Use Classifier as Generator

Image recognition/classification is a widely studied problem, but its reverse problem, image generation, has drawn much less attention until recently. But the vast majority of current methods for image generation require training/retraining a classifier and/or a generator with certain constraints, which can be hard to achieve. In this paper, we propose a simple approach to directly use a normally trained classifier to generate images. We evaluate our method on MNIST and show that it produces recognizable results for human eyes with limited quality with experiments

Frobenius height of prismatic cohomology with coefficients

We study the behavior of Frobenius operators on smooth proper pushforwards of prismatic F-crystals. In particular we show that the i-th pushforward has its Frobenius height increased by at most i. Our proof crucially uses the notion of prismatic F-gauges introduced by Drinfeld and Bhatt--Lurie and its relative version, and we give a self-contained treatment without using the stacky formulation.Comment: 42 pages, comments welcom

Exploration on the Influence of Bilingualism on Language Production

The ability to speak two languages has a significant impact on language production, cognitive function, linguistic aptitude and neurological processes. This study explores the acquisition of bilingualism, its influence on cognition and language development as well as neurobiological implications in comparison with monolingual speech. Bilingual individuals demonstrate improved executive functioning abilities such as flexibility while exhibiting deferred decline through regularly switching between both languages spoken. Linguistic proficiency is affected by ecological factors like age at which learning began or attitudes towards multilingual states that affect usage frequency across differing environments. Although word retrieval can become an impediment for those who are bilingual; however advantages lie in increased vocabulary range alongside metalinguistic awareness gained from diverse communication scenarios. The structure of brain regions linked to processing multilinguality also correlate intrinsically during decision making. The future research direction benefits studying cross-cultural creative expression impacting effective simultaneous use .Enhancing appreciation amongst speakers via comprehension urges us further into grasping influences behind producing multicultural forms essential within our modern day society

N,N′-Bis(4-methyl­benzyl­idene)benzene-1,4-diamine

The centrosymmetric title compound, C22H20N2, crystallizes with one half-mol­ecule in the asymmetric unit. The dihedral angle between the central and outer benzene rings is 46.2 (2)°

Amazon Product Reviews Helpfulness Prediction

E-commerce business become successful by offering people convenient online experience as well as providing tens of thousands of crowd-sourced reviews that are written by customers and users about their experiences and opinions regarding the products or the services they paid for. For an online shopping website, such as Amazon.com, it is very important to recommend high-quality product reviews to the website users because customers make decisions based on what they read from the reviews. However, there are simply way too many reviews out there, and it would be a dreadful task for anyone to read them all. In this paper, we try to build a logistic regression model that can than predict helpfulness of reviews.Master of Science in Information Scienc

[1,1-(Butane-1,4-diyl)-2,3-dicyclohexylguanidinato]dimethylaluminum(III)

In the crystal structure of the title complex, [Al(CH3)2(C17H30N3)], the AlIII cation is coordinated by two methyl ligands and two N atoms from the guanidinato ligand in a distorted tetra­hedral geometry. The dihedral angle between the CN2 and AlC2 planes is 85.37 (2)°. The two N atoms of the guanidinato ligand exhibit an almost uniform affinity to the metal atom

RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL

One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i.e., tables and columns) and the skeleton (i.e., SQL keywords). Such coupled targets increase the difficulty of parsing the correct SQL queries especially when they involve many schema items and logic operators. This paper proposes a ranking-enhanced encoding and skeleton-aware decoding framework to decouple the schema linking and the skeleton parsing. Specifically, for a seq2seq encoder-decode model, its encoder is injected by the most relevant schema items instead of the whole unordered ones, which could alleviate the schema linking effort during SQL parsing, and its decoder first generates the skeleton and then the actual SQL query, which could implicitly constrain the SQL parsing. We evaluate our proposed framework on Spider and its three robustness variants: Spider-DK, Spider-Syn, and Spider-Realistic. The experimental results show that our framework delivers promising performance and robustness. Our code is available at https://github.com/RUCKBReasoning/RESDSQL.Comment: Accepted to AAAI 2023 main conference (oral

Curriculum Graph Machine Learning: A Survey

Graph machine learning has been extensively studied in both academia and industry. However, in the literature, most existing graph machine learning models are designed to conduct training with data samples in a random order, which may suffer from suboptimal performance due to ignoring the importance of different graph data samples and their training orders for the model optimization status. To tackle this critical problem, curriculum graph machine learning (Graph CL), which integrates the strength of graph machine learning and curriculum learning, arises and attracts an increasing amount of attention from the research community. Therefore, in this paper, we comprehensively overview approaches on Graph CL and present a detailed survey of recent advances in this direction. Specifically, we first discuss the key challenges of Graph CL and provide its formal problem definition. Then, we categorize and summarize existing methods into three classes based on three kinds of graph machine learning tasks, i.e., node-level, link-level, and graph-level tasks. Finally, we share our thoughts on future research directions. To the best of our knowledge, this paper is the first survey for curriculum graph machine learning.Comment: IJCAI 2023 Survey Trac