bluuebunny/crossref_metadata_2025 huggingface dataset:
Training Naturalized Semantic Parsers with Very Little Data
Authors: Rongali Subendhu , Arkoudas Konstantine , Rubino Melanie , Hamza Wael
Published: July 2022

Semantic parsing is an important NLP problem, particularly for voice assistants such as Alexa and Google Assistant. State-of-the-art (SOTA) semantic parsers are seq2seq architectures based on large language models that have been pretrained on vast amounts of text. To better leverage that pretraining, recent work has explored a reformulation of semantic parsing whereby the output sequences are themselves natural language sentences, but in a controlled fragment of natural language. This approach delivers strong results, particularly for few-shot semantic parsing, which is of key importance in practice and the focus of our paper. We push this line of work forward by introducing an automated methodology that delivers very significant additional improvements by utilizing modest amounts of unannotated data, which is typically easy to obtain. Our method is based on a novel synthesis of four techniques: joint training with auxiliary unsupervised tasks; constrained decoding; self-training; and paraphrasing. We show that this method delivers new SOTA few-shot performance on the Overnight dataset, particularly in very low-resource settings, and very compelling few-shot results on a new semantic parsing dataset.
Neural Subgraph Explorer: Reducing Noisy Information via Target-oriented Syntax Graph Pruning
Authors: Xing Bowen , Tsang Ivor
Published: July 2022

Recent years have witnessed the emerging success of leveraging syntax graphs for the target sentiment classification task.

However, we discover that existing syntax-based models suffer from two issues:

noisy information aggregation and loss of distant correlations.

In this paper, we propose a novel model termed Neural Subgraph Explorer, which

(1) reduces the noisy information via pruning target-irrelevant nodes on the syntax graph;

(2) introduces beneficial first-order connections between the target and its related words into the obtained graph.

Specifically, we design a multi-hop actions score estimator to evaluate the value of each word regarding the specific target.

The discrete action sequence is sampled through Gumble-Softmax and then used for both of the syntax graph and the self-attention graph.

To introduce the first-order connections between the target and its relevant words, the two pruned graphs are merged.

Finally, graph convolution is conducted on the obtained unified graph to update the hidden states.

And this process is stacked with multiple layers.

To our knowledge, this is the first attempt of target-oriented syntax graph pruning in this task.

Experimental results demonstrate the superiority of our model, which achieves new state-of-the-art performance.
Diversity Features Enhanced Prototypical Network for Few-shot Intent Detection
Authors: Yang Fengyi , Zhou Xi , Wang Yi , Atawulla Abibulla , Bi Ran
Published: July 2022

Few-shot Intent Detection (FSID) is a challenging task in dialogue systems due to the scarcity of available annotated utterances. Although existing few-shot learning approaches have made remarkable progress, they fall short in adapting to the Generalized Few-shot Intent Detection (GFSID) task where both seen and unseen classes are present. A core problem of the simultaneous existence of these two tasks is that limited training samples fail to cover the diversity of user expressions. In this paper, we propose an effective Diversity Features Enhanced Prototypical Network (DFEPN) to enhance diversity features for novel intents by fully exploiting the diversity of known intent samples. Specially, DFEPN generates diversity features of samples in the hidden space via a diversity feature generator module and then fuses these features with original support vectors to get a more suitable prototype vector of each class. To evaluate the effectiveness of our model on both FSID and GFSID tasks, we carry out sufficient experiments on two benchmark intent detection datasets. Results demonstrate that our proposed model outperforms existing state-of-the-art methods and keeps stable performance on both two tasks.
High-resource Language-specific Training for Multilingual Neural Machine Translation
Authors: Yang Jian , Yin Yuwei , Ma Shuming , Zhang Dongdong , Li Zhoujun , Wei Furu
Published: July 2022

Multilingual neural machine translation (MNMT) trained in multiple language pairs has attracted considerable attention due to fewer model parameters and lower training costs by sharing knowledge among multiple languages. Nonetheless, multilingual training is plagued by language interference degeneration in shared parameters because of the negative interference among different translation directions, especially on high-resource languages. In this paper, we propose the multilingual translation model with the high-resource language-specific training (HLT-MT) to alleviate the negative interference, which adopts the two-stage training with the language-specific selection mechanism. Specifically, we first train the multilingual model only with the high-resource pairs and select the language-specific modules at the top of the decoder to enhance the translation quality of high-resource directions. Next, the model is further trained on all available corpora to transfer knowledge from high-resource languages (HRLs) to low-resource languages (LRLs). Experimental results show that HLT-MT outperforms various strong baselines on WMT-10 and OPUS-100 benchmarks. Furthermore, the analytic experiments validate the effectiveness of our method in mitigating the negative interference in multilingual training.
SyntaSpeech: Syntax-Aware Generative Adversarial Text-to-Speech
Authors: Ye Zhenhui , Zhao Zhou , Ren Yi , Wu Fei
Published: July 2022

The recent progress in non-autoregressive text-to-speech (NAR-TTS) has made fast and high-quality speech synthesis possible. However, current NAR-TTS models usually use phoneme sequence as input and thus cannot understand the tree-structured syntactic information of the input sequence, which hurts the prosody modeling. To this end, we propose SyntaSpeech, a syntax-aware and light-weight NAR-TTS model, which integrates tree-structured syntactic information into the prosody modeling modules in PortaSpeech. Specifically, 1) We build a syntactic graph based on the dependency tree of the input sentence, then process the text encoding with a syntactic graph encoder to extract the syntactic information. 2) We incorporate the extracted syntactic encoding with PortaSpeech to improve the prosody prediction. 3) We introduce a multi-length discriminator to replace the flow-based post-net in PortaSpeech, which simplifies the training pipeline and improves the inference speed, while keeping the naturalness of the generated audio. Experiments on three datasets not only show that the tree-structured syntactic information grants SyntaSpeech the ability to synthesize better audio with expressive prosody, but also demonstrate the generalization ability of SyntaSpeech to adapt to multiple languages and multi-speaker text-to-speech. Ablation studies demonstrate the necessity of each component in SyntaSpeech. Source code and audio samples are available at https://syntaspeech.github.io.
Position-aware Joint Entity and Relation Extraction with Attention Mechanism
Authors: Zhang Chenglong , Gao Shuyong , Wang Haofen , Zhang Wenqiang
Published: July 2022

Named entity recognition and relation extraction are two important core subtasks of information extraction, which aim to identify named entities and extract relations between them. In recent years, span representation methods have received a lot of attention and are widely used to extract entities and corresponding relations from plain texts. Most recent works focus on how to obtain better span representations from pre-trained encoders, but ignore the negative impact of a large number of span candidates on slowing down the model performance. In our work, we propose a joint entity and relation extraction model with an attention mechanism and position-attentive markers. The attention score of each candidate span is calculated, and most of the candidate spans with low attention scores are pruned before being fed into the span classifier, thus achieving the goal of removing the most irrelevant spans. At the same time, in order to explore whether the position information can improve the performance of the model, we add position-attentive markers to the model. The experimental results show that our model is effective. With the same pre-trained encoder, our model achieves the new state-of-the-art on standard benchmarks (ACE05, CoNLL04 and SciERC), obtaining a 4.7%-17.8% absolute improvement in relation F1.
EditSinger: Zero-Shot Text-Based Singing Voice Editing System with Diverse Prosody Modeling
Authors: Zhang Lichao , Zhao Zhou , Ren Yi , Deng Liqun
Published: July 2022

Zero-shot text-based singing editing enables singing voice modification based on the given edited lyrics without any additional data from the target singer. However, due to the different demands, challenges occur when applying existing speech editing methods to singing voice editing task, mainly including the lack of systematic consideration concerning prosody in insertion and deletion, as well as the trade-off between the naturalness of pronunciation and the preservation of prosody in replacement. In this paper we propose EditSinger, which is a novel singing voice editing model with specially designed diverse prosody modules to overcome the challenges above. Specifically, 1) a general masked variance adaptor is introduced for the comprehensive prosody modeling of the inserted lyrics and the transition of deletion boundary; and 2) we further design a fusion pitch predictor for replacement. By disentangling the reference pitch and fusing the predicted pronunciation, the edited pitch can be reconstructed, which could ensure a natural pronunciation while preserving the prosody of the original audio. In addition, to the best of our knowledge, it is the first zero-shot text-based singing voice editing system. Our experiments conducted on the OpenSinger prove that EditSinger can synthesize high-quality edited singing voices with natural prosody according to the corresponding operations.
“Think Before You Speak”: Improving Multi-Action Dialog Policy by Planning Single-Action Dialogs
Authors: Zhang Shuo , Zhao Junzhou , Wang Pinghui , Li Yu , Huang Yi , Feng Junlan
Published: July 2022

Multi-action dialog policy (MADP), which generates multiple atomic dialog actions per turn, has been widely applied in task-oriented dialog systems to provide expressive and efficient system responses. Existing MADP models usually imitate action combinations from the labeled multi-action dialog samples. Due to data limitations, they generalize poorly toward unseen dialog flows. While interactive learning and reinforcement learning algorithms can be applied to incorporate external data sources of real users and user simulators, they take significant manual effort to build and suffer from instability. To address these issues, we propose Planning Enhanced Dialog Policy (PEDP), a novel multi-task learning framework that learns single-action dialog dynamics to enhance multi-action prediction. Our PEDP method employs model-based planning for conceiving what to express before deciding the current response through simulating single-action dialogs. Experimental results on the MultiWOZ dataset demonstrate that our fully supervised learning-based method achieves a solid task success rate of 90.6%, improving 3% compared to the state-of-the-art methods. The source code and the appendix of this paper can be obtained from https://github.com/ShuoZhangXJTU/PEDP.
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
Authors: Zhu Tong , Qu Xiaoye , Chen Wenliang , Wang Zhefeng , Huai Baoxing , Yuan Nicholas , Zhang Min
Published: July 2022

Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference.

In contrast to the previous studies, we propose a fast and lightweight model named as PTPCG.

In our model, we design a novel strategy for event argument combination together with a non-autoregressive decoding algorithm via pruned complete graphs, which are constructed under the guidance of the automatically selected pseudo triggers.

Compared to the previous systems, our system achieves competitive results with 19.8% of parameters and much lower resource consumption, taking only 3.8% GPU hours for training and up to 8.5 times faster for inference.

Besides, our model shows superior compatibility for the datasets with (or without) triggers and the pseudo triggers can be the supplements for annotated triggers to make further improvements.

Codes are available at https://github.com/Spico197/DocEE .
Online Bin Packing with Predictions
Authors: Angelopoulos Spyros , Kamali Shahin , Shadkami Kimia
Published: July 2022

Bin packing is a classic optimization problem with a wide range of applications from load balancing to supply chain management. In this work, we study the online variant of the problem, in which a sequence of items of various sizes must be placed into a minimum number of bins of uniform capacity. The online algorithm is enhanced with a (potentially erroneous) prediction concerning the frequency of item sizes in the sequence. We design and analyze online algorithms with efficient tradeoffs between the consistency (i.e., the competitive ratio assuming no prediction error) and the robustness (i.e., the competitive ratio under adversarial error), and whose performance degrades near-optimally as a function of the prediction error. This is the first theoretical and experimental study of online bin packing in the realistic setting of learnable predictions. Previous work addressed only extreme cases with respect to the prediction error, and relied on overly powerful and error-free oracles.
Online Planning in POMDPs with Self-Improving Simulators
Authors: He Jinke , Suau Miguel , Baier Hendrik , Kaisers Michael , Oliehoek Frans A.
Published: July 2022

How can we plan efficiently in a large and complex environment when the time budget is limited? Given the original simulator of the environment, which may be computationally very demanding, we propose to learn online an approximate but much faster simulator that improves over time. To plan reliably and efficiently while the approximate simulator is learning, we develop a method that adaptively decides which simulator to use for every simulation, based on a statistic that measures the accuracy of the approximate simulator. This allows us to use the approximate simulator to replace the original simulator for faster simulations when it is accurate enough under the current context, thus trading off simulation speed and accuracy. Experimental results in two large domains show that when integrated with POMCP, our approach allows to plan with improving efficiency over time.
On the Computational Complexity of Model Reconciliations
Authors: Sreedharan Sarath , Bercher Pascal , Kambhampati Subbarao
Published: July 2022

Model-reconciliation explanation is a popular framework for generating explanations for planning problems. While the framework has been extended to multiple settings since its introduction for classical planning problems, there is little agreement on the computational complexity of generating minimal model reconciliation explanations in the basic setting. In this paper, we address this lacuna by introducing a decision-version of the model-reconciliation explanation generation problem

and we show that it is Sigma-2-P Complete.
Landmark Heuristics for Lifted Classical Planning
Authors: Wichlacz Julia , Höller Daniel , Hoffmann Jörg
Published: July 2022

While state-of-the-art planning systems need a grounded (propositional) task representation, the input model is provided "lifted", specifying predicates and action schemas with variables over a finite object universe. The size of the grounded model is exponential in predicate/action-schema arity, limiting applicability to cases where it is small enough. Recent work has taken up this challenge, devising an effective lifted forward search planner as basis for lifted heuristic search, as well as a variety of lifted heuristic functions based on the delete relaxation. Here we add a novel family of lifted heuristic functions, based on landmarks. We design two methods for landmark extraction in the lifted setting. The resulting heuristics exhibit performance advantages over previous heuristics in several benchmark domains. Especially the combination with lifted delete relaxation heuristics to a LAMA-style planner yields good results, beating the previous state of the art in lifted planning.
Budgeted Sequence Submodular Maximization
Authors: Chen Xuefeng , Feng Liang , Cao Xin , Zeng Yifeng , Hou Yaqing
Published: July 2022

The problem of selecting a sequence of items that maximizes a given submodular function appears in many real-world applications. Existing study on the problem only considers uniform costs over items, but non-uniform costs on items are more general. Taking this cue, we study the problem of budgeted sequence submodular maximization (BSSM), which introduces non-uniform costs of items into the sequence selection. This problem can be found in a number of applications such as movie recommendation, course sequence design and so on. Non-uniform costs on items significantly increase the solution complexity and we prove that BSSM is NP-hard. To solve the problem, we first propose a greedy algorithm GBM with an error bound. We also design an anytime algorithm POBM based on Pareto optimization to improve the quality of solutions. Moreover, we prove that POBM can obtain approximate solutions in expected polynomial running time, and converges faster than a state-of-the-art algorithm POSEQSEL for sequence submodular maximization with cardinality constraints. We further introduce optimizations to speed up POBM. Experimental results on both synthetic and real-world datasets demonstrate the performance of our new algorithms.
Learning and Exploiting Progress States in Greedy Best-First Search
Authors: Ferber Patrick , Cohen Liat , Seipp Jendrik , Keller Thomas
Published: July 2022

Previous work introduced the concept of progress

states. After expanding a progress state, a greedy

best-first search (GBFS) will only expand states

with lower heuristic values. Current methods can

identify progress states only for a single task and

only after a solution for the task has been found.

We introduce a novel approach that learns a description

logic formula characterizing all progress

states in a classical planning domain. Using the

learned formulas in a GBFS to break ties in favor

of progress states often significantly reduces the

search effort.
Completeness and Diversity in Depth-First Proof-Number Search with Applications to Retrosynthesis
Authors: Franz Christopher , Mogk Georg , Mrziglod Thomas , Schewior Kevin
Published: July 2022

We revisit Depth-First Proof-Number Search (DFPN), a well-known algorithm for solving two-player games. First, we consider the completeness property of the algorithm and its variants, i.e., whether they always find a winning strategy when there exists one. While it is known that the standard version is not complete, we show that the combination with the simple Threshold Controlling Algorithm is complete, solving an open problem from the area. Second, we modify DFPN to compute a diverse set of solutions rather than just a single one. Finally, we apply this new variant in Chemistry to the synthesis planning of new target molecules (Retrosynthesis). In this domain a diverse set of many solutions is desirable. We apply additional modifications from the literature to the algorithm and show that it outperforms Monte-Carlo Tree-Search, another well-known algorithm for the same problem, according to a natural diversity measure.
Large Neighborhood Search with Decision Diagrams
Authors: Gillard Xavier , Schaus Pierre
Published: July 2022

Local search is a popular technique to solve combinatorial

optimization problems efficiently. To escape local minima one generally uses

metaheuristics or try to design large neighborhoods around the current best

solution. A somewhat more black box approach consists in using an

optimization solver to explore a large neighborhood. This is the

large-neighborhood search (LNS) idea that we reuse in this work. We

introduce a generic neighborhood exploration algorithm based on restricted

decision diagrams (DD) constructed from the current best

solution. We experiment DD-LNS on two sequencing problems: the

traveling salesman problem with time windows (TSPTW) and a production

planning problem (DLSP). Despite its simplicity, DD-LNS is competitive with

the state-of-the-art MIP approach on DLSP. It is able to improve the

best known solutions of some standard instances for TSPTW and even to prove

the optimality of quite a few other instances.
Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables
Authors: Neumann Frank , Witt Carsten
Published: July 2022

Chance constrained optimization problems allow to model problems where constraints involving stochastic components should only be violated with a small probability. Evolutionary algorithms have been applied to this scenario and shown to achieve high quality results. With this paper, we contribute to the theoretical understanding of evolutionary algorithms for chance constrained optimization. We study the scenario of stochastic components that are independent and Normally distributed.

Considering the simple single-objective (1+1)~EA, we show that imposing an additional uniform constraint already leads to local optima for very restricted scenarios and an exponential optimization time. We therefore introduce a multi-objective formulation of the problem which trades off the expected cost and its variance. We show that multi-objective evolutionary algorithms are highly effective when using this formulation and obtain a set of solutions that contains an optimal solution for any possible confidence level imposed on the constraint. Furthermore, we prove that this approach can also be used to compute a set of optimal solutions for the chance constrained minimum spanning tree problem. Experimental investigations on instances of the NP-hard stochastic minimum weight dominating set problem confirm the benefit of the multi-objective approach in practice.
A Weighting-Based Tabu Search Algorithm for the p-Next Center Problem
Authors: Zhang Qingyun , Su Zhouxing , Lü Zhipeng , Yang Lingxiao
Published: July 2022

The p-next center problem (pNCP) is an extension of the classical p-center problem. It consists of locating p centers from a set of candidate centers and allocating both a reference and a backup center to each client, to minimize the maximum cost, which is the length of the path from a client to its reference center and then to its backup center. Among them, the reference center is the closest center to a client and serves it under normal circumstances, while the backup center is the closest center to the reference center and serves the client when the reference center is out of service. In this paper, we propose a weighting-based tabu search algorithm called WTS for solving pNCP. WTS optimizes the pNCP by solving its decision subproblems with given assignment costs with an efficient swap-based neighborhood structure and a hierarchical penalty strategy for neighborhood evaluation. Extensive experimental studies on 413 benchmark instances demonstrate that WTS outperforms the state-of-the-art methods in the literature. Specifically, WTS improves 12 previous best known results and matches the optimal results for all remaining 401 ones in a much shorter time than other algorithms. More importantly, WTS reaches the lower bounds for 10 instances for the first time.
Exchangeability-Aware Sum-Product Networks
Authors: Lüdtke Stefan , Bartelt Christian , Stuckenschmidt Heiner
Published: July 2022

Sum-Product Networks (SPNs) are expressive probabilistic models that provide exact, tractable inference. They achieve this efficiency by making use of local independence.

On the other hand, mixtures of exchangeable variable models (MEVMs) are a class of tractable probabilistic models that make use of exchangeability of discrete random variables to render inference tractable. Exchangeability, which arises naturally in relational domains, has not been considered for efficient representation and inference in SPNs yet.

The contribution of this paper is a novel probabilistic model which we call Exchangeability-Aware Sum-Product Networks (XSPNs). It contains both SPNs and MEVMs as special cases, and combines the ability of SPNs to efficiently learn deep probabilistic models with the ability of MEVMs to efficiently handle exchangeable random variables. We introduce a structure learning algorithm for XSPNs and empirically show that they can be more accurate than conventional SPNs when the data contains repeated, interchangeable parts.
Robustness Guarantees for Credal Bayesian Networks via Constraint Relaxation over Probabilistic Circuits
Authors: Wijk Hjalmar , Wang Benjie , Kwiatkowska Marta
Published: July 2022

In many domains, worst-case guarantees on the performance (e.g. prediction accuracy) of a decision function subject to distributional shifts and uncertainty about the environment are crucial. In this work we develop a method to quantify the robustness of decision functions with respect to credal Bayesian networks, formal parametric models of the environment where uncertainty is expressed through credal sets on the parameters. In particular, we address the maximum marginal probability (MARmax) problem, that is, determining the greatest probability of an event (such as misclassification) obtainable for parameters in the credal set. We develop a method to faithfully transfer the problem into a constrained optimization problem on a probabilistic circuit. By performing a simple constraint relaxation, we show how to obtain a guaranteed upper bound on MARmax in linear time in the size of the circuit. We further theoretically characterize this constraint relaxation in terms of the original Bayesian network structure, which yields insight into the tightness of the bound. We implement the method and provide experimental evidence that the upper bound is often near tight and demonstrates improved scalability compared to other methods.
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks
Authors: Zeng Huimin , Yue Zhenrui , Zhang Yang , Kou Ziyi , Shang Lanyu , Wang Dong
Published: July 2022

In many applications with real-world consequences, it is crucial to develop reliable uncertainty estimation for the predictions made by the AI decision systems. Targeting at the goal of estimating uncertainty, various deep neural network (DNN) based uncertainty estimation algorithms have been proposed. However, the robustness of the uncertainty returned by these algorithms has not been systematically explored. In this work, to raise the awareness of the research community on robust uncertainty estimation, we show that state-of-the-art uncertainty estimation algorithms could fail catastrophically under our proposed adversarial attack despite their impressive performance on uncertainty estimation. In particular, we aim at attacking out-domain uncertainty estimation: under our attack, the uncertainty model would be fooled to make high-confident predictions for the out-domain data, which they originally would have rejected. Extensive experimental results on various benchmark image datasets show that the uncertainty estimated by state-of-the-art methods could be easily corrupted by our attack.
Deep Interactive Surface Creation from 3D Sketch Strokes
Authors: Bhattacharjee Sukanya , Chaudhuri Parag
Published: July 2022

We present a deep neural framework that allows users to create surfaces from a stream of sparse 3D sketch strokes. Our network consists of a global surface estimation module followed by a local surface refinement. This facilitates in the incremental prediction of surfaces. Thus, our proposed method works with 3D sketch strokes and estimate a surface interactively in real time. We compare the proposed method with various state-of-the-art methods and show its efficacy for surface fitting. Further, we integrate our method into an existing Blender based 3D content creation pipeline to show its usefulness in 3D modelling.
Towards Creativity Characterization of Generative Models via Group-Based Subset Scanning
Authors: Cintas Celia , Das Payel , Quanz Brian , Tadesse Girmaw Abebe , Speakman Skyler , Chen Pin-Yu
Published: July 2022

Deep generative models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have been employed widely in computational creativity research. However, such models discourage out-of-distribution generation to avoid spurious sample generation, thereby limiting their creativity. Thus, incorporating research on human creativity into generative deep learning techniques presents an opportunity to make their outputs more compelling and human-like. As we see the emergence of generative models directed toward creativity research, a need for machine learning-based surrogate metrics to characterize creative output from these models is imperative.

We propose group-based subset scanning to identify, quantify, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of the generative models.

Our experiments on the standard image benchmarks and their ``creatively generated'' variants reveal that the proposed subset scores distribution is more useful for detecting novelty in creative processes in the activation space rather than the pixel space. Further, we found that creative samples generate larger subsets of anomalies than normal or non-creative samples across datasets. The node activations highlighted during the creative decoding process are different from those responsible for the normal sample generation. Lastly, we assess if the images from the subsets selected by our method were also found creative by human evaluators, presenting a link between creativity perception in humans and node activations within deep neural nets.
Art Creation with Multi-Conditional StyleGANs
Authors: Dobler Konstantin , Hübscher Florian , Westphal Jan , Sierra-Múnera Alejandro , de Melo Gerard , Krestel Ralf
Published: July 2022

Creating art is often viewed as a uniquely human endeavor. In this paper, we introduce a multi-conditional Generative Adversarial Network (GAN) approach trained on large amounts of human paintings to synthesize realistic-looking paintings that emulate human art. Our approach is based on the StyleGAN neural network architecture, but incorporates a custom multi-conditional control mechanism that provides fine-granular control over characteristics of the generated paintings, e.g., with regard to the perceived emotion evoked in a spectator. We also investigate several evaluation techniques tailored to multi-conditional generation.
Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems
Authors: Halina Emily , Guzdial Matthew
Published: July 2022

To best assist human designers with different styles, Machine Learning (ML) systems need to be able to adapt to them. However, there has been relatively little prior work on how and when to best adapt an ML system to a co-designer. In this paper we present threshold designer adaptation: a novel method for adapting a creative ML model to an individual designer. We evaluate our approach with a human subject study using a co-creative rhythm game design tool. We find that designers prefer our proposed method and produce higher quality content in comparison to an existing baseline.
Dataset Augmentation in Papyrology with Generative Models: A Study of Synthetic Ancient Greek Character Images
Authors: Swindall Matthew I. , Player Timothy , Keener Ben , Williams Alex C. , Brusuelas James H. , Nicolardi Federica , D'Angelo Marzia , Vergara Claudio , McOsker Michael , Wallin John F.
Published: July 2022

Character recognition models rely substantially on image datasets that maintain a balance of class samples. However, achieving a balance of classes is particularly challenging for ancient manuscript contexts as character instances may be significantly limited. In this paper, we present findings from a study that assess the efficacy of using synthetically generated character instances to augment an existing dataset of ancient Greek character images for use in machine learning models. We complement our model exploration by engaging professional papyrologists to better understand the practical opportunities afforded by synthetic instances. Our results suggest that synthetic instances improve model performance for limited character classes, and may have unexplored effects on character classes more generally. We also find that trained papyrologists are unable to distinguish between synthetic and non-synthetic images and regard synthetic instances as valuable assets for professional and educational contexts. We conclude by discussing the practical implications of our research.
Composition-aware Graphic Layout GAN for Visual-Textual Presentation Designs
Authors: Zhou Min , Xu Chenchen , Ma Ye , Ge Tiezheng , Jiang Yuning , Xu Weiwei
Published: July 2022

In this paper, we study the graphic layout generation problem of producing high-quality visual-textual presentation designs for given images. We note that image compositions, which contain not only global semantics but also spatial information, would largely affect layout results. Hence, we propose a deep generative model, dubbed as composition-aware graphic layout GAN (CGL-GAN), to synthesize layouts based on the global and spatial visual contents of input images. To obtain training images from images that already contain manually designed graphic layout data, previous work suggests masking design elements (e.g., texts and embellishments) as model inputs, which inevitably leaves hint of the ground truth. We study the misalignment between the training inputs (with hint masks) and test inputs (without masks), and design a novel domain alignment module (DAM) to narrow this gap. For training, we built a large-scale layout dataset which consists of 60,548 advertising posters with annotated layout information. To evaluate the generated layouts, we propose three novel metrics according to aesthetic intuitions. Through both quantitative and qualitative evaluations, we demonstrate that the proposed model can synthesize high-quality graphic layouts according to image compositions. The data and code will be available at https://github.com/minzhouGithub/CGL-GAN.
Learning to Generate Poetic Chinese Landscape Painting with Calligraphy
Authors: Yuan Shaozu , Dai Aijun , Yan Zhiling , Liu Ruixue , Chen Meng , Chen Baoyang , Qiu Zhijie , He Xiaodong
Published: July 2022

In this paper, we present a novel system (denoted as Polaca) to generate poetic Chinese landscape painting with calligraphy. Unlike previous single image-to-image painting generation, Polaca takes the classic poetry as input and outputs the artistic landscape painting image with the corresponding calligraphy. It is equipped with three different modules to complete the whole piece of landscape painting artwork: the first one is a text-to-image module to generate landscape painting image, the second one is an image-to-image module to generate stylistic calligraphy image, and the third one is an image fusion module to fuse the two images into a whole piece of aesthetic artwork.
Learning Pollution Maps from Mobile Phone Images
Authors: Bhardwaj Ankit , Iyer Shiva , Jalan Yash , Subramanian Lakshminarayanan
Published: July 2022

Air pollution monitoring and management is one of the key challenges for urban sectors, especially in developing countries. Measuring pollution levels requires significant investment in reliable and durable instrumentation and subsequent maintenance. On the other hand, there have been many attempts by researchers to develop image-based pollution measurement models which have shown significant results and established the feasibility of the idea. But, taking image-level models to a city-level system presents new challenges, which include scarcity of high-quality annotated data and a high amount of label noise. In this paper, we present a low-cost, end-to-end system for learning pollution maps using images captured through a mobile phone. We demonstrate our system for parts of New Delhi and Ghaziabad. We use transfer learning to overcome the problem of data scarcity. We investigate the effects of label noise in detail and introduce the metric of in-interval accuracy to evaluate our models in presence of noise. We use distributed averaging to learn pollution maps and mitigate the effects of noise to some extent. We also develop haze-based interpretable models which have comparable performance to mainstream models. With only 382 images from Delhi and Ghaziabad and single-scene dataset from Beijing and Shanghai, we are able to achieve a mean absolute error of 44 ug/m^3 in PM2.5 concentration on a test set of 267 images and an in-interval accuracy of 67% on predictions. Going further, we learn pollution maps with a mean absolute error as low as 35 ug/m^3 and in-interval accuracy as high as 74% significantly mitigating the image models' error. We also show that the noise in pollution labels emerging from unreliable sensing instrumentation forms a significant barrier to the realization of an ideal air pollution monitoring system. Our codebase can be found at https://github.com/ankitbha/pollution_with_images.
Deciphering Environmental Air Pollution with Large Scale City Data
Authors: Bhattacharyya Mayukh , Nag Sayan , Ghosh Udita
Published: July 2022

Air pollution poses a serious threat to sustainable environmental conditions in the 21st century. Its importance in determining the health and living standards in urban settings is only expected to increase with time. Various factors ranging from artificial emissions to natural phenomena are known to be primary causal agents or influencers behind rising air pollution levels. However, the lack of large scale data involving the major artificial and natural factors has hindered the research on the causes and relations governing the variability of the different air pollutants. Through this work, we introduce a large scale city-wise dataset for exploring the relationships among these agents over a long period of time. We also introduce a transformer based model - cosSquareFormer, for the problem of pollutant level estimation and forecasting. Our model outperforms most of the benchmark models for this task. We also analyze and explore the dataset through our model and other methodologies to bring out important inferences which enable us to understand the dynamics of the casual agents at a deeper level. Through our paper, we seek to provide a great set of foundations for further research into this domain that will demand critical attention of ours in the near future.
S2SNet: A Pretrained Neural Network for Superconductivity Discovery
Authors: Liu Ke , Yang Kaifan , Zhang Jiahong , Xu Renjun
Published: July 2022

Superconductivity allows electrical current to flow without any energy loss, and thus making solids superconducting is a grand goal of physics, material science, and electrical engineering. More than 16 Nobel Laureates have been awarded for their contribution in superconductivity research. Superconductors are valuable for sustainable development goals (SDGs), such as climate change mitigation, affordable and clean energy, industry, innovation and infrastructure, and so on. However, a unified physics theory explaining all superconductivity mechanism is still unknown. It is believed that superconductivity is microscopically due to not only molecular compositions but also the geometric crystal structure. Hence a new dataset, S2S, containing both crystal structures and superconducting critical temperature, is built upon SuperCon and Material Project. Based on this new dataset, we propose a novel model, S2SNet, which utilizes the attention mechanism for superconductivity prediction. To overcome the shortage of data, S2SNet is pre-trained on the whole Material Project dataset with Masked-Language Modeling (MLM). S2SNet makes a new state-of-the-art, with out-of-sample accuracy of 92% and Area Under Curve (AUC) of 0.92. To the best of our knowledge, S2SNet is the first work to predict superconductivity with only information of crystal structures. This work is beneficial to superconductivity discovery and further SDGs. The code and datasets are available at https://github.com/supercond/S2SNet
How to Sample Approval Elections?
Authors: Szufa Stanisław , Faliszewski Piotr , Janeczko Łukasz , Lackner Martin , Slinko Arkadii , Sornat Krzysztof , Talmon Nimrod
Published: July 2022

We extend the map-of-elections framework to the case of approval elections. While doing so, we study a number of statistical cultures, including some new ones, and we analyze their properties. We find that approval elections can be understood in terms of the average number of approvals in the votes, and the extent to which the votes are chaotic.
A Reliability-aware Distributed Framework to Schedule Residential Charging of Electric Vehicles
Authors: Meyur Rounak , Thorve Swapna , Marathe Madhav , Vullikanti Anil , Swarup Samarth , Mortveit Henning
Published: July 2022

Residential consumers have become active participants in the power distribution network after being equipped with residential EV charging provisions. This creates a challenge for the network operator tasked with dispatching electric power to the residential consumers through the existing distribution network infrastructure in a reliable manner. In this paper, we address the problem of scheduling residential EV charging for multiple consumers while maintaining network reliability. An additional challenge is the restricted exchange of information: where the consumers do not have access to network information and the network operator does not have access to consumer load parameters. We propose a distributed framework which generates an optimal EV charging schedule for individual residential consumers based on their preferences and iteratively updates it until the network reliability constraints set by the operator are satisfied. We validate the proposed approach for different EV adoption levels in a synthetically created digital twin of an actual power distribution network. The results demonstrate that the new approach can achieve a higher level of network reliability compared to the case where residential consumers charge EVs based solely on their individual preferences, thus providing a solution for the existing grid to keep up with increased adoption rates without significant investments in increasing grid capacity.
ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria
Authors: Nair Vineet , Prakash Kritika , Wilbur Michael , Taneja Aparna , Namblard Corrine , Adeyemo Oyindamola , Dubey Abhishek , Adereni Abiodun , Tambe Milind , Mukhopadhyay Ayan
Published: July 2022

More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in under-developed countries with low vaccination uptake. One of the United Nations' sustainable development goals (SDG 3) aims to end preventable deaths of newborns and children under five years of age. We focus on Nigeria, where the rate of infant mortality is appalling. We collaborate with HelpMum, a large non-profit organization in Nigeria, to design and optimize the allocation of heterogeneous health interventions under uncertainty to increase vaccination uptake, the first such collaboration in Nigeria. Our framework, ADVISER: AI-Driven Vaccination Intervention Optimiser, is based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination. Our optimization formulation is intractable in practice. We present a heuristic approach that enables us to solve the problem for real-world use-cases. We also present theoretical bounds for the heuristic method. Finally, we show that the proposed approach outperforms baseline methods in terms of vaccination uptake through experimental evaluation. HelpMum is currently planning a pilot program based on our approach to be deployed in the largest city of Nigeria, which would be the first deployment of an AI-driven vaccination uptake program in the country and hopefully, pave the way for other data-driven programs to improve health outcomes in Nigeria.
Scalable and Memory-Efficient Algorithms for Controlling Networked Epidemic Processes Using Multiplicative Weights Update Method
Authors: Sambaturu Prathyush , Minutoli Marco , Halappanavar Mahantesh , Kalyanaraman Ananth , Vullikanti Anil
Published: July 2022

We study the problem of designing scalable algorithms to find effective intervention strategies for controlling stochastic epidemic processes on networks. This is a common problem arising in agent based models for epidemic spread.

Previous approaches to this problem focus on either heuristics with no guarantees or approximation algorithms that scale only to networks corresponding to county-sized populations, typically, with less than a million nodes. In particular, the mathematical-programming based approaches need to solve the Linear Program (LP) relaxation of the problem using an LP solver, which restricts the scalability of this approach. In this work, we overcome this restriction by designing an algorithm that adapts the multiplicative weights update (MWU) framework, along with the sample average approximation (SAA) technique, to approximately solve the linear program (LP) relaxation for the problem. To scale this approach further, we provide a memory-efficient algorithm that enables scaling to large networks, corresponding to country-size populations, with over 300 million nodes and 30 billion edges. Furthermore, we show that this approach provides near-optimal solutions to the LP in practice.
Forecasting the Number of Tenants At-Risk of Formal Eviction: A Machine Learning Approach to Inform Public Policy
Authors: Tabar Maryam , Jung Wooyong , Yadav Amulya , Wilson Chavez Owen , Flores Ashley , Lee Dongwon
Published: July 2022

Eviction of tenants has reached a crisis level in the U.S. and its consequences pose significant challenges to society. To tackle this eviction crisis, policymakers have been allocating financial resources but a more efficient resource allocation would need an accurate forecast of the number of tenants at-risk of evictions ahead of time. To help enhance the existing eviction prevention/diversion programs, in this work, we propose a multi-view deep neural network model, named as MARTIAN, that forecasts the number of tenants at-risk of getting formally evicted (at the census tract level) n months into the future. Then, we evaluate MARTIAN’s predictive performance under various conditions using real-world eviction cases filed across Dallas County, TX. The results of empirical evaluation show that MARTIAN outperforms an extensive set of baseline models in terms of predictive performance. Additionally, MARTIAN’s superior predictive performance is generalizable to unseen census tracts, for which no labeled data is available in the training set. This research has been done in collaboration with Child Poverty Action Lab (CPAL), which is a pioneering non-governmental organization (NGO) working for tackling poverty-related issues across Dallas County, TX. The usability of MARTIAN is under review by subject matter experts. We release our codebase at https://github.com/maryam-tabar/MARTIAN.