Can the Black Lives Matter Movement Reduce Racial Disparities? Evidence from Medical Crowdfunding

Using high-frequency donation records from a major medical crowdfunding site and careful difference-in-difference analysis, we demonstrate that the 2020 BLM surge decreased the fundraising gap between Black and non-Black beneficiaries by around 50\%. The reduction is largely attributed to non-Black donors. Those beneficiaries in counties with moderate BLM activities were most impacted. We construct innovative instrumental variable approaches that utilize weekends and rainfall to identify the global and local effects of BLM protests. Results suggest a broad social movement has a greater influence on charitable-giving behavior than a local event. Social media significantly magnifies the impact of protests

Bohr-type inequalities for unimodular bounded analytic functions

In this paper, we establish several new versions of Bohr-type inequalities for bounded analytic functions in the unit disk by allowing $\varphi=\{\varphi_n(r)\}^{\infty}_{n=0}$ in place of the $\{r^n\}^{\infty}_{n=0}$ in the power series representations of the functions involved with the Bohr sum and thereby introducing a single parameter, which generalize several related results of earlier authors.Comment: 13 pages. The article is with a journa

Teach-DETR: Better Training DETR with Teachers

In this paper, we present a novel training scheme, namely Teach-DETR, to learn better DETR-based detectors from versatile teacher detectors. We show that the predicted boxes from teacher detectors are effective medium to transfer knowledge of teacher detectors, which could be either RCNN-based or DETR-based detectors, to train a more accurate and robust DETR model. This new training scheme can easily incorporate the predicted boxes from multiple teacher detectors, each of which provides parallel supervisions to the student DETR. Our strategy introduces no additional parameters and adds negligible computational cost to the original detector during training. During inference, Teach-DETR brings zero additional overhead and maintains the merit of requiring no non-maximum suppression. Extensive experiments show that our method leads to consistent improvement for various DETR-based detectors. Specifically, we improve the state-of-the-art detector DINO with Swin-Large backbone, 4 scales of feature maps and 36-epoch training schedule, from 57.8% to 58.9% in terms of mean average precision on MSCOCO 2017 validation set. Code will be available at https://github.com/LeonHLJ/Teach-DETR

ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation

In this work, we make the first attempt to evaluate LLMs in a more challenging code generation scenario, i.e. class-level code generation. We first manually construct the first class-level code generation benchmark ClassEval of 100 class-level Python code generation tasks with approximately 500 person-hours. Based on it, we then perform the first study of 11 state-of-the-art LLMs on class-level code generation. Based on our results, we have the following main findings. First, we find that all existing LLMs show much worse performance on class-level code generation compared to on standalone method-level code generation benchmarks like HumanEval; and the method-level coding ability cannot equivalently reflect the class-level coding ability among LLMs. Second, we find that GPT-4 and GPT-3.5 still exhibit dominate superior than other LLMs on class-level code generation, and the second-tier models includes Instruct-Starcoder, Instruct-Codegen, and Wizardcoder with very similar performance. Third, we find that generating the entire class all at once (i.e. holistic generation strategy) is the best generation strategy only for GPT-4 and GPT-3.5, while method-by-method generation (i.e. incremental and compositional) is better strategies for the other models with limited ability of understanding long instructions and utilizing the middle information. Lastly, we find the limited model ability of generating method-dependent code and discuss the frequent error types in generated classes. Our benchmark is available at https://github.com/FudanSELab/ClassEval

The acute toxicity of cypermethrin, emamectin benzoate and imidacloprid on red swamp crayfish (<em>Procambarus clarkia</em>)

Pesticide contamination is commonly found as a mixture of different pesticides rather than individual compounds. However, the regulatory risk evaluation is mostly based on the effects of individual pesticides. In the present study, we aimed to investigate the individual and combined toxicities of cypermethrin (CYP) with emamectin benzoate (EMB) and imidacloprid (IMI) to crayfish using acute indices and various sub-lethal endpoints. Semi-static bioassay procedures were followed in the experiment. The 24, 48, and 72 h LC~50~ values (with 95% confidence limits) of CYP for crayfish were calculated as 0.141, 0.137, and 0.135 ug/ml, respectively, which were higher than those of IMI (75.813, 72.345, 70.568 ug/ml) and EMB (34.581, 27.930, 22.298 ug/ml). Pesticide mixtures of CYP and EMB displayed a synergistic response to crayfish; the LC50 was 0.053, 0.050, and 0.048 ug/ml, which was lower than when only CYP was present. Pesticide mixtures of CYP and EMB were found to be highly toxic to crayfish. At the physiological level, both individuals and mixtures of pesticides caused severe injury to the internal organs of crayfish. Taken together, the synergistic effects indicated that it was highly important to include joint toxicity studies when assessing the risk of pesticides

Orexin-A protects against oxygen-glucose deprivation/reoxygenation-induced cell damage by inhibiting endoplasmic reticulum stress-mediated apoptosis via the Gi and PI3K signaling pathways

The neuropeptide orexin-A (OXA) has a neuroprotective effect, acting as an anti-apoptotic factor in response to multiple stimuli. Apoptosis induced by endoplasmic reticulum stress (ERS) underlies oxygen-glucose deprivation and reoxygenation (OGD/R)-induced cell damage, an in vitro model of ischemia/reperfusion injury. However, that OXA inhibits ERS-induced apoptosis in the OGD/R model has not been reported. In the present study, we investigated the neuroprotective effect of OXA (0.1 μM) on OGD/R-induced damage in the human neuroblastoma cell line SH-SY5Y. After OXA treatment following 4 h oxygen-glucose deprivation (OGD) and then 4 h reoxygenation (R), cell morphology, viability, and apoptosis were analyzed by histology, Cell Counting Kit-8 assay, and flow cytometry, respectively. Western blotting was used to measure expression levels of ERS- and apoptosis-related proteins. To determine signaling pathways involved in OXA-mediated neuroprotection, the Gi pathway inhibitor pertussis toxin (PTX; 100 ng/mL) and PI3K inhibitor LY294002 (LY; 10 μM) were added. In addition, in order to prove the specificity of these characteristics, the OXA antagonist Suvorexant (DORA; Ki of 0.55 nM and 0.35 nM for OX1R and OX2R) was used for intervention. Our results showed that OGD/R induced cell damage, manifested as morphological changes and a significant decrease in viability. Furthermore, Western blotting detected an increase in ERS-related proteins GRP78, p-IRE1α, p-JNK, and Cleaved caspase-12, as well as apoptosis-related proteins Cleaved caspase-3 and Bax, and a decrease in the anti-apoptosis factor Bcl-2. OXA intervention alleviated the degree of cellular damage, and protein expression was also reversed. In addition, the protective effect of OXA was reduced by adding PTX and LY. Meanwhile, after the use of DORA, changes in the expression of related proteins were detected, and it was found that the protective effect of OXA was weakened. Collectively, our results indicate that OXA has a neuroprotective effect on OGD/R-induced cell damage by inhibiting ERS-induced apoptosis through the combined action of Gi and PI3K signaling pathways. These findings help to clarify the mechanism underlying the neuroprotective action of OXA, which should aid the development of further candidate drugs, and provide a new therapeutic direction for the treatment of ischemic stroke

InstructCoder: Empowering Language Models for Code Editing

Code editing encompasses a variety of pragmatic tasks that developers deal with daily. Despite its relevance and practical usefulness, automatic code editing remains an underexplored area in the evolution of deep learning models, partly due to data scarcity. In this work, we explore the use of large language models (LLMs) to edit code based on user instructions, covering a broad range of implicit tasks such as comment insertion, code optimization, and code refactoring. To facilitate this, we introduce InstructCoder, the first dataset designed to adapt LLMs for general-purpose code editing, containing highdiversity code-editing tasks. It consists of over 114,000 instruction-input-output triplets and covers multiple distinct code editing scenarios. The dataset is systematically expanded through an iterative process that commences with code editing data sourced from GitHub commits as seed tasks. Seed and generated tasks are used subsequently to prompt ChatGPT for more task data. Our experiments demonstrate that open-source LLMs fine-tuned on InstructCoder can edit code correctly based on users' instructions most of the time, exhibiting unprecedented code-editing performance levels. Such results suggest that proficient instruction-finetuning can lead to significant amelioration in code editing abilities. The dataset and the source code are available at https://github.com/qishenghu/CodeInstruct

MixReorg: Cross-Modal Mixed Patch Reorganization is a Good Mask Learner for Open-World Semantic Segmentation

Recently, semantic segmentation models trained with image-level text supervision have shown promising results in challenging open-world scenarios. However, these models still face difficulties in learning fine-grained semantic alignment at the pixel level and predicting accurate object masks. To address this issue, we propose MixReorg, a novel and straightforward pre-training paradigm for semantic segmentation that enhances a model's ability to reorganize patches mixed across images, exploring both local visual relevance and global semantic coherence. Our approach involves generating fine-grained patch-text pairs data by mixing image patches while preserving the correspondence between patches and text. The model is then trained to minimize the segmentation loss of the mixed images and the two contrastive losses of the original and restored features. With MixReorg as a mask learner, conventional text-supervised semantic segmentation models can achieve highly generalizable pixel-semantic alignment ability, which is crucial for open-world segmentation. After training with large-scale image-text data, MixReorg models can be applied directly to segment visual objects of arbitrary categories, without the need for further fine-tuning. Our proposed framework demonstrates strong performance on popular zero-shot semantic segmentation benchmarks, outperforming GroupViT by significant margins of 5.0%, 6.2%, 2.5%, and 3.4% mIoU on PASCAL VOC2012, PASCAL Context, MS COCO, and ADE20K, respectively

Rare earth ion-doped Y2.95R0.05MgAl3SiO12 (R = Yb, Y, Dy, Eu, Sm) garnet-type microwave ceramics for 5G application

In this work, Y2.95R0.05MgAl3SiO12 (R=Yb, Y, Dy, Eu, Sm) microwave single-phase dielectric ce-ramics were successfully prepared via conventional ceramic technology by doping a series of rare earth elements with different ionic radius (Yb, Y, Dy, Eu, Sm) for the first time. The effects of A site occupied by rare earth elements on the microwave dielectric properties of Y2.95R0.05MgAl3SiO12 were studied by crystal structure refinement, scanning electron microscope (SEM), bond valence theory, P-V-L theory and infrared reflection spectroscopy. It was found that the ionicity of Y-O bond, the lattice energy, the bond energy and bond valance of Al(Tet)-O bond had important effects on microwave dielectric properties. Particularly, the optimum microwave dielectric properties were obtained for Y2.95Dy0.05MgAl3SiO12 sintered at 1575 °C for 6 h, with εr = 9.68, Q×f = 68,866 GHz, and τf = -35.8 ppm/°C, displaying its potential prospect in the 5G communication

Effects of different intrusion patterns during anterior teeth retraction using clear aligners in extraction cases: an iterative finite element analysis

BackgroundOvertreatment design of clear aligner treatment (CAT) in extraction cases is currently primarily based on the clinical experience of orthodontists and is not supported by robust evidence on the underlying biomechanics. This study aimed to investigate the biomechanical effects of overtreatment strategies involving different maxillary anterior teeth intrusion patterns during anterior teeth retraction by CAT in extraction cases.Materials and methodsA finite element model of the maxillary dentition with the first premolar extracted was constructed. A loading method of clear aligners (CAs) based on the initial state field was proposed. The iterative method was used to simulate the long-term orthodontic tooth movement under the mechanical load exerted by the CAs. Three groups of CAs were utilized for anterior teeth retraction (G0: control group; G1: incisors intrusion group; G2: anterior teeth intrusion group). Tooth displacement and occlusal plane rotation tendency were analyzed.ResultsIn G0, CAT caused lingual tipping and extrusion of the incisors, distal tipping and extrusion of the canines, mesial tipping, and intrusion of the posterior teeth. In G1, the incisors showed minimal extrusion, whereas the canines showed increased extrusion and distal tipping tendency. G2 showed the smallest degree of posterior occlusal plane angle rotation, while the inclination tendency of the canines and second premolars decreased.Conclusion1. In CAT, tooth displacement tendency may change with increased wear time. 2. During anterior teeth retraction, the incisor intrusion pattern can provide effective vertical control for the lateral incisors but has little effect on the central incisors. Anterior teeth intrusion patterns can alleviate the inclination of canines and second premolars, resulting in partial relief of the roller-coaster effect