Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Clause Deletion Heuristics with Reinforcement Learning

We propose a method for training of clause deletion heuristics in DPLL-based solvers using Reinforcement Learning. We have implemented it as part of a software framework SAT-Gym which we plan to release as an OpenAI Gym compatible environment. We present experiments and preliminary results for the clause deletion heuristic in Glucose.
Teaching Assistance through Social Robotics for ASD - Toy robot as Speech Buddy and Mini-Drone as Exercise Partner

—Socially Assistive Robotics (SAR) is rapidly gaining importance in regular teaching. In this paper, we wish to extend SAR beyond usual schools and aim to design interventions for meeting the Individualized Educational Program (IEP) objectives of Children with Autism Spectrum Disorder (CwASD). Adoption of SAR in ASD is challenging because (a) existing studies are often conﬁned to humanoid robots resulting in high costs, and (b) the nature of such interventions poses a considerable technological barrier for Special Educators (SE). We study the use of non-humanoid, non-biomimetic COTS robots, Cozmo and Tello , as providing Teaching Assistance through SAR (TSAR). A TSAR intervention is a Triadic Interaction between a SE, a robot and an autistic child. We present Speech Buddy Cozmo which helps students in verbal communication, speciﬁcally targeting contingent response, receptive and expressive language, turn-taking in conversation and use of non-native language. Exercise Partner Tello is used in another TSAR intervention, which aims to help students in exercise lessons focusing on posture, bilateral coordination, linear movement, and visual-motor skills. Not only did the interventions help in improving learning outcomes, but SEs were comfortable in using the TSARs. Adding to the expected TSAR intervention outcomes, certain beneﬁts were also observed in a few participants like improvement in neck posture and self-motivated writing tasks. Our results show that TSAR based interventions can have a signiﬁcant impact on dealing with ASD.
Key genes involved in cell cycle arrest and DNA damage repair identified in anaplastic thyroid carcinoma using integrated bioinformatics analysis

Background Since anaplastic thyroid carcinoma (ATC) has rapid progression and a poor outcome, identification of the key genes and underlying mechanisms of ATC is required. Methods Gene expression profiles of GSE29265 and GSE33630 were available from the Gene Expression Omnibus database. The two profile datasets included 19 ATC tissues, 55 normal thyroid tissues and 59 papillary thyroid cancer (PTC) tissues. Differentially expressed genes (DEGs) between ATC tissues and normal thyroid tissues as well as ATC tissues and PTC tissues were identified using the GEO2R tool. Common DEGs between the two datasets were selected via Venn software online. Then, we applied the Database for Annotation, Visualization and Integrated Discovery for Kyoto Encyclopedia of Gene and Genome pathway and gene ontology (GO) analyses. Additionally, protein-protein interactions (PPIs) of these DEGs were visualized via Cytoscape with Search Tool for the Retrieval of Interacting Genes. In the PPI networks analyzed by the Molecular Complex Detection plug-in, all 54 upregulated core genes were selected. Furthermore, Kaplan-Meier analysis was applied to analyze overall survival based on these 54 genes. Then, we used the DrugBank database to identify drug relationships for the 54 genes. Additionally, we validated the correlations between genes enriched in pathways and genes identified as prognosis biomarkers of THCA by Gene Expression Profiling Interactive Analysis. Results Four genes (CCNB1, CCNB2, CDK1 and CHEK1) involved cell cycle arrest and DNA repair were significantly enriched in the G2/M phase of the cell cycle pathway and before G2 phase arrest of the P53 pathway. Inhibitors of CHEK1, CDK1 and TOP2A were identified in the DrugBank database. ANLN, DEPDC1, KIF2C, CENPN, TACC3 CCNB2 and CDC6 were hypothesized to be prognostic biomarkers of ATC. Furthermore, CCNB1, CCNB2, CDK1 and CHEK1 were significantly positively associated with these prognosis genes. Conclusions CCNB1, CCNB2, CDK1 and CHEK1 may be key genes involved cell cycle arrest and DNA damage repair in ATC. Further studies are required to confirm the contributions of the identified genes to ATC progression and survival.
Transactions of the Korean Nuclear Society Virtual Spring Meeting July 9-10, 2020 Accuracy Assessment of Change Detection Algorithm for Satellite Imagery in Support of Interpretation of Nuclear Activities

In the field of nuclear nonproliferation, remote monitoring and on-site inspection are conducted to ensure that nuclear energy is used for peaceful purposes. In this course of implementation, satellite information is being used to establish a site inspection plan, to verify correctness and completeness of nuclear material and facilities. Further, it is used as a means to identify and detect suspicious activity in rogue states that have attempted to proliferate nuclear weapons, not complying with the international nonproliferation regime. In the past decades, urban expansion, changes in shorelines and prediction of crop yields were analysed with the image quality of a few hundred meters. On the other hand, defence and security have recently begun to utilise the sub-meter level. For countering nuclear proliferation, observations of small-scale activities, such as the movement of vehicles within a nuclear complex, hot water discharge, steam generation and building construction/renovation, enable the estimation of nuclear-related activities [1]. This process demands a sophisticated visual interpretation of as little as a few pixel changes within an area of interest, spanning tens of square kilometres, which involves expertise in the nuclear fuel cycle and weapon development. Against this backdrop, efforts are being made to improve analytical efficiency by developing algorithms that detect small-scale changes [2-4]. In general, the accuracy of change detection algorithm in computer vision is presented in the form of a confusion matrix in units of pixels. For the purpose of identifying nuclear activities, however, the accuracy of the change detection algorithm has not been evaluated for some reason. In this regard, this research investigates the applicability of the traditional method of accuracy assessment for small-scale changes related to nuclear activity in high-resolution satellite imagery and provides a guideline for proper criterion. The following Section summarises algorithm-based change detection to counter nuclear proliferation and reviews the underlying cause, which makes a quantitative assessment of the algorithm challenging. The results of applying the traditional method for accuracy assessment and requirements of proper criterion to counter nuclear proliferation are discussed in Sections 3 and 4, respectively. The conclusions are set forth in Section 5. 2. Peculiarities of algorithm-based change detection for countering nuclear proliferation
Performance Evaluation between Collaborative Ranging and Trilateration Positioning using RSSI in Wireless Sensor Network

Generally, distance between sensor nodes is measured using received signal strength indicator (RSSI) in wireless sensor network (WSN). Since RSSI is not absolutely accurate and stable in indoor environment, the performance of position estimation largely depends on estimation methods. In this paper, performance evaluation is presented to compare trilateration based positioning technique and collaborative ranging (CR) technique. From evaluation results, CR method is shown to be better than trilateration based method in terms of accuracy and precision.
DESIGNING EFFECTIVE CELEBRITY MESSAGING: RESULTS FROM A NATIONWIDE TWITTER EXPERIMENT ON PUBLIC HEALTH IN INDONESIA

Celebrity endorsements are often sought to influence public opinion, but there is little work (a) decomposing the extent to which celebrity endorsement relies on directly authoring messages as opposed to passing on messages of others, (b) measuring whether explicitly citing external sources enhances message effectiveness, and (c) examining whether these online messages are meaningfully received and digested offline. We address these three questions by conducting a large-scale randomized trial in Indonesia in 2015-2016, in which 46 high profile celebrities agreed to tweet or retweet messages promoting immunization. The decomposition shows that public health messages are 72% more likely to be passed on when a celebrity is involved, but importantly, almost all of the effect (79%) comes only when the celebrity authors the message themselves. Explicitly citing an external medical authority reduces the passing-on rate by 27%. The results suggest that celebrities have an outsize influence in shaping public opinion, but primarily when they speak in their own voice. Date: July 24, 2020. We thank Ran Abramitsky, Marcella Alsan, Nancy Baym, Emily Breza, Leo Bursztyn, Rebecca Diamond, Dean Eckles, Paul Goldsmith-Pinkham, Ben Golub, Mary Gray, Rema Hanna, Johannes Haushofer, Matt Jackson, Tyler McCormick, Matthew Wai-Poi, Alex Wolitsky, and participants at various seminars for helpful discussions. Aaron Kaye, Nurzanty Khadijah, Devika Lakhote, Eva Lyubich, Sinead Maguire, Lina Marliani, Sebastian Steffen, Vincent Tanutama provided excellent research assistance. We thank Nila Moeloek, then Indonesian Special Envoy for Sustainable Development Goals, Diah Saminarsih, and their team for providing support for this project. This study was approved by IRBs at MIT (Protocol #1406006433) and Stanford (Protocol #31451), and registered in the AEA Social Science Registry (AEARCTR-0000757). Funding for this project came from the Australian Government Department of Foreign Affairs and Trade. The views expressed here are those of the authors only and do not represent those of any of the institutions or individuals acknowledged here. Asa Kreativita. †Department of Economics, Stanford University; NBER; J-PAL. §Microsoft Research, New England. ‡Department of Economics, MIT; NBER; J-PAL. World Bank. 0
Digenetic Trematodes in Louisiana Freshwater Fishes.

The parasitic fauna in Louisiana fresh-water fishes is practically unknown and this fact encouraged the writer to undertake the present studies# There are two distinct groups of people who would be interested in such studies* First, the sport and commercial fishermen of the state would be interested from a recreational and economical point of view* Second, the information derived from such research would fill a considerable gap in the scientific knowledge of the fauna in the st^te. This study is not a simple survey project* An attempt is made to present a comprehensive treatment cf the biology and ecology of the digenetic trematodes in fresh-water fishes of the state. The classical methods of collection and examination of fish hosts were utilized throughout this study* Most of the hosts were obtained from personnel of the Louisiana Wild Life and Fisheries Commission but many were obtained from sport and commercial fishermen. Some were directly acquired by the writer through angling and by the use of nets* All parasites were collected, recorded and preserved for study but only the digenetic trematodes are reported in this study* A total of 625 fish, representing fourteen families, twenty genera, and thirty-one species were collected and examined during the years 195U to 1957* The majority of these were game fishes* These
Statistical Query Lower Bounds for Tensor PCA

In the Tensor PCA problem introduced by Richard and Montanari (2014), one is given a dataset consisting of $n$ samples $\mathbf{T}_{1:n}$ of i.i. d. Gaussian tensors of order $k$ with the promise that $\mathbb{E}\mathbf{T}_1$ is a rank-1 tensor and $\|\mathbb{E} \mathbf{T}_1\| = 1$. The goal is to estimate $\mathbb{E} \mathbf{T}_1$. This problem exhibits a large conjectured hard phase when $k>2$: When $d \lesssim n \ll d^{\frac{k}{2}}$ it is information theoretically possible to estimate $\mathbb{E} \mathbf{T}_1$, but no polynomial time estimator is known. We provide a sharp analysis of the optimal sample complexity in the Statistical Query (SQ) model and show that SQ algorithms with polynomial query complexity not only fail to solve Tensor PCA in the conjectured hard phase, but also have a strictly sub-optimal sample complexity compared to some polynomial time estimators such as the Richard-Montanari spectral estimator. Our analysis reveals that the optimal sample complexity in the SQ model depends on whether $\mathbb{E} \mathbf{T}_1$ is symmetric or not. For symmetric, even order tensors, we also isolate a sample size regime in which it is possible to test if $\mathbb{E} \mathbf{T}_1 = \mathbf{0}$ or $\mathbb{E}\mathbf{T}_1 \neq \mathbf{0}$ with polynomially many queries but not estimate $\mathbb{E}\mathbf{T}_1$. Our proofs rely on the Fourier analytic approach of Feldman, Perkins and Vempala (2018) to prove sharp SQ lower bounds.
PolyScope: Multi-Policy Access Control Analysis to Triage Android Systems

Android filesystem access control provides a foundation for Android system integrity. Android utilizes a combination of mandatory (e.g., SEAndroid) and discretionary (e.g., UNIX permissions) access control, both to protect the Android platform from Android/OEM services and to protect Android/OEM services from third-party apps. However, OEMs often create vulnerabilities when they introduce market-differentiating features because they err when re-configuring this complex combination of Android policies. In this paper, we propose the PolyScope tool to triage the combination of Android filesystem access control policies to vet releases for vulnerabilities. The PolyScope approach leverages two main insights: (1) adversaries may exploit the coarse granularity of mandatory policies and the flexibility of discretionary policies to increase the permissions available to launch attacks, which we call permission expansion, and (2) system configurations may limit the ways adversaries may use their permissions to launch attacks, motivating computation of attack operations. We apply PolyScope to three Google and five OEM Android releases to compute the attack operations accurately to vet these releases for vulnerabilities, finding that permission expansion increases the permissions available to launch attacks, sometimes by more than 10X, but a significant fraction of these permissions (about 15-20%) are not convertible into attack operations. Using PolyScope, we find two previously unknown vulnerabilities, showing how PolyScope helps OEMs triage the complex combination of access control policies down to attack operations worthy of testing.
Hierarchical Bi-Directional Feature Perception Network for Person Re-Identification

Previous Person Re-Identification (Re-ID) models aim to focus on the most discriminative region of an image, while its performance may be compromised when that region is missing caused by camera viewpoint changes or occlusion. To solve this issue, we propose a novel model named Hierarchical Bi-directional Feature Perception Network (HBFP-Net) to correlate multi-level information and reinforce each other. First, the correlation maps of cross-level feature-pairs are modeled via low-rank bilinear pooling. Then, based on the correlation maps, Bi-directional Feature Perception (BFP) module is employed to enrich the attention regions of high-level feature, and to learn abstract and specific information in low-level feature. And then, we propose a novel end-to-end hierarchical network which integrates multi-level augmented features and inputs the augmented low- and middle-level features to following layers to retrain a new powerful network. What's more, we propose a novel trainable generalized pooling, which can dynamically select any value of all locations in feature maps to be activated. Extensive experiments implemented on the mainstream evaluation datasets including Market-1501, CUHK03 and DukeMTMC-ReID show that our method outperforms the recent SOTA Re-ID models.
Beyond Lexical: A Semantic Retrieval Framework for Textual SearchEngine

Search engine has become a fundamental component in various web and mobile applications. Retrieving relevant documents from the massive datasets is challenging for a search engine system, especially when faced with verbose or tail queries. In this paper, we explore a vector space search framework for document retrieval. Specifically, we trained a deep semantic matching model so that each query and document can be encoded as a low dimensional embedding. Our model was trained based on BERT architecture. We deployed a fast k-nearest-neighbor index service for online serving. Both offline and online metrics demonstrate that our method improved retrieval performance and search quality considerably, particularly for tail
When Deep Learning Meets Smart Contracts

Ethereum has become a widely used platform to enable secure, Blockchain-based financial and business transactions. However, many identified bugs and vulnerabilities in smart contracts have led to serious financial losses, which raises serious concerns about smart contract security. Thus, there is a significant need to better maintain smart contract code and ensure its high reliability. In this research: (1) Firstly, we propose an automated deep learning based approach to learn structural code embeddings of smart contracts in Solidity, which is useful for clone detection, bug detection and contract validation on smart contracts. We apply our approach to more than 22K solidity contracts collected from the Ethereum blockchain, results show that the clone ratio of solidity code is at around 90%, much higher than traditional software. We collect a list of 52 known buggy smart contracts belonging to 10 kinds of common vulnerabilities as our bug database. Our approach can identify more than 1000 clone related bugs based on our bug databases efficiently and accurately. (2) Secondly, according to developers' feedback, we have implemented the approach in a web-based tool, named Smartembed, to facilitate Solidity developers for using our approach. Our tool can assist Solidity developers to efficiently identify repetitive smart contracts in the existing Ethereum blockchain, as well as checking their contract against a known set of bugs. which can help to improve the users' confidence in the reliability of the contract. We optimize the implementations of Smartembed which is sufficient in supporting developers in real-time for practical uses. The Ethereum ecosystem as well as the individual Solidity developer can both benefit from our research. Smartembed website: http://www.smartembed. tools Demo video: https://youtu.be/o9ylyOpYFq8 Replication package: https://github.com/beyondacm/SmartEmbed
ConnectIt: A Framework for Static and Incremental Parallel Graph Connectivity Algorithms

Connected components is a fundamental kernel in graph applications. The fastest existing multicore algorithms for solving graph connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.
Risk‐Sensitive Markov Decision Processes with Combined Metrics of Mean and Variance

This study investigates the optimization problem of an infinite stage discrete time Markov decision process (MDP) with a long‐run average metric considering both mean and variance of rewards together. Such performance metric is important since the mean indicates average returns and the variance indicates risk or fairness. However, the variance metric couples the rewards at all stages, the traditional dynamic programming is inapplicable as the principle of time consistency fails. We study this problem from a new perspective called the sensitivity‐based optimization theory. A performance difference formula is derived and it can quantify the difference of the mean‐variance combined metrics of MDPs under any two different policies. The difference formula can be utilized to generate new policies with strictly improved mean‐variance performance. A necessary condition of the optimal policy and the optimality of deterministic policies are derived. We further develop an iterative algorithm with a form of policy iteration, which is proved to converge to local optima both in the mixed and randomized policy space. Specially, when the mean reward is constant in policies, the algorithm is guaranteed to converge to the global optimum. Finally, we apply our approach to study the fluctuation reduction of wind power in an energy storage system, which demonstrates the potential applicability of our optimization method.
Grouping effects of sparse CCA models in variable selection

The sparse canonical correlation analysis (SCCA) is a bi-multivariate association model that finds sparse linear combinations of two sets of variables that are maximally correlated with each other. In addition to the standard SCCA model, a simplified SCCA criterion which maixmizes the cross-covariance between a pair of canonical variables instead of their cross-correlation, is widely used in the literature due to its computational simplicity. However, the behaviors/properties of the solutions of these two models remain unknown in theory. In this paper, we analyze the grouping effect of the standard and simplified SCCA models in variable selection. In high-dimensional settings, the variables often form groups with high within-group correlation and low between-group correlation. Our theoretical analysis shows that for grouped variable selection, the simplified SCCA jointly selects or deselects a group of variables together, while the standard SCCA randomly selects a few dominant variables from each relevant group of correlated variables. Empirical results on synthetic data and real imaging genetics data verify the finding of our theoretical analysis.