RESPONSE TO PENNSYLVANIA BILL 700: ACCOUNTABILITY REFORMS IN PENNSYLVANIA’S CHARTER SCHOOLS

In February 2017, a House Bill was introduced in the General Assembly of the Commonwealth of Pennsylvania. This Bill, and future similar bills, seeks to amend the Public School Code, which calls for increases in charter school seats. In this paper, I introduce the story of charter schools and their developments. Then, I discuss the current state of public and charter education in the Commonwealth of Pennsylvania. Finally, I make recommendations for the implementation of such similar Bills. Specifically, charter schools should be held accountable for their finances, for cyber charter school programs, and be diligent in resource sharing with traditional public schools.  Article visualizations

The Supplemental Nutrition Assistance Program as a Source of Social Welfare – Reflections and Recommendations

This paper discusses the Supplemental Nutrition Assistance Program, which aid those in need via food stamps. First the author introduces the program, including its coverage. Then, the author discusses criticisms of the program—that it has high costs and may reduce incentives for work. Finally, recommendations are given in light of the shortcomings

Graph Neural Networks for Molecules

Graph neural networks (GNNs), which are capable of learning representations from graphical data, are naturally suitable for modeling molecular systems. This review introduces GNNs and their various applications for small organic molecules. GNNs rely on message-passing operations, a generic yet powerful framework, to update node features iteratively. Many researches design GNN architectures to effectively learn topological information of 2D molecule graphs as well as geometric information of 3D molecular systems. GNNs have been implemented in a wide variety of molecular applications, including molecular property prediction, molecular scoring and docking, molecular optimization and de novo generation, molecular dynamics simulation, etc. Besides, the review also summarizes the recent development of self-supervised learning for molecules with GNNs.Comment: A chapter for the book "Machine Learning in Molecular Sciences". 31 pages, 4 figure

A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition

The joint task of Dialog Sentiment Classification (DSC) and Act Recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog simultaneously. However, current methods encode the dialog context in only one direction, which limits their ability to thoroughly comprehend the context. Moreover, these methods overlook the explicit correlations between sentiment and act labels, which leads to an insufficient ability to capture rich sentiment and act clues and hinders effective and accurate reasoning. To address these issues, we propose a Bi-directional Multi-hop Inference Model (BMIM) that leverages a feature selection network and a bi-directional multi-hop inference network to iteratively extract and integrate rich sentiment and act clues in a bi-directional manner. We also employ contrastive learning and dual learning to explicitly model the correlations of sentiment and act labels. Our experiments on two widely-used datasets show that BMIM outperforms state-of-the-art baselines by at least 2.6% on F1 score in DAR and 1.4% on F1 score in DSC. Additionally, Our proposed model not only improves the performance but also enhances the interpretability of the joint sentiment and act prediction task.Comment: Accepted by NLPCC 202

Dataset for predicting cybersickness from a virtual navigation task

This work presents a dataset collected to predict cybersickness in virtual reality environments. The data was collected from navigation tasks in a virtual environment designed to induce cybersickness. The dataset consists of many data points collected from diverse participants, including physiological responses (EDA and Heart Rate) and self-reported cybersickness symptoms. The paper will provide a detailed description of the dataset, including the arranged navigation task, the data collection procedures, and the data format. The dataset will serve as a valuable resource for researchers to develop and evaluate predictive models for cybersickness and will facilitate more research in cybersickness mitigation

PoseFusion: Robust Object-in-Hand Pose Estimation with SelectLSTM

Accurate estimation of the relative pose between an object and a robot hand is critical for many manipulation tasks. However, most of the existing object-in-hand pose datasets use two-finger grippers and also assume that the object remains fixed in the hand without any relative movements, which is not representative of real-world scenarios. To address this issue, a 6D object-in-hand pose dataset is proposed using a teleoperation method with an anthropomorphic Shadow Dexterous hand. Our dataset comprises RGB-D images, proprioception and tactile data, covering diverse grasping poses, finger contact states, and object occlusions. To overcome the significant hand occlusion and limited tactile sensor contact in real-world scenarios, we propose PoseFusion, a hybrid multi-modal fusion approach that integrates the information from visual and tactile perception channels. PoseFusion generates three candidate object poses from three estimators (tactile only, visual only, and visuo-tactile fusion), which are then filtered by a SelectLSTM network to select the optimal pose, avoiding inferior fusion poses resulting from modality collapse. Extensive experiments demonstrate the robustness and advantages of our framework. All data and codes are available on the project website: https://elevenjiang1.github.io/ObjectInHand-Dataset

Detoxify Language Model Step-by-Step

Detoxification for LLMs is challenging since it requires models to avoid generating harmful content while maintaining the generation capability. To ensure the safety of generations, previous detoxification methods detoxify the models by changing the data distributions or constraining the generations from different aspects in a single-step manner. However, these approaches will dramatically affect the generation quality of LLMs, e.g., discourse coherence and semantic consistency, since language models tend to generate along the toxic prompt while detoxification methods work in the opposite direction. To handle such a conflict, we decompose the detoxification process into different sub-steps, where the detoxification is concentrated in the input stage and the subsequent continual generation is based on the non-toxic prompt. Besides, we also calibrate the strong reasoning ability of LLMs by designing a Detox-Chain to connect the above sub-steps in an orderly manner, which allows LLMs to detoxify the text step-by-step. Automatic and human evaluation on two benchmarks reveals that by training with Detox-Chain, six LLMs scaling from 1B to 33B can obtain significant detoxification and generation improvement. Our code and data are available at https://github.com/CODINNLG/Detox-CoT. Warning: examples in the paper may contain uncensored offensive content

Bidirectional Association between Major Depressive Disorder and Gastroesophageal Reflux Disease:Mendelian Randomization Study

Background: Observational research has found a bidirectional relationship between major depressive disorder and gastroesophageal reflux disease; however, the causal association of this relationship is undetermined. Aims: A bidirectional Mendelian randomization study was performed to explore the causal relationships between major depressive disorder and gastroesophageal reflux disease. Methods: For the instrumental variables of major depressive disorder and gastroesophageal reflux disease, 31 and 24 single-nucleotide polymorphisms without linkage disequilibrium (r(2) <= 0.001) were selected from relevant genome-wide association studies, respectively, at the genome-wide significance level (p <= 5 x 10(-8)). We sorted summary-level genetic data for major depressive disorder, gastroesophageal reflux disease, gastroesophageal reflux disease without esophagitis, and reflux esophagitis from meta-analysis study of genome-wide association studies involving 173,005 individuals (59,851 cases and 113,154 non-cases), 385,276 individuals (80,265 cases and 305,011 non-cases), 463,010 individuals (4360 cases and 458,650 non-cases), and 383,916 individuals (12,567 cases and 371,349 non-cases), respectively. Results: Genetic liability to major depressive disorder was positively associated with gastroesophageal reflux disease and its subtypes. Per one-unit increase in log-transformed odds ratio of major depressive disorder, the odds ratio was 1.31 (95% confidence interval [CI], 1.19-1.43; p = 1.64 x 10(-8)) for gastroesophageal reflux disease, 1.51 (95% CI, 1.15-1.98; p = 0.003) for gastroesophageal reflux disease without esophagitis, and 1.21 (95% CI, 1.05-1.40; p = 0.010) for reflux esophagitis. Reverse-direction analysis suggested that genetic liability to gastroesophageal reflux disease was causally related to increasing risk of major depressive disorder. Per one-unit increase in log-transformed odds ratio of gastroesophageal reflux disease, the odds ratio of major depressive disorder was 1.28 (95% confidence interval, 1.11-1.47; p = 1.0 x 10(-3)). Conclusions: This Mendelian randomization study suggests a bidirectional causal relationship between major depressive disorder and gastroesophageal reflux disease

Markov Games with Decoupled Dynamics: Price of Anarchy and Sample Complexity

This paper studies the finite-time horizon Markov games where the agents' dynamics are decoupled but the rewards can possibly be coupled across agents. The policy class is restricted to local policies where agents make decisions using their local state. We first introduce the notion of smooth Markov games which extends the smoothness argument for normal form games to our setting, and leverage the smoothness property to bound the price of anarchy of the Markov game. For a specific type of Markov game called the Markov potential game, we also develop a distributed learning algorithm, multi-agent soft policy iteration (MA-SPI), which provably converges to a Nash equilibrium. Sample complexity of the algorithm is also provided. Lastly, our results are validated using a dynamic covering game