bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Essence and Prospect: An Investigation of Alignment Approaches for Big Models
Authors: Wang Xinpeng , Duan Shitong , Yi Xiaoyuan , Yao Jing , Zhou Shanlin , Wei Zhihua , Zhang Peng , Xu Dongkuan , Sun Maosong , Xie Xing
Published: July 2024

Big models have achieved revolutionary breakthroughs in the field of AI, but they also pose potential ethical and societal risks to humans. Addressing such problems, alignment technologies were introduced to make these models conform to human preferences and values. Despite the considerable advancements in the past year, various challenges lie in establishing the optimal alignment strategy, such as data cost and scalable oversight, and how to align remains an open question. In this survey paper, we comprehensively investigate value alignment approaches. We first unpack the historical context of alignment tracing back to the 1920s (where it comes from), then delve into the mathematical essence of alignment (what it is), shedding light on the inherent challenges. Following this foundation, we provide a detailed examination of existing alignment methods, which fall into three categories: RL-based Alignment, SFT-based Alignment, and Inference-Time Alignment, and demonstrate their intrinsic connections, strengths, and limitations, helping readers better understand this research area. In addition, two emerging topics, alignment goal and multimodal alignment, are also discussed as novel frontiers in the field. Looking forward, we discuss potential alignment paradigms and how they could handle remaining challenges, prospecting where future alignment will go.
Large Language Models for Time Series: A Survey
Authors: Zhang Xiyuan , Chowdhury Ranak Roy , Gupta Rajesh K. , Shang Jingbo
Published: July 2024

Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision. Going beyond text, image and graphics, LLMs present a significant potential for analysis of time series data, benefiting domains such as climate, IoT, healthcare, traffic, audio and finance. This survey paper provides an in-depth exploration and a detailed taxonomy of the various methodologies employed to harness the power of LLMs for time series analysis. We address the inherent challenge of bridging the gap between LLMs' original text data training and the numerical nature of time series data, and explore strategies for transferring and distilling knowledge from LLMs to numerical time series analysis. We detail various methodologies, including (1) direct prompting of LLMs, (2) time series quantization, (3) aligning techniques, (4) utilization of the vision modality as a bridging mechanism, and (5) the combination of LLMs with tools. Additionally, this survey offers a comprehensive overview of the existing multimodal time series and text datasets in diverse domains, and discusses the challenges and future opportunities of this emerging field.
A Comprehensive Survey and Taxonomy on Point Cloud Registration Based on Deep Learning
Authors: Zhang Yu-Xin , Gui Jie , Cong Xiaofeng , Gong Xin , Tao Wenbing
Published: July 2024

Point cloud registration (PCR) involves determining a rigid transformation that aligns one point cloud to another. Despite the plethora of outstanding deep learning (DL)-based registration methods proposed, comprehensive and systematic studies on DL-based PCR techniques are still lacking. In this paper, we present a comprehensive survey and taxonomy of recently proposed PCR methods. Firstly, we conduct a taxonomy of commonly utilized datasets and evaluation metrics. Secondly, we classify the existing research into two main categories: supervised and unsupervised registration, providing insights into the core concepts of various influential PCR models. Finally, we highlight open challenges and potential directions for future research. A curated collection of valuable resources is made available at https://github.com/yxzhang15/PCR.
Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic (Extended Abstract)
Authors: Bonassi Luigi , De Giacomo Giuseppe , Favorito Marco , Fuggitti Francesco , Gerevini Alfonso Emilio , Scala Enrico
Published: July 2024

We study classical planning for temporally extended goals expressed in Pure-Past Linear Temporal Logic (PPLTL).

PPLTL is as expressive as Linear-time Temporal Logic on finite traces (LTLf), but as shown in this paper, it is computationally much better behaved for planning.

Specifically, we show that planning for PPLTL goals can be encoded into classical planning with minimal overhead, introducing only a number of new fluents that is at most linear in the PPLTL goal and no spurious additional actions.

Based on these results, we implemented a system called Plan4Past, which can be used along with state-of-the-art classical planners, such as LAMA.

An empirical analysis demonstrates the practical effectiveness of Plan4Past, showing that a classical planner generally performs better with our compilation than with other existing compilations for LTLf goals over the considered benchmarks.
Streamlining Input/Output Logics with Sequent Calculi (Extended Abstract)
Authors: Ciabattoni Agata , Rozplokhas Dmitry
Published: July 2024

Input/Output (I/O) logic is a general framework for reasoning about conditional norms and/or causal relations. We streamline Bochman’s causal I/O logics and their original version via proof-search-oriented sequent calculi. As a byproduct, we obtain new, simple semantics for all these logics, complexity bounds, embeddings into normal modal logics, and efficient deduction methods. Our work encompasses many scattered results and provides uniform solutions to various unresolved problems.
Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs (Extended Abstract)
Authors: Feyzbakhsh Rankooh Masood , Janhunen Tomi
Published: July 2024

We establish a novel relation between delete-free planning, an important task for the AI Planning community also known as relaxed planning, and logic programming. We show that given a planning problem, all subsets of actions that could be ordered to produce relaxed plans for the problem can be bijectively captured with stable models of a logic program describing the corresponding relaxed planning problem. We also consider the supported model semantics of logic programs, and introduce one causal and one diagnostic encoding of the relaxed planning problem as logic programs, both capturing relaxed plans with their supported models. Our experimental results show that these new encodings can provide major performance gain when computing optimal relaxed plans.
Defending Against Backdoor Attacks by Layer-wise Feature Analysis (Extended Abstract)
Authors: Jebreel Najeeb Moharram , Domingo-Ferrer Josep , Li Yiming
Published: July 2024

Training deep neural networks (DNNs) usually requires massive training data and computational resources. Users who cannot afford this may prefer to outsource training to a third party or resort to publicly available pre-trained models. Unfortunately, doing so facilitates a new training-time attack (i.e., backdoor attack) against DNNs. This attack aims to induce misclassification of input samples containing adversary-specified trigger patterns. In this paper, we first conduct a layer-wise feature analysis of poisoned and benign samples from the target class. We find out that the feature difference between benign and poisoned samples tends to be maximum at a critical layer, which is not always the one typically used in existing defenses, namely the layer before fully-connected layers. We also demonstrate how to locate this critical layer based on the behaviors of benign samples. We then propose a simple yet effective method to filter poisoned samples by analyzing the feature differences between suspicious and benign samples at the critical layer. Extensive experiments on two benchmark datasets are reported which confirm the effectiveness of our defense.
A Single Vector Is Not Enough: Taxonomy Expansion via Box Embeddings (Extended Abstract)
Authors: Jiang Song , Yao Qiyue , Wang Qifan , Sun Yizhou
Published: July 2024

Taxonomies support various practical web applications such as product navigation in online shopping and user profile tagging on social platforms. Most existing methods for expanding taxonomies encode entities into vector embeddings (i.e., single points). However, we argue that vectors are insufficient to model the ``is-a'' hierarchy in taxonomy (asymmetrical relation), because two points can only represent pairwise similarity (symmetrical relation). To this end, we propose to project taxonomy entities into boxes (i.e., hyperrectangles). Two boxes can be "contained", "disjoint" and "intersecting", thus naturally representing an asymmetrical taxonomic hierarchy. Upon box embeddings, we propose a novel model BoxTaxo for taxonomy expansion. The core of BoxTaxo is to learn boxes for entities to capture their child-parent hierarchies. Extensive experiments on two benchmarks demonstrate the effectiveness of BoxTaxo compared to vector based models.
GS2P: A Generative Pre-trained Learning to Rank Model with Over-parameterization for Web-Scale Search (Extended Abstract)
Authors: Li Yuchen , Xiong Haoyi , Kong Linghe , Bian Jiang , Wang Shuaiqiang , Chen Guihai , Yin Dawei
Published: July 2024

While Learning to Rank (LTR) is widely employed in web searches to prioritize pertinent webpages from the retrieved contents based on input queries, traditional LTR models stumble over two principal stumbling blocks leading to subpar performance: 1) the lack of well-annotated query-webpage pairs with ranking scores to cover search queries of various popularity, debilitating their coverage of search queries across the popularity spectrum, and 2) ill-trained models that are incapable of inducing generalized representations for LTR, culminating in overfitting. To tackle above challenges, we proposed a Generative Semi-supervised Pre-trained (GS2P) LTR model. Specifically, GS2P first generates pseudo-labels for the unlabeled samples using tree-based LTR models after a series of co-training procedures, then learns the representations of query-webpage pairs with self-attentive transformers via both discriminative and generative losses. Finally, GS2P boosts the performance of LTR through incorporating Random Fourier Features to over-parameterize the models into "interpolating regime", so as to enjoy the further descent of generalization errors with learned representations. We conduct extensive offline experiments on a publicly available dataset and a real-world dataset collected from a large-scale search engine. The results show that GS2P can achieve the best performance on both datasets, compared to baselines.

We also deploy GS2P at a large-scale web search engine with realistic traffic, where we can still observe significant improvement in real-world applications.
Proof Logging for Smart Extensional Constraints (Extended Abstract)
Authors: McIlree Matthew J. , McCreesh Ciaran
Published: July 2024

Proof logging provides an auditable way of guaranteeing that a solver has produced a correct answer using sound reasoning. This is standard practice for Boolean satisfiability solving, but for constraint programming, a challenge is that every propagator must be able to justify all inferences it performs. Here we demonstrate how to support proof logging for a wide range of previously uncertified global constraints. We do this by showing how to justify every inference that could be performed by the propagation algorithms for two families of generalised extensional constraint: "Smart Table" and "Regular Language Membership".
gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling (Extended Abstract)
Authors: Petrov Aleksandr V. , Macdonald Craig
Published: July 2024

Sequential recommendation models predict the next item in a sequence of user-item interactions, akin to how language models predict the next tokens. These models often adapt language model architectures, treating item IDs as if they were token IDs. However, the number of potential items in recommender systems makes calculating the interaction probability for all items impractical during training; therefore, recommender systems frequently employ negative sampling, where the model learns to differentiate between actual user interactions (positives) and randomly chosen non-interactions (negatives), often using Binary Cross-Entropy (BCE) the loss, framing the problem as a binary classification task.

We demonstrate that using negative sampling with BCE can lead to overconfidence, where the model's predicted probabilities for user interactions are higher than the actual probabilities. Although the actual score magnitude is not important for ranking items (only the order of scores matters), overconfidence leads to training instability when using Binary Cross-Entropy (BCE) loss. We show that overconfidence explains the performance gap between two leading sequential recommendation models, SASRec and BERT4Rec -- the former uses negative sampling, while the latter does not. To counter overconfidence, we introduce Generalised Binary Cross-Entropy (gBCE) loss and the gSASRec model that utilises gBCE. We mathematically prove and empirically validate that gSASRec effectively addresses the issue of overconfidence. Consequently, gSASRec's effectiveness is better than that of SASRec and matches the state of the BERT4Rec while retaining negative sampling. On the Gowalla dataset with more than 1MM items, where training BERT4Rec is infeasible, gSASRec outperforms the original SASRec model by 41% in terms of NDCG@10.
Content Matters: A Computational Investigation into the Effectiveness of Retrieval Practice and Worked Examples (Extended Abstract)
Authors: Rachatasumrit Napol , Carvalho Paulo F. , Li Sophie , Koedinger Kenneth R.
Published: July 2024

In this paper, we argue that computational models of learning can contribute precise theory to explain surprising student learning phenomena. In some past studies, practice produces better learning than studying examples, whereas other studies show the opposite result. We explain this contradiction by suggesting that retrieval practice and example study involve different learning cognitive processes, memorization and induction, and each process is optimal for different types of knowledge. We implement and test this theoretical explanation by extending an AI model of human cognition to include both memory and induction processes and comparing the behavior of the simulated learners to those of human participants. We show that the behavior of simulated learners with forgetting matches that of human participants better than simulated learners without forgetting. Simulated learners with forgetting learn best using retrieval practice in situations that emphasize memorization (such as learning facts), whereas studying examples improves learning when multiple pieces of information are available, so induction and generalization are necessary (such as learning skills).
Voting by Axioms (Extended Abstract)
Authors: Schmidtlein Marie Christin , Endriss Ulle
Published: July 2024

We develop an approach for collective decision making from first principles. In this approach, rather than using a---necessarily imperfect---voting rule to map any given scenario where individual agents report their preferences into a collective decision, we identify for every concrete such scenario the most appealing set of normative principles (known as axioms in social choice theory) that would entail a unique decision and then implement that decision. We analyse some of the fundamental properties of this new approach, from both an algorithmic and a normative point of view.
On the Complexity of Finding Set Repairs for Data-Graphs (Abstract Reprint)
Authors: Abriola Sergio , Martinez Maria Vanina , Pardal Nina , Cifuentes Santiago , Baque Edwin Pin
Published: July 2024

In the deeply interconnected world we live in, pieces of information link domains all

around us. As graph databases embrace effectively relationships among data and allow

processing and querying these connections efficiently, they are rapidly becoming a popular

platform for storage that supports a wide range of domains and applications. As in the

relational case, it is expected that data preserves a set of integrity constraints that define

the semantic structure of the world it represents. When a database does not satisfy its

integrity constraints, a possible approach is to search for a ‘similar’ database that does

satisfy the constraints, also known as a repair. In this work, we study the problem of

computing subset and superset repairs for graph databases with data values using a notion

of consistency based on having a set of Reg-GXPath expressions as integrity constraints.

We show that for positive fragments of Reg-GXPath these problems admit a polynomialtime algorithm, while the full expressive power of the language renders them intractable.
Hybrid planning for challenging construction problems: An Answer Set Programming approach (Abstract Reprint)
Authors: Ahmad Faseeh , Patoglu Volkan , Erdem Esra
Published: July 2024

We study construction problems where multiple robots rearrange stacks of prefabricated blocks to build stable structures. These problems are challenging due to ramifications of actions, true concurrency, and requirements of supportedness of blocks by a surface or a robot and stability of the overall structure at all times. We propose a general elaboration tolerant method to solve a wide range of construction problems, based on the knowledge representation and reasoning paradigm of Answer Set Programming. This method not only (i) determines a stable final configuration of the structure, but also (ii) computes the order of manipulation tasks for multiple autonomous robots to build the structure from an initial configuration, (iii) while simultaneously ensuring the requirements of supportedness and stability at all times. We prove the soundness and completeness of our method with respect to these properties. We introduce a set of challenging construction benchmark instances, including construction of (uneven) bridges and overhangs, and discuss the usefulness of our framework over these instances. Furthermore, we perform experiments to investigate the computational performance of our hybrid method, and demonstrate the applicability of our method using a bimanual Baxter robot.
Weighted, Circular and Semi-Algebraic Proofs (Abstract Reprint)
Authors: Bonacina Ilario , Bonet Maria Luisa , Levy Jordi
Published: July 2024

In recent years there has been an increasing interest in studying proof systems stronger than Resolution, with the aim of building more efficient SAT solvers based on them. In defining these proof systems, we try to find a balance between the power of the proof system (the size of the proofs required to refute a formula) and the difficulty of finding the proofs.

In this paper we consider the proof systems circular Resolution, Sherali-Adams, Nullstellensatz and Weighted Resolution and we study their relative power from a theoretical perspective. We prove that circular Resolution, Sherali-Adams and Weighted Resolution are polynomially equivalent proof systems. We also prove that Nullstellensatz is polynomially equivalent to a restricted version of Weighted Resolution. The equivalences carry on also for versions of the systems where the coefficients/weights are expressed in unary.

The practical interest in these systems comes from the fact that they admit efficient algorithms to find proofs in case these have small width/degree.
Detecting Change Intervalswith Isolation Distributional Kernel (Abstract Reprint)
Authors: Cao Yang , Zhu Ye , Ting Kai Ming , Salim Flora D. , Li Hong Xian , Yang Luxing , Li Gang
Published: July 2024

Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have been proposed recently to identify those changes, they still suffer from missing subtle changes, poor scalability, or/and sensitivity to outliers. To meet these challenges, we are the first to generalise the CPD problem as a special case of the Change-Interval Detection (CID) problem. Then we propose a CID method, named iCID, based on a recent Isolation Distributional Kernel (IDK). iCID identifies the change interval if there is a high dissimilarity score between two non-homogeneous temporal adjacent intervals. The data-dependent property and finite feature map of IDK enabled iCID to efficiently identify various types of change-points in data streams with the tolerance of outliers. Moreover, the proposed online and offline versions of iCID have the ability to optimise key parameter settings. The effectiveness and efficiency of iCID have been systematically verified on both synthetic and real-world datasets.
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution
Authors: Huang Binxiao , Li Jason Chun Lok , Ran Jie , Li Boyu , Zhou Jiajun , Yu Dahai , Wong Ngai
Published: July 2024

Conventional super-resolution (SR) schemes make heavy use of convolutional neural networks (CNNs), which involve intensive multiply-accumulate (MAC) operations, and require specialized hardware such as graphics processing units. This contradicts the regime of edge AI that often runs on devices strained by power, computing, and storage resources. Such a challenge has motivated a series of lookup table (LUT)-based SR schemes that employ simple LUT readout and largely elude CNN computation. Nonetheless, the multi-megabyte LUTs in existing methods still prohibit on-chip storage and necessitate off-chip memory transport. This work tackles this storage hurdle and innovates hundred-kilobyte LUT (HKLUT) models amenable to on-chip cache. Utilizing an asymmetric two-branch multistage network coupled with a suite of specialized kernel patterns, HKLUT demonstrates an uncompromising performance and superior hardware efficiency over existing LUT schemes. Our implementation is publicly available at: https://github.com/jasonli0707/hklut.
A differentiable first-order rule learner for inductive logic programming (Abstract Reprint)
Authors: Gao Kun , Inoue Katsumi , Cao Yongzhi , Wang Hanpin
Published: July 2024

Learning first-order logic programs from relational facts yields intuitive insights into the data. Inductive logic programming (ILP) models are effective in learning first-order logic programs from observed relational data. Symbolic ILP models support rule learning in a data-ecient manner. However, symbolic ILP models are not robust to learn from noisy data. Neuro-symbolic ILP models utilize neural networks to learn logic programs in a differentiable manner which improves the robustness of ILP models. However, most neuro-symbolic methods need a strong language bias to learn logic programs, which reduces the usability and flexibility of ILP models and limits the logic program formats. In addition, most neuro-symbolic ILP methods cannot learn logic programs effectively from both small-size datasets and large-size datasets such as knowledge graphs. In the paper, we introduce a novel differentiable ILP model called differentiable first-order rule learner (DFORL), which is scalable to learn rules from both smaller and larger datasets. Besides, DFORL only needs the number of variables in the learned logic programs as input. Hence, DFORL is easy to use and does not need a strong language bias. We demonstrate that DFORL can perform well on several standard ILP datasets, knowledge graphs, and probabilistic relation facts and outperform several well-known differentiable ILP models. Experimental results indicate that DFORL is a precise, robust, scalable, and computationally cheap differentiable ILP model.
Optimality Guarantees for Particle Belief Approximation of POMDPs (Abstract Reprint)
Authors: Lim Michael , Becker Tyler , Kochenderfer Mykel , Tomlin Claire , Sunberg Zachary
Published: July 2024

Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. However, POMDPs are notoriously difficult to solve, especially when the state and observation spaces are continuous or hybrid, which is often the case for physical systems. While recent online sampling-based POMDP algorithms that plan with observation likelihood weighting have shown practical effectiveness, a general theory characterizing the approximation error of the particle filtering techniques that these algorithms use has not previously been proposed. Our main contribution is bounding the error between any POMDP and its corresponding finite sample particle belief MDP (PB-MDP) approximation. This fundamental bridge between PB-MDPs and POMDPs allows us to adapt any sampling-based MDP algorithm to a POMDP by solving the corresponding particle belief MDP, thereby extending the convergence guarantees of the MDP algorithm to the POMDP. Practically, this is implemented by using the particle filter belief transition model as the generative model for the MDP solver. While this requires access to the observation density model from the POMDP, it only increases the transition sampling complexity of the MDP solver by a factor of O(C), where C is the number of particles. Thus, when combined with sparse sampling MDP algorithms, this approach can yield algorithms for POMDPs that have no direct theoretical dependence on the size of the state and observation spaces. In addition to our theoretical contribution, we perform five numerical experiments on benchmark POMDPs to demonstrate that a simple MDP algorithm adapted using PB-MDP approximation, Sparse-PFT, achieves performance competitive with other leading continuous observation POMDP solvers.
Performative Ethics From Within the Ivory Tower: How CSPractitioners Uphold Systems of Oppression (Abstract Reprint)
Authors: McFadden Zari , Alvarez Lauren
Published: July 2024

This paper analyzes where Artificial Intelligence (AI) Ethics research fails and breaks down the dangers of well-intentioned, but ultimately performative ethics research. A large majority of AI ethics research is critiqued for lacking a comprehensive analysis of how AI is interconnected with sociological systems of oppression and power. Our work contributes to the handful of research that presents intersectional, Western systems of oppression and power as a framework for examining AI ethics work and the complexities of building less harmful technology; directly connecting technology to named systems such as capitalism and classism, colonialism, racism and white supremacy, patriarchy, and ableism. We then explore current AI ethics rhetoric's effect on the AI ethics domain and AI regulation. In conclusion, we provide an applied example to contextualize intersectional systems of oppression and AI interventions in the U.S. justice system and present actionable steps for AI practitioners to participate in a less performative, critical analysis of AI.
Long Short-Term Dynamic Prototype Alignment Learning for Video Anomaly Detection
Authors: Huang Chao , Wen Jie , Liu Chengliang , Liu Yabo
Published: July 2024

Video anomaly detection (VAD) is the core problem of intelligent video surveillance. Previous methods commonly adopt the unsupervised paradigm of frame reconstruction or prediction. However, the lack of mining of temporal dependent relationships and diversified event patterns within videos limit the performance of existing methods. To tackle these problems, we propose a novel prototype-guided and dynamic-aware long-distance frame prediction paradigm for VAD. Specifically, we develop a prototype-guided dynamics matching network (PDM-Net) to enhance the discriminant and robustness of anomaly detector. To explore the temporal contexts, we equip PDM-Net with a long short-term dynamic prototype alignment learning mechanism, which stores long-term dynamic prototypes into memory bank and learns how to recall long-term dynamic prototypes with short-term dynamics. As a result, the short input sequences can recall long-term dynamic prototypes stored in the memory bank to achieve the task of long-distance frame prediction. Besides, a feature discrimination module is adopted to extract the representative dynamic features of various normal events meanwhile preserving the diversity of normal patterns. Experimental results on four public datasets demonstrate the superiority of our method.
NeuroSymbolic NLP for Mathematical Reasoning and Software Engineering
Authors: Jana Prithwish
Published: July 2024

In recent years, there has been a significant interest in Large Language Models (LLMs) owing to their notable performance in natural language processing (NLP) tasks. However, while their results show promise in mathematical reasoning and software engineering tasks, LLMs have not yet achieved a satisfactory performance level in these domains. In response, current approaches have prioritized scaling up the size of LLMs, necessitating substantial computational resources and data. Our objective, however, is to pursue a different path by developing neurosymbolic language models. We propose to integrate logical and symbolic feedback during the training process, enabling significantly smaller language models to achieve far better reasoning capabilities than the LLMs currently in use.
Parameter Efficient Instruction Tuning of LLMs for Financial Applications
Authors: Khatuya Subhendu
Published: July 2024

XBRL tagging in financial texts involves categorizing entities into numerous labels, presenting challenges for state-of-the-art models. Financial reports like 10-Q and 10-K, which must be tagged with XBRL according to a taxonomy with thousands of labels. The FNXL dataset exemplifies this with 2,794 labels. Manual tagging is neither scalable nor cost-effective, necessitating automatic annotation methods. Additionally, summarizing long Earnings Call Transcripts (ECTs) is crucial for financial decision-making. The ECTSum dataset highlights challenges in automatic summarization, including a high compression ratio and documents exceeding typical LLM token limits. This study proposes novel methods for both XBRL tagging and ECT summarization.
Two-Sided Facility Location Games
Authors: Krogmann Simon
Published: July 2024

Facility location problems have been studied in settings like hospital placement or the competition between stores.

In some cases, a central authority coordinates facility placements to optimize metrics like the coverage of an area or emergency response time.

In many cases, however, facilities are placed by multiple rational agents to maximize their utility, e.g., the number of clients they attract.

In previous research, these games feature simplistic client behavior independent of other clients' strategic choices, e.g., visiting the closest facility.

Our goal is to understand what happens if clients also act selfishly, resulting in a two-stage game consisting of strategic facility and client agents.

In three recent publications, we investigated such two-stage models for clients that optimize their waiting times.

We showed the existence and gave algorithms for (approximate) subgame perfect equilibria, a common extension of Nash equilibria for sequential games.

To learn more about this domain, we intend to investigate further natural client behaviors and eventually create a more general model or hierarchy of two-sided facility location games.

With this, we aim to make predictions in real-world settings, e.g., the placement of renewable energy infrastructure.
Fair and Efficient Chore Allocation: Existence and Computation
Authors: Murhekar Aniket
Published: July 2024

We investigate the existence and computation of fair and efficient allocations of indivisible chores to agents with additive preferences. We consider the popular envy-based fairness notions of envy-freeness up to one chore (EF1) and the efficiency notion of Pareto-optimality (PO).

The existence of an allocation of chores that is simultaneously EF1 and PO is regarded a major open problem in discrete fair division. We show that an EF1 and PO allocation can be computed in polynomial time for certain structured instances. These results comprise the first non-trivial positive results for the problem and reveal insights towards settling the problem in its full generality.
Multivariate Analysis and Structural Restrictions in Computational Social Choice
Authors: Schierreich Šimon
Published: July 2024

In my research, I focus on computationally hard problems in the area of computational social choice. I am interested in the study of input restrictions that guarantee the existence of efficient and scalable algorithms that can be of practical interest.
Optimization Under Epistemic Uncertainty With a Focus on Decision-Focused Learning
Authors: Schutte Noah
Published: July 2024

Due to the complexity of randomness, optimization problems are often modeled to be deterministic to be solvable. Specifically epistemic uncertainty, i.e., uncertainty that is caused due to a lack of knowledge, is not easy to model, let alone easy to subsequently solve. Despite this, taking uncertainty into account is often required for optimization models to produce robust decisions that perform well in practice. We analyze effective existing frameworks, aiming to improve robustness without increasing complexity. Specifically we focus on robustness in decision-focused learning, which is a framework aimed at making context-based predictions for an optimization problem's uncertain parameters that minimize decision error.
Implicit Anomaly Subgraph Detection (IASD) in Multi-Domain Attribute Networks
Authors: Sun Ying
Published: July 2024

Anomaly subgraph detection is a vital task in various real applications. However, with the advancement of AI technology, it faces new challenges: 1) Anomaly features are often deeply hidden within large datasets, and 2) Anomaly detection approaches are required to unveil the mechanisms behind anomaly generation.

Our study focuses on detecting hidden anomaly subgraphs within big data and offering improved explanations for the root cause of anomalies by integrating multi-domain datasets.
N-Agent Ad Hoc Teamwork
Authors: Wang Caroline
Published: July 2024

Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In fully cooperative multi-agent reinforcement learning, the learning algorithm controls all agents in the scenario, while in ad hoc teamwork, the learning algorithm usually assumes control over only a single agent in the scenario. However, many cooperative settings in the real world are much less restrictive. For example, in an autonomous driving scenario, a company might train its cars to cooperate with each other, yet once on the road, these cars must additionally cooperate with cars from other companies. Towards expanding the class of scenarios that cooperative learning methods may optimally address, this research agenda introduces and proposes to study N-agent ad hoc teamwork (NAHT), where a set of autonomous agents must interact and cooperate with dynamically varying numbers and types of teammates.
Culturally-aware Image Captioning
Authors: Yun Youngsik
Published: July 2024

The primary research challenge lies in mitigating and measuring geographical and demographic biases in generative models, which is crucial for ensuring fairness in AI applications. Existing models trained on web-crawled datasets like LAION-400M often perpetuate harmful stereotypes and biases, especially concerning minority groups or less-represented regions. To address this, I proposed a framework called CIC (Culturally-aware Image Caption) to generate culturally-aware image captions. This framework leverages visual question answering (VQA) to extract cultural visual elements from images. It prompts both caption prompts and cultural visual elements to generate culturally-aware captions using large language models (LLMs). Human evaluations confirm the effectiveness of our approach in depicting cultural information accurately.

Two key future directions are outlined. First, current image caption evaluation methods are inadequate for assessing culturally-aware captions, necessitating the development of new evaluation metrics leveraging cultural datasets and representations. Second, ethical considerations, particularly concerning stereotypes embedded in existing models, demand consensus and standards development through diverse cultural perspectives. Addressing these challenges is vital for the responsible deployment of AI technologies in diverse real-world contexts.
Trustworthy Machine Learning under Imperfect Data
Authors: Han Bo
Published: July 2024

Trustworthy machine learning (TML) under imperfect data has recently brought much attention in the data-centric fields of machine learning (ML) and artificial intelligence (AI). Specifically, there are mainly three types of imperfect data along with their challenges for ML, including i) label-level imperfection: noisy labels; ii) feature-level imperfection: adversarial examples; iii) distribution-level imperfection: out-of-distribution data. Therefore, in this paper, we systematically share our insights and solutions of TML to handle three types of imperfect data. More importantly, we discuss some new challenges in TML, which also open more opportunities for future studies, such as trustworthy foundation models, trustworthy federated learning, and trustworthy causal learning.
Algorithmic Fairness in Distribution of Resources and Tasks
Authors: Hosseini Hadi
Published: July 2024

The widespread adoption of Artificial Intelligence (AI) systems has profoundly reshaped decision-making in social, political, and commercial contexts. This paper explores the critical issue of fairness in AI-driven decision-making, particularly in allocating resources and tasks. By examining recent advancements and key questions in computational social choice, I highlight challenges and prospects in designing fair systems in collective decision-making that are scalable, adaptable to intricate environments, and are aligned with complex and diverse human preferences.
The Rise of Federated Intelligence: From Federated Foundation Models Toward Collective Intelligence
Authors: Long Guodong
Published: July 2024

The success of foundation models advances the development of various intelligent and personalized agents to handle intricate tasks in their daily lives, however finite resources and privacy concerns from end users limit the potential of customizing the large intelligent agents for personal use. This paper explores the preliminary design of federated intelligence that paves the way toward personalized intelligent agents in large-scale collaboration scenarios. In Federated Intelligence, agents can collaboratively augment their intelligence quotient (IQ) by learning complementary knowledge and fine-grained adaptations. These personalized intelligent agents can also co-work together to jointly address complex tasks in the form of collective intelligence. The paper will highlight federated intelligence as a new pathway for tackling complex intelligent tasks by refining and extending centralized foundation models to an open and collaborative paradigm.
Towards a Theory of Machine Learning on Graphs and its Applications in Combinatorial Optimization
Authors: Morris Christopher
Published: July 2024

Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across many disciplines, from life and physical to social and engineering sciences. Despite their practical success, our theoretical understanding of the properties of GNNs remains incomplete. Here, we survey the author's and his collaborators' progress in developing a deeper theoretical understanding of GNNs' expressive power and generalization abilities. In addition, we overview recent progress in using GNNs to speed up solvers for hard combinatorial optimization tasks.
Expanding the Reach of Social Choice Theory
Authors: Suksompong Warut
Published: July 2024

The field of social choice theory investigates how individual preferences are aggregated to reach collective decisions. While traditional social choice addresses problems such as choosing a winning candidate based on voter rankings or fairly allocating resources among individuals with the same entitlement, the wide range of decision-making scenarios in real-world applications calls for an extension beyond these basic frameworks. In this paper, I present an overview of my efforts to expand the reach of social choice theory in the domains of fair division, voting, and tournaments. Furthermore, I discuss avenues and challenges of bringing the developed theory closer to practice.
PPTFormer: Pseudo Multi-Perspective Transformer for UAV Segmentation
Authors: Ji Deyi , Jin Wenwei , Lu Hongtao , Zhao Feng
Published: July 2024

The ascension of Unmanned Aerial Vehicles (UAVs) in various fields necessitates effective UAV image segmentation, which faces challenges due to the dynamic perspectives of UAV-captured images. Traditional segmentation algorithms falter as they cannot accurately mimic the complexity of UAV perspectives, and the cost of obtaining multi-perspective labeled datasets is prohibitive. To address these issues, we introduce the PPTFormer, a novel Pseudo Multi-Perspective Transformer network that revolutionizes UAV image segmentation. Our approach circumvents the need for actual multi-perspective data by creating pseudo perspectives for enhanced multi-perspective learning. The PPTFormer network boasts Perspective Decomposition, novel Perspective Prototypes, and a specialized encoder and decoder that together achieve superior segmentation results through Pseudo Multi-Perspective Attention (PMP Attention) and fusion. Our experiments demonstrate that PPTFormer achieves state-of-the-art performance across five UAV segmentation datasets, confirming its capability to effectively simulate UAV flight perspectives and significantly advance segmentation precision. This work presents a pioneering leap in UAV scene understanding and sets a new benchmark for future developments in semantic segmentation.
PyClause - Simple and Efficient Rule Handling for Knowledge Graphs
Authors: Betz Patrick , Galárraga Luis , Ott Simon , Meilicke Christian , Suchanek Fabian , Stuckenschmidt Heiner
Published: July 2024

Rule mining finds patterns in structured data such as knowledge graphs. Rules can predict facts, help correct errors, and yield explainable insights about the data. However, existing rule mining implementations focus exclusively on mining rules -- and not on their application. The PyClause library offers a rich toolkit for the application of the mined rules: from explaining facts to predicting links, scoring rules, and deducing query results. The library is easy to use and can handle substantial data loads.
FD-UAD: Unsupervised Anomaly Detection Platform Based on Defect Autonomous Imaging and Enhancement
Authors: Chang Yang , Lin Yuxuan , Wang Boyang , Zhao Qing , Wang Yan , Zhang Wenqiang
Published: July 2024

In industrial quality control, detecting defects is essential. However, manual checks and machine vision encounter challenges in complex conditions, as defects vary among products made of different materials and shapes. We create FD-UAD, Unsupervised Anomaly Detection Platform Based on Defect Autonomous Imaging and Enhancement. It uses multi-sensor technology, combining RGB and infrared imaging, liquid lenses for adjustable focal lengths, and uses image fusion to capture multidimensional features. The system incorporates image restoration techniques such as enhancement, deblurring, denoising, and super-resolution, alongside unsupervised anomaly detection model for enhanced accuracy. FD-UAD is successfully used in a top diesel engine manufacturer, demonstrating its value in AI-enhanced industrial applications.
IntEr-HRI Competition: Intrinsic Error Evaluation during Human - Robot Interaction
Authors: Chari Kartik , Kueper Niklas , Kim Su Kyoung , Kirchner Frank , Kirchner Elsa Andrea
Published: July 2024

Reliable detection of human intentions from electroencephalogram (EEG) to improve human-robot interaction (HRI) has recently gained significant importance. To ensure safe and satisfactory interactions, implicit detection of erroneous behavior of robotic systems, particularly assistive devices, is essential. This can be achieved by detecting error-related potentials (ErrPs) in EEG, evoked by visual, tactile, or visuo-tactile stimuli. Of these, the ErrPs evoked tactilely with the help of a robot remains unexplored and has been the main focus of this competition. The task for participating teams was to develop robust AI models for continuous real-time classification of erroneous behavior of assistive robotic devices from the human EEG. Even though the competition results prove its feasibility, a performance gap (balanced accuracy and computation time) of more than 10% was observed between the offline and online classification of errors in real-world scenarios. In addition to the competitive AI models developed by the participating teams, this competition also contributed towards a one-of-its-kind open-access EEG and EMG dataset, a lossless live streaming solution for EEG data, and a novel quantitative metric for benchmarking online asynchronous EEG detection solutions.
Integrating LLM, VLM, and Text-to-Image Models for Enhanced Information Graphics: A Methodology for Accurate and Visually Engaging Visualizations
Authors: Chen Chao-Ting , Huang Hen-Hsen
Published: July 2024

This study presents an innovative approach to the creation of information graphics, where the accuracy of content and aesthetic appeal are of paramount importance. Traditional methods often struggle to balance these two aspects, particularly in complex visualizations like phylogenetic trees. Our methodology integrates the strengths of Large Language Models (LLMs), Vision Language Models (VLMs), and advanced text-to-image models to address this challenge. Initially, an LLM plans the layout and structure, employing Mermaid—a JavaScript-based tool that uses Markdown-like scripts for diagramming—to establish a precise and structured foundation. This structured script is crucial for ensuring data accuracy in the graphical representation. Following this, text-to-image models are employed to enhance the vector graphic generated by Mermaid, adding rich visual elements and enhancing overall aesthetic appeal. The integration of text-to-image models is a key innovation, enabling the creation of graphics that are not only informative but also visually captivating. Finally, a VLM performs quality control, ensuring that the visual enhancements align with the informational accuracy. This comprehensive approach effectively combines the accuracy of structured data representation, the creative potential of text-to-image models, and the validation capabilities of VLMs. The result is a new standard in information graphic creation, suitable for diverse applications ranging from education to scientific communication, where both information integrity and visual engagement are essential.
Deserción universitaria: el lema y el dilema desde los discursos de Lacan
Authors: Gonzalez Sabogal Diego Alejandro
Published: July 2024

El presente ensayo es una elaboración desde el discurso psicoanalítico, que pretende analizar el fenómeno de la deserción universitaria, como un fenómeno producto de los lemas del discurso del amo y el discurso universitario que desarrolla Lacan, en el que se encuentra una educación homogeneizadora del sujeto que apunta a que sea para todos por igual y a los ideales del mercado, de la educación profesionalizante, de las pedagogías de la razón y del saber como totalidad que configuran el nexo entre la institución y el sujeto.  
Es menester por tanto acoger el dilema como el conflicto que atraviesa la singularidad con las propuestas del psicoanálisis y de las pedagogías críticas que permitan movilizar los lugares comunes de pasividad y homogeneización que generan síntomas en el lazo social como el de la deserción, y pasar a dar una mayor cabida a la singularidad y a las aberturas institucionales.