bluuebunny/crossref_metadata_2025 huggingface dataset:
Sparse Enhanced Network: An Adversarial Generation Method for Robust Augmentation in Sequential Recommendation
Authors: Chen Junyang , Zou Guoxuan , Zhou Pan , Yirui Wu , Chen Zhenghan , Su Houcheng , Wang Huan , Gong Zhiguo
Published: March 2024

Sequential Recommendation plays a significant role in daily recommendation systems, such as e-commerce platforms like Amazon and Taobao. However, even with the advent of large models, these platforms often face sparse issues in the historical browsing records of individual users due to new users joining or the introduction of new products. As a result, existing sequence recommendation algorithms may not perform well. To address this, sequence-based data augmentation methods have garnered attention.

Existing sequence enhancement methods typically rely on augmenting existing data, employing techniques like cropping, masking prediction, random reordering, and random replacement of the original sequence. While these methods have shown improvements, they often overlook the exploration of the deep embedding space of the sequence. To tackle these challenges, we propose a Sparse Enhanced Network (SparseEnNet), which is a robust adversarial generation method. SparseEnNet aims to fully explore the hidden space in sequence recommendation, generating more robust enhanced items. Additionally, we adopt an adversarial generation method, allowing the model to differentiate between data augmentation categories and achieve better prediction performance for the next item in the sequence. Experiments have demonstrated that our method achieves a remarkable 4-14% improvement over existing methods when evaluated on the real-world datasets. (https://github.com/junyachen/SparseEnNet)
Efficient Representation Learning of Satellite Image Time Series and Their Fusion for Spatiotemporal Applications
Authors: Goyal Poonam , Kaur Arshveer , Ram Arvind , Goyal Navneet
Published: March 2024

Satellite data bolstered by their increasing accessibility is leading to many endeavors of automated monitoring of the earth's surface for various applications. Such applications demand high spatial resolution images at a temporal resolution of a few days which entails the challenge of processing a huge volume of image time series data. To overcome this computing bottleneck, we present PatchNet, a bespoke adaptation of beam search and attention mechanism. PatchNet is an automated patch selection neural network that requires only a partial spatial traversal of an image time series and yet achieves impressive results. Satellite systems face a trade-off between spatial and temporal resolutions due to budget/technical constraints e.g., Landsat-8/9 or Sentinel-2 have high spatial resolution whereas, MODIS has high temporal resolution. To deal with the limitation of coarse temporal resolution, we propose FuSITSNet, a twofold feature-based generic fusion model with multimodal learning in a contrastive setting. It produces a learned representation after fusion of two satellite image time series leveraging finer spatial resolution of Landsat and finer temporal resolution of MODIS. The patch alignment module of FuSITSNet aligns the PatchNet processed patches of Landsat-8 with the corresponding MODIS regions to incorporate its finer resolution temporal features. The untraversed patches are handled by the cross-modality attention which highlights additional hot spot features from the two modalities. We conduct extensive experiments on more than 2000 counties of US for crop yield, snow cover, and solar energy prediction and show that even one-fourth spatial processing of image time series produces state-of-the-art results. FuSITSNet outperforms the predictions of single modality and data obtained using existing generative fusion models and allows for monitoring of dynamic phenomena using freely accessible images, thereby unlocking new opportunities.
Label Attentive Distillation for GNN-Based Graph Classification
Authors: Hong Xiaobin , Li Wenzhong , Wang Chaoqun , Lin Mingkai , Lu Sanglu
Published: March 2024

Graph Neural Networks (GNNs) have emerged as a powerful tool for modeling graph-structured data, exhibiting remarkable potential in applications such as social networks, recommendation systems, and molecular structures. However, the conventional GNNs perform node-level feature aggregation from neighbors without considering graph-label information, which leads to the misaligned embedding problem that may cause a detrimental effect on graph-level tasks such as graph classification. In this paper, we propose a novel label-attentive distillation method called LAD-GNN for graph representation learning to solve this problem. It alternatively trains a teacher model and a student GNN with a distillation-based approach. In the teacher model, a label-attentive encoder is proposed to encode the label information fusing with the node features to generate ideal embedding. In the student model, the ideal embedding is used as intermediate supervision to urge the student GNN to learn class-friendly node embedding to facilitate graph-level tasks. Generally, LAD-GNN is an enhanced GNN training approach that can be incorporated with arbitrary GNN backbone to improve performance without significant increase of computational cost. Extensive experiments with 7 GNN backbones based on 10 benchmark datasets show that LAD-GNN improves the SOTA GNNs in graph classification accuracy. The source codes of LAD-GNN are publicly available on https://github.com/XiaobinHong/LAD-GNN.
AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model
Authors: Hu Teng , Zhang Jiangning , Yi Ran , Du Yuzhen , Chen Xu , Liu Liang , Wang Yabiao , Wang Chengjie
Published: March 2024

Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. Although anomaly generation methods have been proposed to augment the anomaly data, they either suffer from poor generation authenticity or inaccurate alignment between the generated anomalies and masks. To address the above problems, we propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model, which utilizes the strong prior information of latent diffusion model learned from large-scale dataset to enhance the generation authenticity under few-shot training data. Firstly, we propose Spatial Anomaly Embedding, which consists of a learnable anomaly embedding and a spatial embedding encoded from an anomaly mask, disentangling the anomaly information into anomaly appearance and location information. Moreover, to improve the alignment between the generated anomalies and the anomaly masks, we introduce a novel Adaptive Attention Re-weighting Mechanism. Based on the disparities between the generated anomaly image and normal sample, it dynamically guides the model to focus more on the areas with less noticeable generated anomalies, enabling generation of accurately-matched anomalous image-mask pairs. Extensive experiments demonstrate that our model significantly outperforms the state-of-the-art methods in generation authenticity and diversity, and effectively improves the performance of downstream anomaly inspection tasks. The code and data are available in https://github.com/sjtuplayer/anomalydiffusion.
ReGCL: Rethinking Message Passing in Graph Contrastive Learning
Authors: Ji Cheng , Huang Zixuan , Sun Qingyun , Peng Hao , Fu Xingcheng , Li Qian , Li Jianxin
Published: March 2024

Graph contrastive learning (GCL) has demonstrated remarkable efficacy in graph representation learning. However, previous studies have overlooked the inherent conflict that arises when employing graph neural networks (GNNs) as encoders for node-level contrastive learning. This conflict pertains to the partial incongruity between the feature aggregation mechanism of graph neural networks and the embedding distinction characteristic of contrastive learning. Theoretically, to investigate the location and extent of the conflict, we analyze the participation of message-passing from the gradient perspective of InfoNCE loss. Different from contrastive learning in other domains, the conflict in GCL arises due to the presence of certain samples that contribute to both the gradients of positive and negative simultaneously under the manner of message passing, which are opposite optimization directions. To further address the conflict issue, we propose a practical framework called ReGCL, which utilizes theoretical findings of GCL gradients to effectively improve graph contrastive learning. Specifically, two gradient-based strategies are devised in terms of both message passing and loss function to mitigate the conflict. Firstly, a gradient-guided structure learning method is proposed in order to acquire a structure that is adapted to contrastive learning principles. Secondly, a gradient-weighted InfoNCE loss function is designed to reduce the impact of false negative samples with high probabilities, specifically from the standpoint of the graph encoder. Extensive experiments demonstrate the superiority of the proposed method in comparison to state-of-the-art baselines across various node classification benchmarks.
ResDiff: Combining CNN and Diffusion Model for Image Super-resolution
Authors: Shang Shuyao , Shan Zhengyang , Liu Guangxing , Wang LunQian , Wang XingHua , Zhang Zekai , Zhang Jinglin
Published: March 2024

Adapting the Diffusion Probabilistic Model (DPM) for direct image super-resolution is wasteful, given that a simple Convolutional Neural Network (CNN) can recover the main low-frequency content. Therefore, we present ResDiff, a novel Diffusion Probabilistic Model based on Residual structure for Single Image Super-Resolution (SISR). ResDiff utilizes a combination of a CNN, which restores primary low-frequency components, and a DPM, which predicts the residual between the ground-truth image and the CNN predicted image. In contrast to the common diffusion-based methods that directly use LR space to guide the noise towards HR space, ResDiff utilizes the CNN’s initial prediction to direct the noise towards the residual space between HR space and CNN-predicted space, which not only accelerates the generation process but also acquires superior sample quality. Additionally, a frequency-domain-based loss function for CNN is introduced to facilitate its restoration, and a frequency-domain guided diffusion is designed for DPM on behalf of predicting high-frequency details. The extensive experiments on multiple benchmark datasets demonstrate that ResDiff outperforms previous diffusion based methods in terms of shorter model convergence time, superior generation quality, and more diverse samples.
Temporal Graph Contrastive Learning for Sequential Recommendation
Authors: Zhang Shengzhe , Chen Liyi , Wang Chao , Li Shuangli , Xiong Hui
Published: March 2024

Sequential recommendation is a crucial task in understanding users' evolving interests and predicting their future behaviors. While existing approaches on sequence or graph modeling to learn interaction sequences of users have shown promising performance, how to effectively exploit temporal information and deal with the uncertainty noise in evolving user behaviors is still quite challenging. To this end, in this paper, we propose a Temporal Graph Contrastive Learning method for Sequential Recommendation (TGCL4SR) which leverages not only local interaction sequences but also global temporal graphs to comprehend item correlations and analyze user behaviors from a temporal perspective. Specifically, we first devise a Temporal Item Transition Graph (TITG) to fully leverage global interactions to understand item correlations, and augment this graph by dual transformations based on neighbor sampling and time disturbance. Accordingly, we design a Temporal item Transition graph Convolutional network (TiTConv) to capture temporal item transition patterns in TITG. Then, a novel Temporal Graph Contrastive Learning (TGCL) mechanism is designed to enhance the uniformity of representations between augmented graphs from identical sequences. For local interaction sequences, we design a temporal sequence encoder to incorporate time interval embeddings into the architecture of Transformer. At the training stage, we take maximum mean discrepancy and TGCL losses as auxiliary objectives. Extensive experiments on several real-world datasets show the effectiveness of TGCL4SR against state-of-the-art baselines of sequential recommendation.
GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting
Authors: Zhou Fan , Pan Chen , Ma Lintao , Liu Yu , Xue Siqiao , Zhang James , Zhou Jun , Mei Hongyuan , Lin Weitao , Zhuang Zi , Ning Wenxin , Hu Yunhua
Published: March 2024

Time series forecasts of different temporal granularity are widely used in real-world applications, e.g., sales prediction in days and weeks for making different inventory plans. However, these tasks are usually solved separately without ensuring coherence, which is crucial for aligning downstream decisions. Previous works mainly focus on ensuring coherence with some straightforward methods, e.g., aggregation from the forecasts of fine granularity to the coarse ones, and allocation from the coarse granularity to the fine ones. These methods merely take the temporal hierarchical structure to maintain coherence without improving the forecasting accuracy. In this paper, we propose a novel granularity message-passing mechanism (GMP) that leverages temporal hierarchy information to improve forecasting performance and also utilizes an adaptive reconciliation (AR) strategy to maintain coherence without performance loss. Furthermore, we introduce an optimization module to achieve task-based targets while adhering to more real-world constraints. Experiments on real-world datasets demonstrate that our framework (GMP-AR) achieves superior performances on temporal hierarchical forecasting tasks compared to state-of-the-art methods. In addition, our framework has been successfully applied to a real-world task of payment traffic management in Alipay by integrating with the task-based optimization module.
Content Filtering with Inattentive Information Consumers
Authors: Ball Ian , Bono James , Grana Justin , Immorlica Nicole , Lucier Brendan , Slivkins Aleksandrs
Published: March 2024

We develop a model of content filtering as a game between the filter and the content consumer, where the latter incurs information costs for examining the content. Motivating examples include censoring misinformation, spam/phish filtering, and recommender systems acting on a stream of content. When the attacker is exogenous, we show that improving the filter’s quality is weakly Pareto improving, but has no impact on equilibrium payoffs until the filter becomes sufficiently accurate. Further, if the filter does not internalize the consumer’s information costs, its lack of commitment power may render it useless and lead to inefficient outcomes. When the attacker is also strategic, improvements in filter quality may decrease equilibrium payoffs.
How to Evaluate Behavioral Models
Authors: D'Eon Greg , Greenwood Sophie , Leyton-Brown Kevin , Wright James R.
Published: March 2024

Researchers building behavioral models, such as behavioral game theorists, use experimental data to evaluate predictive models of human behavior. However, there is little agreement about which loss function should be used in evaluations, with error rate, negative log-likelihood, cross-entropy, Brier score, and squared L2 error all being common choices. We attempt to offer a principled answer to the question of which loss functions should be used for this task, formalizing axioms that we argue loss functions should satisfy. We construct a family of loss functions, which we dub ``diagonal bounded Bregman divergences'', that satisfy all of these axioms. These rule out many loss functions used in practice, but notably include squared L2 error; we thus recommend its use for evaluating behavioral models.
Manipulation-Robust Selection of Citizens’ Assemblies
Authors: Flanigan Bailey , Liang Jennifer , Procaccia Ariel D. , Wang Sven
Published: March 2024

Among the recent work on designing algorithms for selecting citizens' assembly participants, one key property of these algorithms has not yet been studied: their manipulability. Strategic manipulation is a concern because these algorithms must satisfy representation constraints according to volunteers' self-reported features; misreporting these features could thereby increase a volunteer's chance of being selected, decrease someone else's chance, and/or increase the expected number of seats given to their group. Strikingly, we show that Leximin — an algorithm that is widely used for its fairness — is highly manipulable in this way. We then introduce a new class of selection algorithms that use Lp norms as objective functions. We show that the manipulability of the Lp-based algorithm decreases in O(1/n^(1-1/p)) as the number of volunteers n grows, approaching the optimal rate of O(1/n) as p approaches infinity. These theoretical results are confirmed via experiments in eight real-world datasets.
An Exercise in Tournament Design: When Some Matches Must Be Scheduled
Authors: Gupta Sushmita , Sridharan Ramanujan , Strulo Peter
Published: March 2024

Single-elimination (SE) tournaments are a popular format used in competitive environments and decision making. Algorithms for SE tournament manipulation have been an active topic of research in recent years. In this paper, we initiate the algorithmic study of a novel variant of SE tournament manipulation that aims to model the fact that certain matchups are highly desired in a sporting context, incentivizing an organizer to manipulate the bracket to make such matchups take place. We obtain both hardness and tractability results. We show that while the problem of computing a bracket enforcing a given set of matches in an SE tournament is NP-hard, there are natural restrictions that lead to polynomial-time solvability. In particular, we show polynomial-time solvability if there is a linear ordering on the ability of players with only a constant number of exceptions where a player with lower ability beats a player with higher ability.
Proportional Representation in Metric Spaces and Low-Distortion Committee Selection
Authors: Kalayci Yusuf , Kempe David , Kher Vikram
Published: March 2024

We introduce a novel definition for a small set R of k points being "representative" of a larger set in a metric space. Given a set V (e.g., documents or voters) to represent, and a set C of possible representatives, our criterion requires that for any subset S comprising a theta fraction of V, the average distance of S to their best theta*k points in R should not be more than a factor gamma compared to their average distance to the best theta*k points among all of C. This definition is a strengthening of proportional fairness and core fairness, but - different from those notions - requires that large cohesive clusters be represented proportionally to their size.

Since there are instances for which - unless gamma is polynomially large - no solutions exist, we study this notion in a resource augmentation framework, implicitly stating the constraints for a set R of size k as though its size were only k/alpha, for alpha > 1. Furthermore, motivated by the application to elections, we mostly focus on the "ordinal" model, where the algorithm does not learn the actual distances; instead, it learns only for each point v in V and each candidate pairs c, c' which of c, c' is closer to v. Our main result is that the Expanding Approvals Rule (EAR) of Aziz and Lee is (alpha, gamma) representative with gamma
Computing Nash Equilibria in Potential Games with Private Uncoupled Constraints
Authors: Patris Nikolas , Stavroulakis Stelios , Kalogiannis Fivos , Zhang Rose , Panageas Ioannis
Published: March 2024

We consider the problem of computing Nash equilibria in potential games where each player's strategy set is subject to private uncoupled constraints. This scenario is frequently encountered in real-world applications like road network congestion games where individual drivers adhere to personal budget and fuel limitations. Despite the plethora of algorithms that efficiently compute Nash equilibria (NE) in potential games, the domain of constrained potential games remains largely unexplored. We introduce an algorithm that leverages the Lagrangian formulation of NE. The algorithm is implemented independently by each player and runs in polynomial time with respect to the approximation error, the sum of the size of the action-spaces, and the game's inherit parameters.
Simultaneous Optimization of Bid Shading and Internal Auction for Demand-Side Platforms
Authors: Xu Yadong , Ni Bonan , Shen Weiran , Wang Xun , Wang Zichen , Xue Yinsong , Tang Pingzhong
Published: March 2024

Online advertising has been one of the most important sources for industry's growth, where the demand-side platforms (DSP) play an important role via bidding to the ad exchanges on behalf of their advertiser clients. Since more and more ad exchanges have shifted from second to first price auctions, it is challenging for DSPs to adjust bidding strategy in the volatile environment. Recent studies on bid shading in first-price auctions may have limited performance due to relatively strong hypotheses about winning probability distribution. Moreover, these studies do not consider the incentive of advertiser clients, which can be crucial for a reliable advertising platform. In this work, we consider both the optimization of bid shading technique and the design of internal auction which is ex-post incentive compatible (IC) for the management of a DSP. Firstly, we prove that the joint design of bid shading and ex-post IC auction can be reduced to choosing one monotone bid function for each advertiser without loss of optimality. Then we propose a parameterized neural network to implement the monotone bid functions. With well-designed surrogate loss, the objective can be optimized in an end-to-end manner. Finally, our experimental results demonstrate the effectiveness and superiority of our algorithm.
Learning Coalition Structures with Games
Authors: Xu Yixuan Even , Ling Chun Kai , Fang Fei
Published: March 2024

Coalitions naturally exist in many real-world systems involving multiple decision makers such as ridesharing, security, and online ad auctions, but the coalition structure among the agents is often unknown. We propose and study an important yet previously overseen problem -- Coalition Structure Learning (CSL), where we aim to carefully design a series of games for the agents and infer the underlying coalition structure by observing their interactions in those games. We establish a lower bound on the sample complexity -- defined as the number of games needed to learn the structure -- of any algorithms for CSL and propose the Iterative Grouping (IG) algorithm for designing normal-form games to achieve the lower bound. We show that IG can be extended to other succinct games such as congestion games and graphical games. Moreover, we solve CSL in a more restrictive and practical setting: auctions. We show a variant of IG to solve CSL in the auction setting even if we cannot design the bidder valuations. Finally, we conduct experiments to evaluate IG in the auction setting and the results align with our theoretical analysis.
Explaining Reinforcement Learning Agents through Counterfactual Action Outcomes
Authors: Amitai Yotam , Septon Yael , Amir Ofra
Published: March 2024

Explainable reinforcement learning (XRL) methods aim to help elucidate agent policies and decision-making processes. The majority of XRL approaches focus on local explanations, seeking to shed light on the reasons an agent acts the way it does at a specific world state. While such explanations are both useful and necessary, they typically do not portray the outcomes of the agent's selected choice of action.
In this work, we propose ``COViz'', a new local explanation method that visually compares the outcome of an agent's chosen action to a counterfactual one. In contrast to most local explanations that provide state-limited observations of the agent's motivation, our method depicts alternative trajectories the agent could have taken from the given state and their outcomes.
We evaluated the usefulness of COViz in supporting people's understanding of agents' preferences and compare it with reward decomposition, a local explanation method that describes an agent's expected utility for different actions by decomposing it into meaningful reward types. Furthermore, we examine the complementary benefits of integrating both methods. Our results show that such integration significantly improved participants' performance.
Can You Rely on Synthetic Labellers in Preference-Based Reinforcement Learning? It’s Complicated
Authors: Metcalf Katherine , Sarabia Miguel , Fedzechkina Masha , Theobald Barry-John
Published: March 2024

Preference-based Reinforcement Learning (PbRL) enables non-experts to train Reinforcement Learning models using preference feedback. However, the effort required to collect preference labels from real humans means that PbRL research primarily relies on synthetic labellers. We validate the most common synthetic labelling strategy by comparing against labels collected from a crowd of humans on three Deep Mind Control (DMC) suite tasks: stand, walk, and run. We find that: (1) the synthetic labels are a good proxy for real humans under some circumstances, (2) strong preference label agreement between human and synthetic labels is not necessary for similar policy performance, (3) policy performance is higher at the start of training from human feedback and is higher at the end of training from synthetic feedback, and (4) training on only examples with high levels of inter-annotator agreement does not meaningfully improve policy performance. Our results justify the use of synthetic labellers to develop and ablate PbRL methods, and provide insight into how human labelling changes over the course of policy training.
Sample-Constrained Black Box Optimization for Audio Personalization
Authors: Rajagopalan Rajalaxmi , Wei Yu-Lin , Roy Choudhury Romit
Published: March 2024

We consider the problem of personalizing audio to maximize user experience. Briefly, we aim to find a filter h*, which applied to any music or speech, will maximize the user’s satisfaction. This is a black-box optimization problem since the user’s satisfaction function is unknown. Substantive work has been done on this topic where the key idea is to play audio samples to the user, each shaped by a different filter hi, and query the user for their satisfaction scores f(hi). A family of “surrogate” functions is then designed to fit these scores and the optimization method gradually refines these functions to arrive at the filter ˆh* that maximizes satisfaction.

In certain applications, we observe that a second type of querying is possible where users can tell us the individual elements h*[j] of the optimal filter h*. Consider an analogy from cooking where the goal is to cook a recipe that maximizes user satisfaction. A user can be asked to score various cooked recipes (e.g., tofu fried rice) or to score individual ingredients (say, salt, sugar, rice, chicken, etc.). Given a budget of B queries, where a query can be of either type, our goal is to find the recipe that will maximize this user’s satisfaction.

Our proposal builds on Sparse Gaussian Process Regression (GPR) and shows how a hybrid approach can outperform any one type of querying. Our results are validated through simulations and real world experiments, where volunteers gave feedback on music/speech audio and were able to achieve high satisfaction levels. We believe this idea of hybrid querying opens new problems in black-box optimization and solutions can benefit other applications beyond audio personalization.
On Computing Makespan-Optimal Solutions for Generalized Sliding-Tile Puzzles
Authors: Gozon Marcus , Yu Jingjin
Published: March 2024

In the 15-puzzle game, 15 labeled square tiles are reconfigured on a 4 × 4 board through an escort, wherein each (time) step, a single tile neighboring it may slide into it, leaving the space previously occupied by the tile as the new escort. We study a generalized sliding-tile puzzle (GSTP) in which (1) there are 1+ escorts and (2) multiple tiles can move synchronously in a single time step. Compared with popular discrete multi-agent/robot motion models, GSTP provides a more accurate model for a broad array of high-utility applications, including warehouse automation and autonomous garage parking, but is less studied due to the more involved tile interactions. In this work, we analyze optimal GSTP solution structures, establishing that computing makespan optimal solutions for GSTP is NP-complete and developing polynomial time algorithms yielding makespans approximating the minimum with expected/high probability constant factors, assuming randomized start and goal configurations.
Hierarchical Planning and Learning for Robots in Stochastic Settings Using Zero-Shot Option Invention
Authors: Shah Naman , Srivastava Siddharth
Published: March 2024

This paper addresses the problem of inventing and using hierarchical representations for stochastic robot-planning problems. Rather than using hand-coded state or action representations as input, it presents new methods for learning how to create a high-level action representation for long-horizon, sparse reward robot planning problems in stochastic settings with unknown dynamics. After training, this system yields a robot-specific but environment independent planning system. Given new problem instances in unseen stochastic environments, it first creates zero-shot options (without any experience on the new environment) with dense pseudo-rewards and then uses them to solve the input problem in a hierarchical planning and refinement process. Theoretical results identify sufficient conditions for completeness of the presented approach. Extensive empirical analysis shows that even in settings that go beyond these sufficient conditions, this approach convincingly outperforms baselines by 2x in terms of solution time with orders of magnitude improvement in solution quality.
Computing the Why-Provenance for Datalog Queries via SAT Solvers
Authors: Calautti Marco , Livshits Ester , Pieris Andreas , Schneider Markus
Published: March 2024

Explaining an answer to a Datalog query is an essential task towards Explainable AI, especially nowadays where Datalog plays a critical role in the development of ontology-based applications. A well-established approach for explaining a query answer is the so-called why-provenance, which essentially collects all the subsets of the input database that can be used to obtain that answer via some derivation process, typically represented as a proof tree. It is well known, however, that computing the why-provenance for Datalog queries is computationally expensive, and thus, very few attempts can be found in the literature. The goal of this work is to demonstrate how off-the-shelf SAT solvers can be exploited towards an efficient computation of the why-provenance for Datalog queries. Interestingly, our SAT-based approach allows us to build the why-provenance in an incremental fashion, that is, one explanation at a time, which is much more useful in a practical context than the one-shot computation of the whole set of explanations as done by existing approaches.
Learning Small Decision Trees for Data of Low Rank-Width
Authors: Dabrowski Konrad K. , Eiben Eduard , Ordyniak Sebastian , Paesani Giacomo , Szeider Stefan
Published: March 2024

We consider the NP-hard problem of finding a smallest decision tree
representing a classification instance in terms of a partially defined
Boolean function. Small decision trees are desirable to provide an
interpretable model for the given data. We show that the problem is
fixed-parameter tractable when parameterized by the rank-width of the
incidence graph of the given classification instance. Our algorithm
proceeds by dynamic programming using an NLC decomposition obtained
from a rank-width decomposition. The key to the algorithm is a
succinct representation of partial solutions. This allows us to limit
the space and time requirements for each dynamic programming step in
terms of the parameter.
Learning MDL Logic Programs from Noisy Data
Authors: Hocquette Céline , Niskanen Andreas , Järvisalo Matti , Cropper Andrew
Published: March 2024

Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches in terms of predictive accuracies and scale to moderate amounts of noise.
Efficient Axiomatization of OWL 2 EL Ontologies from Data by Means of Formal Concept Analysis
Authors: Kriegel Francesco
Published: March 2024

We present an FCA-based axiomatization method that produces a complete OWL 2 EL TBox (the terminological part of an OWL 2 EL ontology) from a graph dataset in at most exponential time. We describe technical details that allow for efficient implementation as well as variations that dispense with the computation of extremely large axioms, thereby rendering the approach applicable albeit some completeness is lost. Moreover, we evaluate the prototype on real-world datasets.
Auditable Algorithms for Approximate Model Counting
Authors: Meel Kuldeep S. , Chakraborty Supratik , Akshay S.
Published: March 2024

The problem of model counting, i.e., counting satisfying assignments of a Boolean formula, is a fundamental problem in computer science, with diverse applications. Given #P-hardness of the problem, many algorithms have been developed over the years to provide an approximate model count. Recently, building on the practical success of SAT-solvers used as NP oracles, the focus has shifted from theory to practical implementations of such algorithms. This has brought to focus new challenges. In this paper, we consider one such challenge – that of auditable deterministic approximate model counters wherein a counter should also generate a certificate, which allows a user (often with limited computational power) to independently audit whether the count returned by an invocation of the algorithm is indeed within the promised bounds.

We start by examining a celebrated approximate model counting algorithm due to Stockmeyer that uses polynomially many calls to a \Sigma^2_P oracle, and show that it can be audited via a \Pi^2_P formula on (n^2 log^2 n) variables, where n is the number of variables in the original formula. Since n is often large (10’s to 100’s of thousands) for typical instances, we ask if the count of variables in the certificate formula can be reduced – a critical question towards potential implementation. We show that this improvement in certification can be achieved with a tradeoff in the counting algorithm’s complexity. Specifically, we develop new deterministic approximate model counting algorithms that invoke a \Sigma^3_P oracle, but can be certified using a \Pi^2_P formula on fewer variables: our final algorithm uses just (n log n) variables.

Our study demonstrates that one can simplify certificate checking significantly if we allow the counting algorithm to access a slightly more powerful oracle. We believe this shows for the first time how the audit complexity can be traded for the complexity of approximate counting.
New forms of social integration fostering the authoritarian turn in Hungary
Authors: Gerő Márton , Kovách Imre , Kristóf Luca
Published: March 2024

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1. PERFF-seq: Cell and Nuclei Preparation v1
Authors: Abay Tsion , Stickels Robert , Takizawa Meril , Chaligne Ronan , Lareau Caleb
Published: March 2024

This protocol can be used for preparation of Cells and Fresh-Frozen and Formalin-Fixed Paraffin-Embedded (FFPE) Tissue Derived Nuclei. This protocol should be followed by "PERFF-seq: HCR-FlowFISH and Polymer Disassembly."
ssDNA2.0: Adapter/splinter mix v1
Authors: Meyer Matthias , Schmidt Anna , Nagel Sarah
Published: March 2024

Protocol for the preparation of decontaminated (i.e., nuclease-treated) Adapter/splinter mix for the first adapter ligation in automated single-stranded DNA library preparation using the ssDNA2.0 method (Gansauge et al. 2020). References Gansauge, M.-T., Aximu-Petri, A., Nagel, S., & Meyer, M. (2020). Manual and automated preparation of single-stranded DNA libraries for the sequencing of DNA from ancient biological remains and other sources of highly degraded DNA. Nature Protocols, 15, 2279-2300.
Microplastics in marine environments: protocol for isolating natural biofilms from seawater-incubated particles v1
Authors: Massot Meril , gadoin elsa , Rajkovic Andreja , not provided stephanie.bedhomme
Published: March 2024

Microplastics, upon which microbial communities arise rapidly, have emerged as a significant source of pollution in the ocean1. Studying these communities is crucial to understand their potential impacts on marine ecosystems, as well as their role as vector for potential pathogens2, 3 , and reservoir for antimicrobial resistance genes4, 5. The proposed workflow delineates an approach for incubating microplastics particles in seawater, ensuring reproducibility while controlling the origin, incubation duration, and weathering conditions of microplastics. The workflow details the preparation of the incubation device, sample collection, and subsequent laboratory processes to obtain the natural marine biofilms associated with microplastics. Our processing method helps minimize contamination by naturally occurring microparticles in marine environments and allows for an accurate estimate of microplastics concentration by direct counting. The aim of this publication is to provide a standardized protocol to facilitate comparisons between natural communities associated with microplastic pollution. References 1. Bowley, J., Baker-Austin, C., Porter, A., Hartnell, R. & Lewis, C. Oceanic Hitchhikers – Assessing Pathogen Risks from Marine Microplastic. Trends Microbiol. 29, 107–116 (2021). 2. Kirstein IV., Kirmizi S., Wichels A., Garin-Fernandez A., Erler R., Löder M., Gerdts G., – Dangerous hitchhikers? Evidence forpotentially pathogenic Vibrio spp. on microplastic particles. Mar Environ Res .120, 1–8 (2016). 3. Pedrotti ML., De Figueiredo Lacerda AL., Petit S., Ghiglione JF., Gorsky G., Vibrio spp and other potential pathogenic bacteria associated to microfibers in the North-Western Mediterranean Sea. PLOS ONE 17:e0275284. (2022). 4. Wu X., Pan J., Li M., Li Y., Bartlam M., Wang Y., Selective enrichment of bacterial pathogens by microplastic biofilm. Water Res 165, 114979. (2019). 5. Liang H., de Haan WP., Cerdà-Domènech M., Méndez J., Lucena F., García-Aljaro C., Sanchez-Vidal A., Ballesté E., Detection of faecal bacteria and antibiotic resistance genes in biofilms attached to plastics from human-impacted coastal areas. Environ Pollut. 319:120983. (2023).
Protocol Collection: Perfusing, Sectioning, IHC, Mounting and Coverslipping Mouse Brain Specimens v1
Authors: Ouellette Naveen , not provided daphne.toglia , Myers Holly
Published: March 2024

This protocol collection details the steps for a whole mouse brain specimen to be perfused, sliced, stained with antibodies, DAPI, or both, and made into slides. The intracardiac perfusion fixation includes anesthesia, exsanguination, fixation, brain removal, and post-fixation storage. A sliding microtome is used to slice the whole brain into sections, and the sections are stained, mounted on glass slides, and coverslipped. The result is that anatomical structures in brain regions of interest may be labeled in a way that can be viewed by laser channels on a microscope and imaged for data collection.
Mounting and Coverslipping Mouse Brain Sections v1
Authors: Ouellette Naveen , not provided daphne.toglia , Myers Holly
Published: March 2024

This protocol details mounting thin sliced mouse brain tissue sections onto charged slides in a uniform orientation that does not create wrinkles or artifacts that might interfere with imaging, and then how to apply a coverslip. After mounting and coverslipping, slides will be ready for imaging.
STUDY OF FEMINISM ARCHITECTURE CONCEPT IN MUSEUM BUILDING "THE SOLOMON R GUGGENHEIM MUSEUM IN AMERICA US"
Authors: Haqsaleh Afied Dien , Ashadi Ashadi
Published: March 2024

Abstract: A study of the concept of feminist architecture in the Solomon R Guggenheim Museum building. In the past, an understanding held by society regarding sex differences between men and women affected control, where women were not given freedom, power and rights completely different from men who could do anything and be anything. In the world of architecture, feminism is present as part of post-modern architecture because of the saturation of modern buildings that occur. studies the concept of Feminist Architecture at the Solomon R Guggenheim Museum in New York, America, which is considered to have approached the principles of feminist architecture in its buildings and the method to be used is descriptive qualitative. The author needs to do research on the concept of Feminism Architecture in order to know its true characteristics. From the research results, it is concluded that the case study of the building that has been studied is the Feminist Architecture approach and applies it and among them is having the form of a building that resembles the shape of items used by women, the shape of the building has arches reflecting the woman's body, there is a division of space as a divider between women and men, as well as the use of colors in buildings or interiors that match the preferences of women. Keywords: Architecture, Architecture Feminism, Feminism, Solomon R Guggenheim Museum, Women.
DESIGN OF MIXED-USE AREA IN LEUWI PANJANG BUS TERMINAL BASED ON TRANSIT-ORIENTED DEVELOPMENT (TOD), BANDUNG CITY, INDONESIA
Authors: Rustiandi Firdaus Dwi Putra , Permana Asep Yudi , Al Haq Risya Maryam
Published: March 2024

AbstractLeuwi Panjang Terminal of Bandung City is a terminal with an A-type category in the city of Bandung. This terminal is one of the proposed Transit Oriented Development in Bandung Urban Mobile Project 2031 programs. Listed in the BUMP 2031 program, the Leuwi Panjang Terminal area will be equipped as a public transportation transit, monorail. Based on the existing criteria, the Leuwi Panjang terminal doesn’t fulfill the criteria as a Transit Oriented Development area, including inadequate facilities, the circulation systems that are not mutually integrated and the unavailability of signage makes visitors confused about their activity paths, and the stiffness of pedestrians and cyclists to move around in this area. This study discusses the architectural design recommendations of the TOD area that fill the TOD standards of the Leuwi Panjang Terminal. The method used is by conducting area studies, activity studies, and circulation studies. The study is analyzed and processed to produce a synthesis and application to the design to fulfill the TOD criteria and government regulations. Keywords: Transit-Oriented Development, Integration, Terminal, Of Bandung City.
FACTORS AFFECTING THE SPEED OF CERTIFICATE ACCEPTANCE OF FUNCTIONALITY
Authors: Kusumahastiti Andhyni , Siswosukarto Suprapto , Priyosulistyo Henricus
Published: March 2024

Topic of this research was carried out in relation to the Regulation of the Minister of Public Works 25/PRT/M/2007 concerning the Certificate of Feasibility of Function (SLF). The factors affecting the speed  of  getting the certificate will be investigated. Each region is required to make further regional regulations that refer to the Regulation of the Minister of Public Works 25/PRT/M/2007. Such a regional regulation can affect the speed and quality of results. The case studies selected in this study were SLF at the Microbiology Building, FKKMK UGM and Hotel de Braga, Bandung. This research based on the results of interviews and data obtained in the microbology building UGM Yogyakarta and Hotel de Braga Bandung. The factors that influence the SLF application process are socialization related to SLF, human resources in the application and issuance process, and understanding of the main tasks and functions of each related human resource. The difference requirements and procedures of the regional regulations does not affect each process but does affect the duration of the SLF issuance process. Substantially, the regional regulations in Sleman Regency has no differences compared to the regional regulations in the city of Bandung regarding SLF but sequence.
AESTHETIC VALUES OF RUINS: A CASE STUDY OF THE MALABAR RADIO STATION AREA
Authors: Purwatiana Rahmadean Alifani , Handayani Sri
Published: March 2024

Relics of the past that have historical value must be preserved, but due to war and natural factors, these relics only leave ruins, so the conservation measures are different from buildings. The aesthetic value is one of the reasons for maintaining the ruins as they are, where this value is related to the physical and non-physical aspects of the ruins. One of the locations of the historical ruins is the ruins in the Malabar Radio Transmitting Station Area located on Mount Puntang, Banjaran District, Bandung Regency, West Java, which used to be a Radio Station complex that bridged long-distance communications between Indonesia and the Netherlands, has important historical value and impact the time. A change in function to become a tourist spot at this location can intervene in the ruins by adding or developing tourist facilities that can reduce or damage their historical and aesthetic values. This study intends to examine the aesthetic value of the ruins of the Malabar Radio Station to find a value that can be maintained as it is. This research uses qualitative research methods by searching historical data through primary and secondary sources. The results of the study found that the aesthetic value of the ruins consisted of 1) physical aesthetic values related to the shape of the ruins that can be observed and felt with the five senses, identified forms and materials (traces of weathering, the remaining walls, and floors) that blend into the mountainous landscape to give it an interesting and distinctive landscape character and visual appeal; and 2) non-physical aesthetic value related to the function of marking the passage of time, evidence of representation of the involvement of indigenous workers, defense of independence in the Banjaran region, and becoming a tourist attraction. Both of these aesthetic values are things that must be protected and maintained for the preservation of the ruins.
ANALYSIS OF NATURAL DAYLIGHTING AT GPIB BETHEL BANDUNG USING DIALUX EVO 10.1 SIMULATION
Authors: Puspita Wieke Oksi , Abidin Zidanendra Fidel , Khairunisya Nabilla Febriani , Paramita Beta
Published: March 2024

Natural daylighting comes from sunlight that is reflected from outside into the building and plays an important role for the building. One of the buildings that must have good natural lighting is the Church. Natural lighting is needed to support religious activities in the Church from a functional and architectural point of view by giving a certain meaning or aesthetic value. Based on the applicable standards, the church as a place of worship has a minimum requirement of 200 lux lighting (SNI) 03-6197-2000. This study aims to determine the suitability level of natural lighting in the church with applicable standards. The method used in this study is a quantitative method with data acquisition through direct observation and simulated using DIALux Evo 10.1 software. Simulation results data in the form of lux calculations and lighting distribution. Based on the simulation results, it was found that natural lighting at GPIB Bethel Bandung did not meet the applicable standards for two effective times. Therefore, a response is needed to overcome this by increasing the number and width of window openings.