bluuebunny/crossref_metadata_2025 huggingface dataset:
Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding
Authors: Zhang Yichi , Duan Zhihao , Lu Ming , Ding Dandan , Zhu Fengqing , Ma Zhan
Published: March 2024

While convolution and self-attention are extensively used in learned image compression (LIC) for transform coding, this paper proposes an alternative called Contextual Clustering based LIC (CLIC) which primarily relies on clustering operations and local attention for correlation characterization and compact representation of an image. As seen, CLIC expands the receptive field into the entire image for intra-cluster feature aggregation. Afterward, features are reordered to their original spatial positions to pass through the local attention units for inter-cluster embedding. Additionally, we introduce the Guided Post-Quantization Filtering (GuidedPQF) into CLIC, effectively mitigating the propagation and accumulation of quantization errors at the initial decoding stage. Extensive experiments demonstrate the superior performance of CLIC over state-of-the-art works: when optimized using MSE, it outperforms VVC by about 10% BD-Rate in three widely-used benchmark datasets; when optimized using MS-SSIM, it saves more than 50% BD-Rate over VVC. Our CLIC offers a new way to generate compact representations for image compression, which also provides a novel direction along the line of LIC development.
Multi-Domain Deep Learning from a Multi-View Perspective for Cross-Border E-commerce Search
Authors: Zhang Yiqian , Feng Yinfu , Zhou Wen-Ji , Ye Yunan , Tan Min , Xiao Rong , Tang Haihong , Ding Jiajun , Yu Jun
Published: March 2024

Building click-through rate (CTR) and conversion rate (CVR) prediction models for cross-border e-commerce search requires modeling the correlations among multi-domains. Existing multi-domain methods would suffer severely from poor scalability and low efficiency when number of domains increases. To this end, we propose a Domain-Aware Multi-view mOdel (DAMO), which is domain-number-invariant, to effectively leverage cross-domain relations from a multi-view perspective. Specifically, instead of working in the original feature space defined by different domains, DAMO maps everything to a new low-rank multi-view space. To achieve this, DAMO firstly extracts multi-domain features in an explicit feature-interactive manner. These features are parsed to a multi-view extractor to obtain view-invariant and view-specific features. Then a multi-view predictor inputs these two sets of features and outputs view-based predictions. To enforce view-awareness in the predictor, we further propose a lightweight view-attention estimator to dynamically learn the optimal view-specific weights w.r.t. a view-guided loss. Extensive experiments on public and industrial datasets show that compared with state-of-the-art models, our DAMO achieves better performance with lower storage and computational costs. In addition, deploying DAMO to a large-scale cross-border e-commence platform leads to 1.21%, 1.76%, and 1.66% improvements over the existing CGC-based model in the online AB-testing experiment in terms of CTR, CVR, and Gross Merchandises Value, respectively.
Improved Metric Distortion via Threshold Approvals
Authors: Anshelevich Elliot , Filos-Ratsikas Aris , Jerrett Christopher , Voudouris Alexandros A.
Published: March 2024

We consider a social choice setting in which agents and alternatives are represented by points in a metric space, and the cost of an agent for an alternative is the distance between the corresponding points in the space. The goal is to choose a single alternative to (approximately) minimize the social cost (cost of all agents) or the maximum cost of any agent, when only limited information about the preferences of the agents is given. Previous work has shown that the best possible distortion one can hope to achieve is 3 when access to the ordinal preferences of the agents is given, even when the distances between alternatives in the metric space are known. We improve upon this bound of 3 by designing deterministic mechanisms that exploit a bit of cardinal information. We show that it is possible to achieve distortion 1+sqrt(2) by using the ordinal preferences of the agents, the distances between alternatives, and a threshold approval set per agent that contains all alternatives for whom her cost is within an appropriately chosen factor of her cost for her most-preferred alternative. We show that this bound is the best possible for any deterministic mechanism in general metric spaces, and also provide improved bounds for the fundamental case of a line metric.
Fair Lotteries for Participatory Budgeting
Authors: Aziz Haris , Lu Xinhang , Suzuki Mashbat , Vollen Jeremy , Walsh Toby
Published: March 2024

In pursuit of participatory budgeting (PB) outcomes with broader fairness guarantees, we initiate the study of lotteries over discrete PB outcomes. As the projects have heterogeneous costs, the amount spent may not be equal ex ante and ex post. To address this, we develop a technique to bound the amount by which the ex-post spend differs from the ex-ante spend---the property is termed budget balanced up to one project (BB1). With respect to fairness, we take a best-of-both-worlds perspective, seeking outcomes that are both ex-ante and ex-post fair. Towards this goal, we initiate a study of ex-ante fairness properties in PB, including Individual Fair Share (IFS), Unanimous Fair Share (UFS) and their stronger variants, as well as Group Fair Share (GFS). We show several incompatibility results between these ex-ante fairness notions and existing ex-post concepts based on justified representation. One of our main contributions is a randomized algorithm which simultaneously satisfies ex-ante Strong UFS, ex-post full justified representation (FJR) and ex-post BB1 for PB with binary utilities.
Principal-Agent Reward Shaping in MDPs
Authors: Ben-Porat Omer , Mansour Yishay , Moshkovitz Michal , Taitler Boaz
Published: March 2024

Principal-agent problems arise when one party acts on behalf of another, leading to conflicts of interest. The economic literature has extensively studied principal-agent problems, and recent work has extended this to more complex scenarios such as Markov Decision Processes (MDPs). In this paper, we further explore this line of research by investigating how reward shaping under budget constraints can improve the principal's utility. We study a two-player Stackelberg game where the principal and the agent have different reward functions, and the agent chooses an MDP policy for both players. The principal offers an additional reward to the agent, and the agent picks their policy selfishly to maximize their reward, which is the sum of the original and the offered reward. Our results establish the NP-hardness of the problem and offer polynomial approximation algorithms for two classes of instances: Stochastic trees and deterministic decision processes with a finite horizon.
Participation Incentives in Approval-Based Committee Elections
Authors: Bullinger Martin , Dong Chris , Lederer Patrick , Mehler Clara
Published: March 2024

In approval-based committee (ABC) voting, the goal is to
choose a subset of predefined size of the candidates based on
the voters’ approval preferences over the candidates. While
this problem has attracted significant attention in recent years,
the incentives for voters to participate in an election for a
given ABC voting rule have been neglected so far. This paper
is thus the first to explicitly study this property, typically called
participation, for ABC voting rules. In particular, we show
that all ABC scoring rules even satisfy group participation,
whereas most sequential rules severely fail participation. We
furthermore explore several escape routes to the impossibility
for sequential ABC voting rules: we prove for many sequential
rules that (i) they satisfy participation on laminar profiles, (ii)
voters who approve none of the elected candidates cannot
benefit by abstaining, and (iii) it is NP-hard for a voter to
decide whether she benefits from abstaining
Low-Distortion Clustering with Ordinal and Limited Cardinal Information
Authors: Burkhardt Jakob , Caragiannis Ioannis , Fehrs Karl , Russo Matteo , Schwiegelshohn Chris , Shyam Sudarshan
Published: March 2024

Motivated by recent work in computational social choice, we extend the metric distortion framework to clustering problems. Given a set of n agents located in an underlying metric space, our goal is to partition them into k clusters, optimizing some social cost objective. The metric space is defined by a distance function d between the agent locations. Information about d is available only implicitly via n rankings, through which each agent ranks all other agents in terms of their distance from her. Still, even though no cardinal information (i.e., the exact distance values) is available, we would like to evaluate clustering algorithms in terms of social cost objectives that are defined using d. This is done using the notion of distortion, which measures how far from optimality a clustering can be, taking into account all underlying metrics that are consistent with the ordinal information available.

Unfortunately, the most important clustering objectives (e.g., those used in the well-known k-median and k-center problems) do not admit algorithms with finite distortion. To sidestep this disappointing fact, we follow two alternative approaches: We first explore whether resource augmentation can be beneficial. We consider algorithms that use more than k clusters but compare their social cost to that of the optimal k-clusterings. We show that using exponentially (in terms of k) many clusters, we can get low (constant or logarithmic) distortion for the k-center and k-median objectives. Interestingly, such an exponential blowup is shown to be necessary. More importantly, we explore whether limited cardinal information can be used to obtain better results. Somewhat surprisingly, for k-median and k-center, we show that a number of queries that is polynomial in k and only logarithmic in n (i.e., only sublinear in the number of agents for the most relevant scenarios in practice) is enough to get constant distortion.
The Complexity of Fair Division of Indivisible Items with Externalities
Authors: Deligkas Argyrios , Eiben Eduard , Korchemna Viktoriia , Schierreich Šimon
Published: March 2024

We study the computational complexity of fairly allocating a set of indivisible items under externalities. In this recently-proposed setting, in addition to the utility the agent gets from their bundle, they also receive utility from items allocated to other agents.
We focus on the extended definitions of envy-freeness up to one item (EF1) and of envy-freeness up to any item (EFX), and we provide the landscape of their complexity for several different scenarios. We prove that it is NP-complete to decide whether there exists an EFX allocation, even when there are only three agents, or even when there are only six different values for the items. We complement these negative results by showing that when both the number of agents and the number of different values for items are bounded by a parameter the problem becomes fixed-parameter tractable. Furthermore, we prove that two-valued and binary-valued instances are equivalent and that EFX and EF1 allocations coincide for this class of instances. Finally, motivated from real-life scenarios, we focus on a class of structured valuation functions, which we term agent/item-correlated. We prove their equivalence to the "standard" setting without externalities. Therefore, all previous results for EF1 and EFX apply immediately for these valuations.
Spatial Voting with Incomplete Voter Information
Authors: Imber Aviram , Israel Jonas , Brill Markus , Shachnai Hadas , Kimelfeld Benny
Published: March 2024

We consider spatial voting where candidates are located in the Euclidean d-dimensional space, and each voter ranks candidates based on their distance from the voter's ideal point. We explore the case where information about the location of voters' ideal points is incomplete: for each dimension, we are given an interval of possible values. We study the computational complexity of finding the possible and necessary winners for positional scoring rules. Our results show that we retain tractable cases of the classic model where voters have partial-order preferences. Moreover, we show that there are positional scoring rules under which the possible-winner problem is intractable for partial orders, but tractable in the one-dimensional spatial setting.
We also consider approval voting in this setting. We show that for up to two dimensions, the necessary-winner problem is tractable, while the possible-winner problem is hard for any number of dimensions.
Optimal Mechanism in a Dynamic Stochastic Knapsack Environment
Authors: Jung Jihyeok , Song Chan-Oi , Lee Deok-Joo , Yoon Kiho
Published: March 2024

This study introduces an optimal mechanism in a dynamic stochastic knapsack environment. The model features a single seller who has a fixed quantity of a perfectly divisible item. Impatient buyers with a piece-wise linear utility function arrive randomly and they report the two-dimensional private information: marginal value and demanded quantity. We derive a revenue-maximizing dynamic mechanism in a finite discrete time framework that satisfies incentive compatibility, individual rationality, and feasibility conditions. This is achieved by characterizing buyers' utility and utilizing the Bellman equation. Moreover, we establish the essential penalty scheme for incentive compatibility, as well as the allocation and payment policies. Lastly, we propose algorithms to approximate the optimal policy, based on the Monte Carlo simulation-based regression method and reinforcement learning.
Towards Optimal Subsidy Bounds for Envy-Freeable Allocations
Authors: Kawase Yasushi , Makino Kazuhisa , Sumita Hanna , Tamura Akihisa , Yokoo Makoto
Published: March 2024

We study the fair division of indivisible items with subsidies among n agents, where the absolute marginal valuation of each item is at most one. Under monotone valuations (where each item is a good), it is known that a maximum subsidy of 2(n-1) and a total subsidy of 2(n-1)² are sufficient to guarantee the existence of an envy-freeable allocation. In this paper, we improve upon these bounds, even in a wider model. Namely, we show that, given an EF1 allocation, we can compute in polynomial time an envy-free allocation with a subsidy of at most n-1 per agent and a total subsidy of at most n(n-1)/2. Moreover, we present further improved bounds for monotone valuations.
Opponent-Model Search in Games with Incomplete Information
Authors: Li Junkang , Zanuttini Bruno , Ventos Véronique
Published: March 2024

Games with incomplete information are games that model situations where players do not have common knowledge about the game they play, e.g. card games such as poker or bridge. Opponent models can be of crucial importance for decision-making in such games. We propose algorithms for computing optimal and/or robust strategies in games with incomplete information, given various types of knowledge about opponent models. As an application, we describe a framework for reasoning about an opponent's reasoning in such games, where opponent models arise naturally.
On the Outcome Equivalence of Extensive-Form and Behavioral Correlated Equilibria
Authors: Zhang Brian Hu , Sandholm Tuomas
Published: March 2024

We investigate two notions of correlated equilibrium for extensive-form games: the extensive-form correlated equilibrium (EFCE) and the behavioral correlated equilibrium (BCE). We show that the two are outcome-equivalent, in the sense that every outcome distribution achievable under one notion is achievable under the other. Our result implies, to our knowledge, the first polynomial-time algorithm for computing a BCE.
Data-Driven Knowledge-Aware Inference of Private Information in Continuous Double Auctions
Authors: Cui Lvye , Yu Haoran
Published: March 2024

Inferring the private information of humans from their strategic behavioral data is crucial and challenging. The main approach is first obtaining human behavior functions (which map public information and human private information to behavior), enabling subsequent inference of private information from observed behavior. Most existing studies rely on strong equilibrium assumptions to obtain behavior functions. Our work focuses on continuous double auctions, where multiple traders with heterogeneous rationalities and beliefs dynamically trade commodities and deriving equilibria is generally intractable. We develop a knowledge-aware machine learning-based framework to infer each trader's private cost vectors for producing different units of its commodity. Our key idea is to learn behavior functions by incorporating the statistical knowledge about private costs given the observed trader asking behavior across the population. Specifically, we first use a neural network to characterize each trader's behavior function. Second, we leverage the statistical knowledge to derive the posterior distribution of each trader's private costs given its observed asks. Third, through designing a novel loss function, we utilize the knowledge-based posterior distributions to guide the learning of the neural network. We conduct extensive experiments on a large experimental dataset, and demonstrate the superior performance of our framework over baselines in inferring the private information of humans.
Hypergraph-Guided Disentangled Spectrum Transformer Networks for Near-Infrared Facial Expression Recognition
Authors: Luo Bingjun , Wang Haowen , Wang Jinpeng , Zhu Junjie , Zhao Xibin , Gao Yue
Published: March 2024

With the strong robusticity on illumination variations, near-infrared (NIR) can be an effective and essential complement to visible (VIS) facial expression recognition in low lighting or complete darkness conditions. However, facial expression recognition (FER) from NIR images presents a more challenging problem than traditional FER due to the limitations imposed by the data scale and the difficulty of extracting discriminative features from incomplete visible lighting contents. In this paper, we give the first attempt at deep NIR facial expression recognition and propose a novel method called near-infrared facial expression transformer (NFER-Former). Specifically, to make full use of the abundant label information in the field of VIS, we introduce a Self-Attention Orthogonal Decomposition mechanism that disentangles the expression information and spectrum information from the input image, so that the expression features can be extracted without the interference of spectrum variation. We also propose a Hypergraph-Guided Feature Embedding method that models some key facial behaviors and learns the structure of the complex correlations between them, thereby alleviating the interference of inter-class similarity. Additionally, we construct a large NIR-VIS Facial Expression dataset that includes 360 subjects to better validate the efficiency of NFER-Former. Extensive experiments and ablation studies show that NFER-Former significantly improves the performance of NIR FER and achieves state-of-the-art results on the only two available NIR FER datasets, Oulu-CASIA and Large-HFE.
Spatial-Related Sensors Matters: 3D Human Motion Reconstruction Assisted with Textual Semantics
Authors: Yang Xueyuan , Yao Chao , Ban Xiaojuan
Published: March 2024

Leveraging wearable devices for motion reconstruction has emerged as an economical and viable technique. Certain methodologies employ sparse Inertial Measurement Units (IMUs) on the human body and harness data-driven strategies to model human poses. However, the reconstruction of motion based solely on sparse IMU data is inherently fraught with ambiguity, a consequence of numerous identical IMU readings corresponding to different poses. In this paper, we explore the spatial importance of sparse sensors, supervised by text that describes specific actions. Specifically, uncertainty is introduced to derive weighted features for each IMU. We also design a Hierarchical Temporal Transformer (HTT) and apply contrastive learning to achieve precise temporal and feature alignment of sensor data with textual semantics. Experimental results demonstrate our proposed approach achieves significant improvements in multiple metrics compared to existing methods. Notably, with textual supervision, our method not only differentiates between ambiguous actions such as sitting and standing but also produces more precise and natural motion.
DexFuncGrasp: A Robotic Dexterous Functional Grasp Dataset Constructed from a Cost-Effective Real-Simulation Annotation System
Authors: Hang Jinglue , Lin Xiangbo , Zhu Tianqiang , Li Xuanheng , Wu Rina , Ma Xiaohong , Sun Yi
Published: March 2024

Robot grasp dataset is the basis of designing the robot's grasp generation model. Compared with the building grasp dataset for Low-DOF grippers, it is harder for High-DOF dexterous robot hand. Most current datasets meet the needs of generating stable grasps, but they are not suitable for dexterous hands to complete human-like functional grasp, such as grasp the handle of a cup or pressing the button of a flashlight, so as to enable robots to complete subsequent functional manipulation action autonomously, and there is no dataset with functional grasp pose annotations at present. This paper develops a unique Cost-Effective Real-Simulation Annotation System by leveraging natural hand's actions. The system is able to capture a functional grasp of a dexterous hand in a simulated environment assisted by human demonstration in real world. By using this system, dexterous grasp data can be collected efficiently as well as cost-effective. Finally, we construct the first dexterous functional grasp dataset with rich pose annotations. A Functional Grasp Synthesis Model is also provided to validate the effectiveness of the proposed system and dataset. Our project page is: https://hjlllll.github.io/DFG/.
MorphVAE: Advancing Morphological Design of Voxel-Based Soft Robots with Variational Autoencoders
Authors: Song Junru , Yang Yang , Peng Wei , Zhou Weien , Wang Feifei , Yao Wen
Published: March 2024

Soft robot design is an intricate field with unique challenges due to its complex and vast search space. In the past literature, evolutionary computation algorithms, including novel probabilistic generative models (PGMs), have shown potential in this realm. However, these methods are sample inefficient and predominantly focus on rigid robots in locomotion tasks, which limit their performance and application in robot design automation. In this work, we propose MorphVAE, an innovative PGM that incorporates a multi-task training scheme and a meticulously crafted sampling technique termed ``continuous natural selection'', aimed at bolstering sample efficiency. This method empowers us to gain insights from assessed samples across diverse tasks and temporal evolutionary stages, while simultaneously maintaining a delicate balance between optimization efficiency and biodiversity. Through extensive experiments in various locomotion and manipulation tasks, we substantiate the efficiency of MorphVAE in generating high-performing and diverse designs, surpassing the performance of competitive baselines.
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning
Authors: Zha Yantian , Guan Lin , Kambhampati Subbarao
Published: March 2024

Our work aims at efficiently leveraging ambiguous demonstrations for the training of a reinforcement learning (RL) agent. An ambiguous demonstration can usually be interpreted in multiple ways, which severely hinders the RL agent from learning stably and efficiently. Since an optimal demonstration may also suffer from being ambiguous, previous works that combine RL and learning from demonstration (RLfD works) may not work well. Inspired by how humans handle such situations, we propose to use self-explanation (an agent generates explanations for itself) to recognize valuable high-level relational features as an interpretation of why a successful trajectory is successful. This way, the agent can leverage the explained important relations as guidance for its RL learning. Our main contribution is to propose the Self-Explanation for RL from Demonstrations (SERLfD) framework, which can overcome the limitations of existing RLfD works. Our experimental results show that an RLfD model can be improved by using our SERLfD framework in terms of training stability and performance. To foster further research in self-explanation-guided robot learning, we have made our demonstrations and code publicly accessible at https://github.com/YantianZha/SERLfD. For a deeper understanding of our work, interested readers can refer to our arXiv version at https://arxiv.org/pdf/2110.05286.pdf, including an accompanying appendix.
Complexity of Credulous and Skeptical Acceptance in Epistemic Argumentation Framework
Authors: Alfano Gianvincenzo , Greco Sergio , Parisi Francesco , Trubitsyna Irina
Published: March 2024

Dung’s Argumentation Framework (AF) has been extended in several directions. Among the numerous proposed extensions, three of them seem to be of particular interest and have correlations between them. These extensions are: constrained AF (CAF), where AF is augmented with (strong) constraints; epistemic AF (EAF), where AF is augmented with epistemic constraints; and incomplete AF (iAF), where arguments and attacks can be uncertain. While the complexity and expressiveness of CAF and iAF have been studied, that of EAF has not been explored so far. In this paper we investigate the complexity and expressivity of EAF. To this end, we first introduce the Labeled CAF (LCAF), a variation of CAF where constraints are defined over the alphabet of labeled arguments. Then, we investigate the complexity of credulous and skeptical reasoning and show that: i) EAF is more expressive than iAF (under preferred semantics), ii) although LCAF is a restriction of EAF where modal operators are not allowed, these frameworks have the same complexity, iii) the results for LCAF close a gap in the characterization of the complexity of CAF. Interestingly, even though EAF has the same complexity as LCAF, it allows modeling domain knowledge in a more natural and easy-to-understand way.
Defeasible Normative Reasoning: A Proof-Theoretic Integration of Logical Argumentation
Authors: Arieli Ofer , Van Berkel Kees , Straßer Christian
Published: March 2024

We present a novel computational approach to resolving conflicts among norms by nonmonotonic normative reasoning (in constrained I/O logics). Our approach extends standard sequent-based proof systems and makes them more adequate to nonmonotonic reasoning by adding to the sequents annotations that keep track of what is known about the defeasible status of the derived sequents. This makes transparent the reasons according to which norms should be applicable or inapplicable, and accordingly the sequents that make use of such norms are accepted or retracted. We also show that this proof theoretic method has tight links to the semantics of formal argumentation frameworks. The outcome of this paper is thus a threefold characterization result that relates, in the context of nonmonotonic normative reasoning, three traditional ingredients of AI-based reasoning methods: maximally consistent sets of premises (in constrained I/O logics), derived sequents (which are accepted in corresponding annotated sequent calculi), and logical arguments (that belong to the grounded extensions of the induced logical argumentation frameworks).
Generalisation through Negation and Predicate Invention
Authors: Cerna David M. , Cropper Andrew
Published: March 2024

The ability to generalise from a small number of examples is a fundamental challenge in machine learning. To tackle this challenge, we introduce an inductive logic programming (ILP) approach that combines negation and predicate invention.
Combining these two features allows an ILP system to generalise better by learning rules with universally quantified body-only variables. We implement our idea in NOPI, which can learn normal logic programs with predicate invention, including Datalog programs with stratified negation. Our experimental results on multiple domains show that our approach can improve predictive accuracies and learning times.
Submodel Enumeration for CTL Is Hard
Authors: Fröhlich Nicolas , Meier Arne
Published: March 2024

Expressing system specifications using Computation Tree Logic (CTL) formulas, formalising programs using Kripke structures, and then model checking the system is an established workflow in program verification and has wide applications in AI. In this paper, we consider the task of model enumeration, which asks for a uniform stream of output systems that satisfy the given specification. We show that, given a CTL formula and a system (potentially falsified by the formula), enumerating satisfying submodels is always hard for CTL--regardless of which subset of CTL-operators is considered. As a silver lining on the horizon, we present fragments via restrictions on the allowed Boolean functions that still allow for fast enumeration.
On the Structural Hardness of Answer Set Programming: Can Structure Efficiently Confine the Power of Disjunctions?
Authors: Hecher Markus , Kiesel Rafael
Published: March 2024

Answer Set Programming (ASP) is a generic problem modeling and solving framework with a strong focus on knowledge representation and a rapid growth of industrial applications. So far, the study of complexity resulted in characterizing hardness and determining their sources, fine-grained insights in the form of dichotomy-style results, as well as detailed parameterized complexity landscapes. Unfortunately, for the well-known parameter treewidth disjunctive programs require double-exponential runtime under reasonable complexity assumptions. This quickly becomes out of reach. We deal with the classification of structural parameters for disjunctive ASP on the program's rule structure (incidence graph).
First, we provide a polynomial kernel to obtain single-exponential runtime in terms of vertex cover size, despite subset-minimization being not represented in the program’s structure. Then we turn our attention to strictly better structural parameters between vertex cover size and treewidth. Here, we provide double-exponential lower bounds for the most prominent parameters in that range: treedepth, feedback vertex size, and cliquewidth. Based on this, we argue that unfortunately our options beyond vertex cover size are limited. Our results provide an in-depth hardness study, relying on a novel reduction from normal to disjunctive programs, trading the increase of complexity for an exponential parameter compression.
Knowledge Enhanced Representation Learning for Drug Discovery
Authors: Hoang Thanh Lam , Sbodio Marco Luca , Martinez Galindo Marcos , Zayats Mykhaylo , Fernandez-Diaz Raul , Valls Victor , Picco Gabriele , Berrospi Cesar , Lopez Vanessa
Published: March 2024

Recent research on predicting the binding affinity between drug molecules and proteins use representations learned, through unsupervised learning techniques, from large databases of molecule SMILES and protein sequences. While these representations have significantly enhanced the predictions, they are usually based on a limited set of modalities, and they do not exploit available knowledge about existing relations among molecules and proteins. Our study reveals that enhanced representations, derived from multimodal knowledge graphs describing relations among molecules and proteins, lead to state-of-the-art results in well-established benchmarks (first place in the leaderboard for Therapeutics Data Commons benchmark ``Drug-Target Interaction Domain Generalization Benchmark", with an improvement of 8 points with respect to previous best result). Moreover, our results significantly surpass those achieved in standard benchmarks by using conventional pre-trained representations that rely only on sequence or SMILES data. We release our multimodal knowledge graphs, integrating data from seven public data sources, and which contain over 30 million triples. Pretrained models from our proposed graphs and benchmark task source code are also released.
A Unified View on Forgetting and Strong Equivalence Notions in Answer Set Programming
Authors: Saribatur Zeynep G. , Woltran Stefan
Published: March 2024

Answer Set Programming (ASP) is a prominent rule-based language for knowledge representation and reasoning with roots in logic programming and non-monotonic reasoning. The aim to capture the essence of removing (ir)relevant details in ASP programs led to the investigation of different notions, from strong persistence (SP) forgetting, to faithful abstractions, and, recently, strong simplifications, where the latter two can be seen as relaxed and strengthened notions of forgetting, respectively. Although it was observed that these notions are related, especially given that they have characterizations through the semantics for strong equivalence, it remained unclear whether they can be brought together. In this work, we bridge this gap by introducing a novel relativized equivalence notion, which is a relaxation of the recent simplification notion, that is able to capture all related notions from the literature. We provide the necessary and sufficient conditions for relativized simplifiability, which shows that the challenging part is for when the context programs do not contain all the atoms to remove. We then introduce an operator that combines projection and a relaxation of SP-forgetting to obtain the relativized simplifications. We furthermore provide complexity results that complete the overall picture.
Optimised Storage for Datalog Reasoning
Authors: Zhang Xinyue , Hu Pan , Nenov Yavor , Horrocks Ian
Published: March 2024

Materialisation facilitates Datalog reasoning by precomputing all consequences of the facts and the rules so that queries can be directly answered over the materialised facts. However, storing all materialised facts may be infeasible in practice, especially when the rules are complex and the given set of facts is large. We observe that for certain combinations of rules, there exist data structures that compactly represent the reasoning result and can be efficiently queried when necessary. In this paper, we present a general framework that allows for the integration of such optimised storage schemes with standard materialisation algorithms. Moreover, we devise optimised storage schemes targeting at transitive rules and union rules, two types of (combination of) rules that commonly occur in practice. Our experimental evaluation shows that our approach significantly improves memory consumption, sometimes by orders of magnitude, while remaining competitive in terms of query answering time.
Constructing efficient Ti4O7/PbO2 heterostructured photoelectrode for water remediation
Authors: Li Xinyu , Hossain Md Azharul , Jiang Zeqi , Fu Yinghuan , Ma Hongchao
Published: March 2024

Photoelectrocatalytic oxidation (PEC) technology is considered to be an efficient process for the treatment of organic wastewater, and its performance depends on the characteristics of the PEC photoanodes. Therefore, the construction of heterogeneous nanostructured photoelectrodes based on suitable semiconductor materials for fast induced carrier transfer efficiency is essential for a high-performance PEC technique. Herein, titanium suboxide/lead (IV) oxide (Ti4O7/PbO2) ceramic photoelectrode with efficient PEC performance was synthesized by coupling PbO2 nanospheres with Ti4O7 through a hydrothermal method. To maximize the PEC performance of the ceramic electrodes, the Ti4O7/PbO2 nano-heterostructures were optimized by modulating the hydrothermal temperature. The optimized ceramic electrodes possessed a low Tafel slope, a low charge-transfer resistance and a good photocurrent response. Ti4O7/PbO2-110 exhibited an efficient PEC activity (approximately 92.21%) for the degradation of reactive brilliant blue KN-R. The efficient PEC performance of Ti4O7/PbO2-110 arose from the formation of a type II heterojunction between Ti4O7 and PbO2, which achieved efficient photogenerated carrier separation and facilitated the formation of intermediate active species. This work not only validates the efficient performance of Ti4O7/PbO2-110 for PEC water purification but also provides a reference strategy for the preparation of heterostructured ceramic photoelectrodes with a high PEC efficiency.
Die unsichtbaren neunzig Prozent hervorholen – Rezension von Thomas Honegger "Tweaking Things a Little"
Authors: Weinreich Frank
Published: March 2024

Rezension von Thomas Honegger. Tweaking Things a Little. Essays on the Fantasy of J.R.R. Tolkien and G.R.R. Martin. Zurich, Jena 2023
Cerrahi Hemşirelerin Aşılanma Durumlarının COVİD-19 ve Ölüm Korkusuna Etkisi
Authors: Topal Dilek , Karaman Özlü Zeynep
Published: March 2024

Amaç : Bu araştırmanın amacı, cerrahi kliniklerde çalışan hemşirelerin aşılanma durumlarının Covid-19 ve ölüm korkusuna etkisini incelemekti. Yöntemler : Tanımlayıcı türdeki bu çalışma, Nisan 2021-Kasım 2021 tarihleri arasında Erzurum ilinde bir üniversite araştırma hastanesi ve bir bölge eğitim ve araştırma hastanesinin cerrahi kliniklerinde görev yapan 110 hemşire ile gerçekleştirildi. Verilerin toplanmasında “Sağlık Çalışanı Tanıtıcı Bilgi Formu”, “Covid-19 Korkusu Ölçeği” ve “Tanatofobi Ölçeği” ile toplandı. Bulgular : Aşı olan hemşirelerin Covid-19 Korkusu Ölçeği puan ortalamaları 17,90 ± 6,102, aşı olmayan hemşirelerin 17,24 ± 6,13 olarak bulundu. Aşı olan hemşirelerin Tanatofobi Ölçeği puan ortalamaları 16,88 ± 5,10, aşı olmayan hemşirelerin 16,84 ± 4,28 olduğu belirlendi. Hemşirelerin aşılanma durumları ile Covid-19 korkusu ve Tanatofobi ölçeklerinden aldıkları puan ortalamaları arasında istatistiksel anlamlılık oluşturacak bir fark olmadığı saptandı (P>0,05). Aşı olmayan hemşirelerin Tanatofobi Ölçeği ile Covid-19 Korkusu Ölçeği toplam puan ortalamaları arasında pozitif yönde ileri derecede anlamlı bir ilişki olduğu belirlendi (P<0,001). Aşı olan hemşirelerin sadece çalışılan klinikteki eleman sayısı ile aşı olmayan hemşirelerin ise eğitim durumları ve çalışılan klinikteki eleman sayısı ile Tanatofobi Ölçeği puan ortalamaları arasındaki farkın istatistiksel olarak önemli olduğu tespit edildi (P<0,05). Sonuç: Cerrahi klinikte çalışan hemşirelerin aşılanma durumlarının Covid-19 ve ölüm korkusu üzerinde anlamlı farklılık oluşturmadığı ancak aşı olmayan hemşirelerin Covid-19 korkuları artıkça ölüm korkularının arttığı görüldü. Çalışılan klinikteki hemşire sayısının yetersiz veya kısmen yeterli olmasının da ölüm korkusunu artırdığı belirlendi.
Yerkes, M. A. & Bal, M. (Eds.) (2022). Solidarity and Social Justice in Contemporary Societies: An Interdisciplinary Approach to Understanding Inequalities. Springer
Authors: Yi Ran
Published: March 2024

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Solving Linear and Nonlinear Fuzzy Fractional Volterra-Fredholm Integro Differential Equations Using Shehu Adomian Decomposition Method
Authors:   Research Scholar, PG & Research Department of Mathematics, Bishop Heber College (affiliated to Bharathidasan University), Tiruchirapppalli-17, Tamil Nadu, India, Savla S Luvis , Sharmila R Gethsi
Published: March 2024

Objectives: In applied sciences and engineering, fuzzy fractional differential equations (FFDEs) and fuzzy fractional integral equations (FFIEs) are a crucial topic. The main objective of this work is to discover an analytical approximate solution for the fuzzy fractional Volterra-Fredholm integro differential equations (FFVFIDE). In the Caputo concept, fractional derivatives are regarded. Methods: The Shehu transform is challenging to exist for nonlinear problems. So, the Shehu transform is combined with the Adomian decomposition method is called the Shehu Adomian decomposition method (SHADM) and has been proposed to solve both linear and nonlinear FFVFIDEs. Findings: Both linear and nonlinear FFVIFIDEs can be solved using this technique. For nonlinear terms, Adomian polynomials have been used. The main benefit of this approach is that it converges quickly to the exact solution. Figures and numerical examples demonstrate the expertise of the suggested approach. Novelty: The comparison between the exact solution and numerical solution is shown in figures for various values of fractional order . The numerical evolution demonstrates the efficiency and reliability of the proposed SHADM. The proposed approach is rapid, exact, and simple to apply and produce excellent outcomes. Keywords: Fractional calculus, fuzzy number, Mittag ­ Leffler function, Shehu Adomian decomposition method, fuzzy fractional Volterra­-Fredholm integro differential equation
A Two-Phase Approach for Efficient Traffic Sign Detection and Recognition
Authors:   Associate Professor, Department of Computer Applications, B.M.S. College of Engineering, Bangalore, Karnataka, India, Uma S , Prateeksha S , Padmapriya V
Published: March 2024

Objectives: The objective of this study is to enhance the accuracy of traffic sign detection and recognition in modern intelligent transport systems, addressing real-time challenges under varying conditions. Methods: A two-phase approach is adopted. The first phase employs the You Only Look Once version 8 (YOLOv8) architecture to efficiently detect traffic signs under real-time conditions, considering variables like adverse weather and obstructions. Subsequently, the second phase employs a sequential convolutional network for precise recognition, utilizing the output from the first phase. This integrated method enhances traffic sign detection and recognition, contributing to road safety and efficient traffic management in complex transportation scenarios. Findings: The YOLOv8 architecture, utilized in Phase 1, demonstrated exceptional performance with a mean Average Precision (mAP) of 0.986 during validation. In Phase 2, the Convolutional Neural Network (CNN)-based recognition model achieved an impressive test accuracy of 98.7% on 463 test images, with a low-test loss of 0.1186, indicating consistent accuracy. The robustness of both models is confirmed by successful testing with three unseen images. YOLOv8 accurately detected and classified these images, while the CNN model correctly recognized them. These findings underscore the effectiveness of the two-phase approach in enhancing traffic sign detection and recognition, with significant implications for improving road safety and traffic management in real-world scenarios. Novelty: The novelty of this approach lies in its seamless integration of YOLOv8 for efficient traffic sign detection and a sequential convolutional network for accurate recognition, offering a significant advancement in addressing real-time challenges and contributing to enhancing road safety and traffic management in an increasingly complex transportation landscape. Keywords: Traffic sign detection, Traffic sign recognition, Convolutional Neural Networks, YOLOv8, Object detection
Optimizing High Data Rate Up to 2.5 Tbps Transmission using 64-Channel DWDM System with DCF and NRZ Modulation
Authors:   Ph.D. Scholar, Department of physics, C.U. Shah University, Surendranagar, Gujrat, India, Thummar Ruturaj , Dhadhal Dharmendra , Mishra Vivekanand
Published: March 2024

Objectives: This study aims to enhance and optimize a modern communication system for high data rate transmission using Dense Wavelength Division Multiplexing (DWDM). The objectives include employing a 64-channel DWDM system, implementing different data speeds, and utilizing a dispersion compensation method. Methods: To achieve the objectives, we simulate and analyze the effects of Dispersion Compensation Fiber (DCF) in a DWDM system with 64 channels. The system employs Non-Return-to-Zero (NRZ) modulation format at varied bit rates and multiple energy levels. We propose a method for achieving data rates of 10 to 40 Gbps using NRZ modulation and Erbium-Doped Fiber Amplifier (EDFA) over a single-mode fiber transmission distance of 40 to 160 km, along with a dispersion compensation fiber of 8 to 32 km (DCF). The performance of the developed model is evaluated based on Quality Factor, Bit Error Rate (BER), Eye Height, and Threshold. These metrics are measured at two input energy levels from an optical power source, covering a communication capacity ranging from 0.625 Tbps to 2.5 Tbps. Findings: Through the simulations and analyses, we uncover the impact of dispersion and demonstrate the effectiveness of the proposed method in compensating for dispersion effects. Performance analysis of two different input transmitter power levels, -10 dBm is more efficient compared to 0 dBm input transmitter power in terms of bit error rate and quality factor. Novelty: This study presents a novel approach to improving modern communication systems by utilizing DWDM with a dispersion compensation method. The use of a 64-channel DWDM system, combined with the proposed NRZ modulation technique and dispersion compensation fiber, provides an efficient solution for achieving high data rates over long transmission distances while minimizing the effects of dispersion. The findings contribute to the advancement of communication systems for high data rate transmission. Keywords Dispersion effect, Bit Error Rate, Q-factor, Optisystem software, Erbium doped fiber amplifier
DAPI Staining Mouse Brain Sections v1
Authors: not provided daphne.toglia , Myers Holly
Published: March 2024

This protocol details the steps for staining 4% PFA fixed mouse brain tissue sections with DAPI (4',6-diamidino-2-phenylindole), a fluorescent stain that can be used to label anatomic regions of interest in a mouse brain, allowing for imaging with a fluorescent microscope. The protocol includes a table with suggested staining duration based on section thickness and guidelines for calculating the volume of DAPI solution needed for staining.
Plate Scale Tn5 based tagmentation library prep protocol v1
Authors: Mirchandani Cade , genetti max , Wang Pingting , Pepper-Tunick Evan , Russell Shelbi , Corbett-Detig Russell
Published: March 2024

This protocol is a fork of (dx.doi.org/10.17504/protocols.io.bv5gn83w), which a demonstrated efficient and high-throughput tagmentation sequencing library prep protocol based on Picelli et al 2014. In this version, the tagmentation and amplification steps remain largely the same - based on the Tn5 transposase and KAPA HiFi kit, respectively. Here, significant modifications have been made to the library pooling and cleanup processes. Specifically, this protocol involves pooling the crude PCR products, followed by the removal of small fragments via a spin column kit, and depletion of large fragments through agarose gel extraction. These modifications enable faster processing times while still producing high quality sequencing libraries. This protocol has been optimized for sequencing Drosophila cell culture samples, from which high-quality genomic DNA (gDNA) can be readily obtained. The accessibility to high-quality gDNA simplifies the normalization of gDNA input amounts prior to tagmentation, thereby streamlining the pooling and cleanup steps.
Fiber Photometry during Sleep v1
Authors: daniel daniel.dautan , Svenningsson Per
Published: March 2024

Measurement of photometry during sleep using AAVs
2. PERFF-seq: HCR Flow-FISH and Polymer Disassembly Protocol v1
Authors: Abay Tsion , Stickels Robert , Takizawa Meril , Chaligne Ronan , Lareau Caleb
Published: March 2024

This protocol can be used for performing HCR Flow-FISH followed by cell/nuclei enrichment via FACS and polymer disassembly on fixed and permeabilized cells and tissue derived nuclei. This protocol should be performed after finalizing "PERFF-seq: Cell and Nuclei Preparation" for the desired sample type.