harder tasks. We further analyze the sentence representations of P-tuning compared to fine-tuning to help understand the above behaviour, and reveal how P-tuning generally presents a more limited ability to separate the sentence embeddings from different classes of augmented data. In addition, it displays poorer performance on heavily altered data. However, we demonstrate that by adding a simple contrastive loss function it can help mitigate such issues for prefix tuning, resulting in sizable improvements to augmented data performance.Comment: Published at the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023) at ACL 202

Climate policies under dynamic international economic cycles: a heterogeneous countries DSGE model

In light of increased economic integration and global warming, addressing critical issues such as the role of multilateral climate policies and the strategic interaction of countries in climate negotiations becomes paramount. We thus established for this paper an open economy environmental dynamic stochastic general equilibrium model with heterogeneous production sectors, bilateral climate policies, asymmetric economies, and asymmetric stochastic shocks, using China and the EU as case studies in order to analyze the interaction and linking of international carbon markets under dynamic international economic cycles. This led us to some major conclusions. First, with various methods we verified that, due to deadweight loss, the efficiency of the separate carbon market is lower than that of the joint carbon market. Second, the intensity of the spillover effects depends partly on different climate policies. This means that, in terms of supply-side shocks, the EU's economy in a joint carbon market is more sensitive because its cross-border spillover effects are enhanced, while demand-side shocks have a stronger impact on the EU's economy under a separate carbon market. Third, the Ramsey policy rule revealed that both China's and the EU's emission quotas should be adjusted pro-cyclically under separate carbon markets. The cross-border spillover effects of the joint carbon market, however can change the pro-cyclical characteristics of foreign (EU's) optimal quotas

SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis

Organoids are self-organized 3D cell clusters that closely mimic the architecture and function of in vivo tissues and organs. Quantification of organoid morphology helps in studying organ development, drug discovery, and toxicity assessment. Recent microscopy techniques provide a potent tool to acquire organoid morphology features, but manual image analysis remains a labor and time-intensive process. Thus, this paper proposes a comprehensive pipeline for microscopy analysis that leverages the SegmentAnything to precisely demarcate individual organoids. Additionally, we introduce a set of morphological properties, including perimeter, area, radius, non-smoothness, and non-circularity, allowing researchers to analyze the organoid structures quantitatively and automatically. To validate the effectiveness of our approach, we conducted tests on bright-field images of human induced pluripotent stem cells (iPSCs) derived neural-epithelial (NE) organoids. The results obtained from our automatic pipeline closely align with manual organoid detection and measurement, showcasing the capability of our proposed method in accelerating organoids morphology analysis.Comment: submitted to SPIE: Medical Imaging 202

Enhanced Nigrostriatal Neuron-specific, Long-term Expression by Using Neural-specific Promoters in Combination with Targeted Gene Transfer by Modified Helper Virus-free HSV-1 Vector Particles

Background: Direct gene transfer into neurons has potential for developing gene therapy treatments for specific neurological conditions, and for elucidating neuronal physiology. Due to the complex cellular composition of specific brain areas, neuronal type-specific recombinant gene expression is required for many potential applications of neuronal gene transfer. One approach is to target gene transfer to a specific type of neuron. We developed modified Herpes Simplex Virus (HSV-1) particles that contain chimeric glycoprotein C (gC) – glial cell line-derived neurotrophic factor (GDNF) or brain-derived neurotrophic factor (BDNF) proteins. HSV-1 vector particles containing either gC – GDNF or gC – BDNF target gene transfer to nigrostriatal neurons, which contain specific receptors for GDNF or BDNF. A second approach to achieve neuronal type-specific expression is to use a cell type-specific promoter, and we have used the tyrosine hydroxylase (TH) promoter to restrict expression to catecholaminergic neurons or a modified neurofilament heavy gene promoter to restrict expression to neurons, and both of these promoters support long-term expression from HSV-1 vectors. To both improve nigrostriatal-neuron specific expression, and to establish that targeted gene transfer can be followed by long-term expression, we performed targeted gene transfer with vectors that support long-term, neuronal-specific expression. Results: Helper virus-free HSV-1 vector packaging was performed using either gC – GDNF or gC – BDNF and vectors that contain either the TH promoter or the modified neurofilament heavy gene promoter. Vector stocks were injected into the midbrain proximal to the substantia nigra, and the rats were sacrificed at either 4 days or 1 month after gene transfer. Immunofluorescent costaining was performed to detect both recombinant gene products and nigrostriatal neurons. The combination of targeted gene transfer with neuronal-specific promoters improved nigrostriatal neuron-specific expression (83 to 93%) compared to either approach alone, and supported long-term (1 month) expression at levels similar to those observed using untargeted gene transfer. Conclusion: Targeted gene transfer can be used in combination with neuronal-specific promoters to achieve a high level of nigrostriatal neuron-specific expression. Targeted gene transfer can be followed by long-term expression. Nigrostriatal neuron-specific expression may be useful for specific gene therapy approaches to Parkinson's disease or for genetic analyses of nigrostriatal neuron physiology

Curcumin suppresses leukemia cell proliferation by downregulation of P13K/AKT/mTOR signalling pathway

Purpose: To investigate the effect of curcumin ester on the proliferation of leukemia cell lines in vitro. Methods: Changes in WEHI-3 and THP 1 cell viabilities were measured using Cell Counting Kit 8 (CCK 8). Analysis of cell cycle and determination of apoptosis were carried out using propidium iodide and Annexin V fluorescein isothiocyanate staining. Transmission electron microscopy was used for observing the presence of apoptotic features in cells. Results: Treatment with curcumin ester for 72 h caused significant reduction in the proliferation of WEHI-3 and THP 1 cells. Curcumin ester, at a dose of 50 µM, decreased the proliferations of WEHI-3 and THP 1 cells to 28 and 32 %, respectively. On exposure to curcumin ester for 72 h, cell cycle in WEHI-3 cells was arrested in G1/G0 phase. Curcumin ester at doses of 25, 30 and 50 µM enhanced apoptosis in WEHI-3 cells to 46, 58 and 64 %, respectively. Curcumin ester suppressed the levels of phosphoinositide 3 kinase (PI3K), protein kinase B (AKT) and mechanistic target of rapamycin (mTOR) protein and mRNA in WEHI-3 cells. In curcumin ester-treated WEHI-3 cells, the presence of apop¬totic bodies increased significantly and concentration-dependently. Conclusion: These results demonstrate that curcumin ester inhibits leukemia cell proliferation by inducing apoptosis and arresting cell cycle in G1/G0 phase, probably via suppression of PI3K, AKT and mTOR, and promotion of PTEN. Thus, curcumin ester has potentials for use in the development of an effective treatment strategy for leukemia

DAG-Structured Long Short-Term Memory for Semantic Compositionality

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