bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction
Authors: Zhou Qiang , Gu Jingjing , Lu Xinjiang , Zhuang Fuzhen , Zhao Yanchao , Wang Qiuhong , Zhang Xiao
Published: September 2022

Potential crowd flow prediction for new planned transportation sites is a fundamental task for urban planners and administrators. Intuitively, the potential crowd flow of the new coming site can be implied by exploring the nearby sites. However, the transportation modes of nearby sites (e.g. bus stations, bicycle stations) might be different from the target site (e.g. subway station), which results in severe data scarcity issues. To this end, we propose a data-driven approach, named MOHER, to predict the potential crowd flow in a certain mode for a new planned site. Specifically, we first identify the neighbor regions of the target site by examining the geographical proximity as well as the urban function similarity. Then, to aggregate these heterogeneous relations, we devise a cross-mode relational GCN, a novel relation-specific transformation model, which can learn not only the correlation but also the differences between different transportation modes. Afterward, we design an aggregator for inductive potential flow representation. Finally, an LTSM module is used for sequential flow prediction. Extensive experiments on real-world data sets demonstrate the superiority of the MOHER framework compared with the state-of-the-art algorithms.
Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks
Authors: Zhu Cunchao , Chen Muhao , Fan Changjun , Cheng Guangquan , Zhang Yan
Published: September 2022

Large knowledge graphs often grow to store temporal facts that model the dynamic relations or interactions of entities along the timeline. Since such temporal knowledge graphs often suffer from incompleteness, it is important to develop time-aware representation learning models that help to infer the missing temporal facts. While the temporal facts are typically evolving, it is observed that many facts often show a repeated pattern along the timeline, such as economic crises and diplomatic activities. This observation indicates that a model could potentially learn much from the known facts appeared in history. To this end, we propose a new representation learning model for temporal knowledge graphs, namely CyGNet, based on a novel time-aware copy-generation mechanism. CyGNet is not only able to predict future facts from the whole entity vocabulary, but also capable of identifying facts with repetition and accordingly predicting such future facts with reference to the known facts in the past. We evaluate the proposed method on the knowledge graph completion task using five benchmark datasets. Extensive experiments demonstrate the effectiveness of CyGNet for predicting future facts with repetition as well as de novo fact prediction.
Relation-Aware Neighborhood Matching Model for Entity Alignment
Authors: Zhu Yao , Liu Hongzhi , Wu Zhonghai , Du Yingpeng
Published: September 2022

Entity alignment which aims at linking entities with the same meaning from different knowledge graphs (KGs) is a vital step for knowledge fusion. Existing research focused on learning embeddings of entities by utilizing structural information of KGs for entity alignment. These methods can aggregate information from neighboring nodes but may also bring noise from neighbors. Most recently, several researchers attempted to compare neighboring nodes in pairs to enhance the entity alignment. However, they ignored the relations between entities which are also important for neighborhood matching. In addition, existing methods paid less attention to the positive interactions between the entity alignment and the relation alignment. To deal with these issues, we propose a novel Relation-aware Neighborhood Matching model named RNM for entity alignment. Specifically, we propose to utilize the neighborhood matching to enhance the entity alignment. Besides comparing neighbor nodes when matching neighborhood, we also try to explore useful information from the connected relations. Moreover, an iterative framework is designed to leverage the positive interactions between the entity alignment and the relation alignment in a semi-supervised manner. Experimental results on three real-world datasets demonstrate that the proposed model RNM performs better than state-of-the-art methods.
Persistence of Anti-vaccine Sentiment in Social Networks Through Strategic Interactions
Authors: Haque A S M Ahsan-Ul , Thakur Mugdha , Bielskas Matthew , Marathe Achla , Vullikanti Anil
Published: September 2022

Vaccination is the primary intervention for controlling the spread of infectious diseases. A certain level of vaccination rate (referred to as "herd immunity'') is needed for this intervention to be effective. However, there are concerns that herd immunity might not be achieved due to an increasing level of hesitancy and opposition to vaccines. One of the primary reasons for this is the cost of non-conformance with one's peers. We use the framework of network coordination games to study the persistence of anti-vaccine sentiment in a population. We extend it to incorporate the opposing forces of the pressure of conforming to peers, herd-immunity and vaccination benefits. We study the structure of the equilibria in such games, and the characteristics of unvaccinated nodes. We also study Stackelberg strategies to reduce the number of nodes with anti-vaccine sentiment. Finally, we evaluate our results on different kinds of real world social networks.
Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19
Authors: Rodríguez Alexander , Muralidhar Nikhil , Adhikari Bijaya , Tabassum Anika , Ramakrishnan Naren , Prakash B. Aditya
Published: September 2022

Forecasting influenza in a timely manner aids health organizations and policymakers in adequate preparation and decision making. However, effective influenza forecasting still remains a challenge despite increasing research interest. It is even more challenging amidst the COVID pandemic, when the influenza-like illness (ILI) counts are affected by various factors such as symptomatic similarities with COVID-19 and shift in healthcare seeking patterns of the general population. Under the current pandemic, historical influenza models carry valuable expertise about the disease dynamics but face difficulties adapting. Therefore, we propose CALI-Net, a neural transfer learning architecture which allows us to 'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our framework enables this adaptation by automatically learning when it should emphasize learning from COVID-related signals and when it should learn from the historical model. Thus, we exploit representations learned from historical ILI data as well as the limited COVID-related signals. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic. In addition, we show that success in our primary goal, adaptation, does not sacrifice overall performance as compared with state-of-the-art influenza forecasting approaches.
Reinforcement Learning of Sequential Price Mechanisms
Authors: Brero Gianluca , Eden Alon , Gerstgrasser Matthias , Parkes David , Rheingans-Yoo Duncan
Published: September 2022

We introduce the use of reinforcement learning for indirect mechanisms, working with the existing class of sequential price mechanisms, which generalizes both serial dictatorship and posted price mechanisms and essentially characterizes all strongly obviously strategyproof mechanisms. Learning an optimal mechanism within this class forms a partially-observable Markov decision process. We provide rigorous conditions for when this class of mechanisms is more powerful than simpler static mechanisms, for sufficiency or insufficiency of observation statistics for learning, and for the necessity of complex (deep) policies. We show that our approach can learn optimal or near-optimal mechanisms in several experimental settings.
PoA of Simple Auctions with Interdependent Values
Authors: Eden Alon , Feldman Michal , Talgam-Cohen Inbal , Zviran Ori
Published: September 2022

We expand the literature on the price of anarchy (PoA) of simultaneous item auctions by considering settings with correlated values; we do this via the fundamental economic model of interdependent values (IDV).
It is well-known that in multi-item settings with private values, correlated values can lead to bad PoA, which can be polynomially large in the number of agents~n.
In the more general model of IDV, we show that the PoA can be polynomially large even in single-item settings.
On the positive side, we identify a natural condition on information dispersion in the market, which enables good PoA guarantees.
Under this condition, we show that for single-item settings, the PoA of standard mechanisms degrades gracefully.
For settings with multiple items we show a separation between two domains:
If there are more buyers, we devise a new simultaneous item auction with good PoA, under limited information asymmetry.
To the best of our knowledge, this is the first positive PoA result for correlated values in multi-item settings.
The main technical difficulty in establishing this result is that the standard tool for establishing PoA results --- the smoothness framework --- is unsuitable for IDV settings, and so we must introduce new techniques to address the unique challenges imposed by such settings.
In the domain of more items, we establish impossibility results even for surprisingly simple scenarios.
Almost Envy-freeness, Envy-rank, and Nash Social Welfare Matchings
Authors: Farhadi Alireza , Hajiaghayi MohammadTaghi , Latifian Mohamad , Seddighin Masoud , Yami Hadi
Published: September 2022

Envy-freeness up to one good (EF1) and envy-freeness up to any good (EFX) are two well-known extensions of envy-freeness for the case of indivisible items. It is shown that EF1 can always be guaranteed for agents with subadditive valuations. In sharp contrast, it is unknown whether or not an EFX allocation always exists, even for four agents and additive valuations. In addition, the best approximation guarantee for EFX is (φ − 1) ≃ 0.61 by Amanitidis et al..
In order to find a middle ground to bridge this gap, in this paper we suggest another fairness criterion, namely envy-freeness up to a random good or EFR, which is weaker than EFX, yet stronger than EF1. For this notion, we provide a polynomial-time 0.73-approximation allocation algorithm. For our algorithm we introduce Nash Social Welfare Matching which makes a connection between Nash Social Welfare and envy freeness.
Fair and Efficient Allocations under Lexicographic Preferences
Authors: Hosseini Hadi , Sikdar Sujoy , Vaish Rohit , Xia Lirong
Published: September 2022

Envy-freeness up to any good (EFX) provides a strong and intuitive guarantee of fairness in the allocation of indivisible goods. But whether such allocations always exist or whether they can be efficiently computed remains an important open question. We study the existence and computation of EFX in conjunction with various other economic properties under lexicographic preferences--a well-studied preference restriction model in artificial intelligence and economics. In sharp contrast to the known results for additive valuations, we not only prove the existence of EFX and Pareto optimal allocations, but in fact provide an algorithmic characterization of these two properties. We also characterize the mechanisms that are, in addition, strategyproof, non-bossy, and neutral. When the efficiency notion is strengthened to rank-maximality, we obtain non-existence and computational hardness results, and show that tractability can be restored when EFX is relaxed to another well-studied fairness notion called maximin share guarantee (MMS).
If You Like Shapley Then You’ll Love the Core
Authors: Yan Tom , Procaccia Ariel D.
Published: September 2022

The prevalent approach to problems of credit assignment in machine learning -- such as feature and data valuation -- is to model the problem at hand as a cooperative game and apply the Shapley value. But cooperative game theory offers a rich menu of alternative solution concepts, which famously includes the core and its variants. Our goal is to challenge the machine learning community's current consensus around the Shapley value, and make a case for the core as a viable alternative. To that end, we prove that arbitrarily good approximations to the least core -- a core relaxation that is always feasible -- can be computed efficiently (but prove an impossibility for a more refined solution concept, the nucleolus). We also perform experiments that corroborate these theoretical results and shed light on settings where the least core may be preferable to the Shapley value.
MARTA: Leveraging Human Rationales for Explainable Text Classification
Authors: Arous Ines , Dolamic Ljiljana , Yang Jie , Bhardwaj Akansha , Cuccu Giuseppe , Cudré-Mauroux Philippe
Published: September 2022

Explainability is a key requirement for text classification in many application domains ranging from sentiment analysis to medical diagnosis or legal reviews. Existing methods often rely on "attention" mechanisms for explaining classification results by estimating the relative importance of input units. However, recent studies have shown that such mechanisms tend to mis-identify irrelevant input units in their explanation. In this work, we propose a hybrid human-AI approach that incorporates human rationales into attention-based text classification models to improve the explainability of classification results. Specifically, we ask workers to provide rationales for their annotation by selecting relevant pieces of text. We introduce MARTA, a Bayesian framework that jointly learns an attention-based model and the reliability of workers while injecting human rationales into model training. We derive a principled optimization algorithm based on variational inference with efficient updating rules for learning MARTA parameters. Extensive validation on real-world datasets shows that our framework significantly improves the state of the art both in terms of classification explainability and accuracy.
Contextual Conditional Reasoning
Authors: Casini Giovanni , Meyer Thomas , Varzinczak Ivan
Published: September 2022

We extend the expressivity of classical conditional reasoning by introducing context as a new parameter. The enriched conditional logic generalises the defeasible setting in the style of Kraus, Lehmann and Magidor, and allows for a more refined representation of an agent’s epistemic state, distinguishing, for example, between expectations and counterfactuals. In this paper we introduce the language for the enriched logic, and define an appropriate semantic framework for it. We analyse which properties generally associated with conditional reasoning are still satisfied by the new semantic framework, provide an appropriate representation result, and define an entailment relation based on Lehmann and Magidor’s notion of Rational Closure.
(Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs
Authors: Hwang Jena D. , Bhagavatula Chandra , Le Bras Ronan , Da Jeff , Sakaguchi Keisuke , Bosselut Antoine , Choi Yejin
Published: September 2022

Recent years have brought about a renewed interest in commonsense representation and reasoning in the field of natural language understanding. The development of new commonsense knowledge graphs (CSKG) has been central to these advances as their diverse facts can be used and referenced by machine learning models for tackling new and challenging tasks. At the same time, there remain questions about the quality and coverage of these resources due to the massive scale required to comprehensively encompass general commonsense knowledge.

In this work, we posit that manually constructed CSKGs will never achieve the coverage necessary to be applicable in all situations encountered by NLP agents. Therefore, we propose a new evaluation framework for testing the utility of KGs based on how effectively implicit knowledge representations can be learned from them.
 
With this new goal, we propose Atomic 2020, a new CSKG of general-purpose commonsense knowledge containing knowledge that is not readily available in pretrained language models. We evaluate its properties in comparison with other leading CSKGs, performing the first large-scale pairwise study of commonsense knowledge resources. Next, we show that Atomic 2020 is better suited for training knowledge models that can generate accurate, representative knowledge for new, unseen entities and events. Finally, through human evaluation, we show that the few-shot performance of GPT-3 (175B parameters), while impressive, remains ~12 absolute points lower than a BART-based knowledge model trained on Atomic 2020 despite using over 430x fewer parameters.
Decentralized Multi-Agent Linear Bandits with Safety Constraints
Authors: Amani Sanae , Thrampoulidis Christos
Published: September 2022

We study decentralized stochastic linear bandits, where a network of N agents acts cooperatively to efficiently solve a linear bandit-optimization problem over a d-dimensional space. For this problem, we propose DLUCB: a fully decentralized algorithm that minimizes the cumulative regret over the entire network. At each round of the algorithm each agent chooses its actions following an upper confidence bound (UCB) strategy and agents share information with their immediate neighbors through a carefully designed consensus procedure that repeats over cycles. Our analysis adjusts the duration of these communication cycles ensuring near-optimal regret performance O(d \log{NT}\sqrt{NT}) at a communication rate of O(dN^2) per round. The structure of the network affects the regret performance via a small additive term – coined the regret of delay – that depends on the spectral gap of the underlying graph. Notably, our results apply to arbitrary network topologies without a requirement for a dedicated agent acting as a server. In consideration of situations with high communication cost, we propose RC-DLUCB: a modification of DLUCB with rare communication among agents. The new algorithm trades off regret performance for a significantly reduced total communication cost of O(d^3N^5/2) over all T rounds. Finally, we show that our ideas extend naturally to the emerging, albeit more challenging, setting of safe bandits. For the recently studied problem of linear bandits with unknown linear safety constraints, we propose the first safe decentralized algorithm. Our study contributes towards applying bandit techniques in safety-critical distributed systems that repeatedly deal with unknown stochastic environments. We present numerical simulations for various network topologies that corroborate our theoretical findings.
On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory
Authors: Araujo Alexandre , Negrevergne Benjamin , Chevaleyre Yann , Atif Jamal
Published: September 2022

This paper tackles the problem of Lipschitz regularization of Convolutional Neural Networks. Lipschitz regularity is now established as a key property of modern deep learning with implications in training stability, generalization, robustness against adversarial examples, etc. However, computing the exact value of the Lipschitz constant of a neural network is known to be NP-hard. Recent attempts from the literature introduce upper bounds to approximate this constant that are either efficient but loose or accurate but computationally expensive. In this work, by leveraging the theory of Toeplitz matrices, we introduce a new upper bound for convolutional layers that is both tight and easy to compute. Based on this result we devise an algorithm to train Lipschitz regularized Convolutional Neural Networks.
Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently
Authors: Burkholz Rebekka , Quackenbush John
Published: September 2022

Cascade models are central to understanding, predicting, and controlling epidemic spreading and information propagation. Related optimization, including influence maximization, model parameter inference, or the development of vaccination strategies, relies heavily on sampling from a model. This is either inefficient or inaccurate. As alternative, we present an efficient message passing algorithm that computes the probability distribution of the cascade size for the Independent Cascade Model on weighted directed networks and generalizations. Our approach is exact on trees but can be applied to any network topology. It approximates locally tree-like networks well, scales to large networks, and can lead to surprisingly good performance on more dense networks, as we also exemplify on real world data.
Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications
Authors: Chang Haw-Shiuan , Agrawal Amol , McCallum Andrew
Published: September 2022

Most unsupervised NLP models represent each word with a single point or single region in semantic space, while the existing multi-sense word embeddings cannot represent longer word sequences like phrases or sentences. We propose a novel embedding method for a text sequence (a phrase or a sentence) where each sequence is represented by a distinct set of multi-mode codebook embeddings to capture different semantic facets of its meaning. The codebook embeddings can be viewed as the cluster centers which summarize the distribution of possibly co-occurring words in a pre-trained word embedding space. We introduce an end-to-end trainable neural model that directly predicts the set of cluster centers from the input text sequence during test time. Our experiments show that the per-sentence codebook embeddings significantly improve the performances in unsupervised sentence similarity and extractive summarization benchmarks. In phrase similarity experiments, we discover that the multi-facet embeddings provide an interpretable semantic representation but do not outperform the single-facet baseline.
Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning
Authors: Chen Chao , Li Dongsheng , Yan Junchi , Huang Hanchi , Yang Xiaokang
Published: September 2022

One-bit matrix completion is an important class of positive-unlabeled (PU) learning problems where the observations consist of only positive examples, e.g., in top-N recommender systems. For the first time, we show that 1-bit matrix completion can be formulated as the problem of recovering clean graph signals from noise-corrupted signals in hypergraphs. This makes it possible to enjoy recent advances in graph signal learning. Then, we propose the spectral graph matrix completion (SGMC) method, which can recover the underlying matrix in distributed systems by filtering the noisy data in the graph frequency domain. Meanwhile, it can provide micro- and macro-level explanations by following vertex-frequency analysis. To tackle the computational and memory issue of performing graph signal operations on large graphs, we construct a scalable Nystrom algorithm which can efficiently compute orthonormal eigenvectors. Furthermore, we also develop polynomial and sparse frequency filters to remedy the accuracy loss caused by the approximations. We demonstrate the effectiveness of our algorithms on top-N recommendation tasks, and the results on three large-scale real-world datasets show that SGMC can outperform state-of-the-art top-N recommendation algorithms in accuracy while only requiring a small fraction of training time compared to the baselines.
Deep Spiking Neural Network with Neural Oscillation and Spike-Phase Information
Authors: Chen Yi , Qu Hong , Zhang Malu , Wang Yuchen
Published: September 2022

Deep spiking neural network (DSNN) is a promising computational model towards artificial intelligence. It benefits from both the DNNs and SNNs through a hierarchy structure to extract multiple levels of abstraction and the event-driven computational manner to provide ultra-low-power neuromorphic implementation, respectively. However, how to efficiently train the DSNNs remains an open question because of the non-differentiable spike function that prevents the traditional back-propagation (BP) learning algorithm directly applied to DSNNs. Here, inspired by the findings from the biological neural networks, we address the above-mentioned problem by introducing neural oscillation and spike-phase information to DSNNs. Specifically, we propose an Oscillation Postsynaptic Potential (Os-PSP) and phase-locking active function, and further put forward a new spiking neuron model, namely Resonate Spiking Neuron (RSN). Based on the RSN, we propose a Spike-Level-Dependent Back-Propagation (SLDBP) learning algorithm for DSNNs. Experimental results show that the proposed learning algorithm resolves the problems caused by the incompatibility between the BP learning algorithm and SNNs, and achieves state-of-the-art performance in single spike-based learning algorithms. This work investigates the contribution of introducing biologically inspired mechanisms, such as neural oscillation and spike-phase information to DSNNs and providing a new perspective to design future DSNNs.
Cost-aware Graph Generation: A Deep Bayesian Optimization Approach
Authors: Cui Jiaxu , Yang Bo , Sun Bingyi , Liu Jiming
Published: September 2022

Graph-structured data is ubiquitous throughout the natural and social sciences, ranging from complex drug molecules to artificial neural networks. Evaluating their functional properties, e.g., drug effectiveness and prediction accuracy, is usually costly in terms of time, money, energy, or environment, becoming a bottleneck for the graph generation task. In this work, from the perspective of saving cost, we propose a novel Cost-Aware Graph Generation (CAGG) framework to generate graphs with optimal properties at as low cost as possible. By introducing a robust Bayesian graph neural network as the surrogate model and a goal-oriented training scheme for the generation model, the CAGG can approach the real expensive evaluation function and generate search space close to the optimal property, to avoid unnecessary evaluations. Intensive experiments conducted on two challenging real-world applications, including molecular discovery and neural architecture search, demonstrate its effectiveness and applicability. The results show that it can generate the optimal graphs and reduce the evaluation costs significantly compared to the state-of-the-art.
Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models
Authors: Dedieu Antoine , Lázaro-Gredilla Miguel , George Dileep
Published: September 2022

We consider the problem of learning the underlying graph of a sparse Ising model with p nodes from n i.i.d. samples. The most recent and best performing approaches combine an empirical loss (the logistic regression loss or the interaction screening loss) with a regularizer (an L1 penalty or an L1 constraint). This results in a convex problem that can be solved separately for each node of the graph.
In this work, we leverage the cardinality constraint L0 norm, which is known to properly induce sparsity, and further combine it with an L2 norm to better model the non-zero coefficients.
We show that our proposed estimators achieve an improved sample complexity, both (a) theoretically, by reaching new state-of-the-art upper bounds for recovery guarantees, and (b) empirically, by showing sharper phase transitions between poor and full recovery for graph topologies studied in the literature, when compared to their L1-based state-of-the-art methods.
Adversarial Training and Provable Robustness: A Tale of Two Objectives
Authors: Fan Jiameng , Li Wenchao
Published: September 2022

We propose a principled framework that combines adversarial training and provable robustness verification for training certifiably robust neural networks. We formulate the training problem as a joint optimization problem with both empirical and provable robustness objectives and develop a novel gradient-descent technique that can eliminate bias in stochastic multi-gradients. We perform both theoretical analysis on the convergence of the proposed technique and experimental comparison with state-of-the-arts. Results on MNIST and CIFAR-10 show that our method can consistently match or outperform prior approaches for provable l∞ robustness. Notably, we achieve 6.60% verified test error on MNIST at ε = 0.3, and 66.57% on CIFAR-10 with ε = 8/255.
Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability
Authors: Han Tao , Tu Wei-Wei , Li Yu-Feng
Published: September 2022

Unlabeled data exploitation and interpretability are usually both required in reality. They, however, are conducted independently, and very few works try to connect the two. For unlabeled data exploitation, state-of-the-art semi-supervised learning (SSL) results have been achieved via encouraging the consistency of model output on data perturbation, that is, consistency assumption. However, it remains hard for users to understand how particular decisions are made by state-of-the-art SSL models. To this end, in this paper we first disclose that the consistency assumption is closely related to causality invariance, where causality invariance lies in the main reason why the consistency assumption is valid. We then propose ECT (Explanation Consistency Training) which encourages a consistent reason of model decision under data perturbation. ECT employs model explanation as a surrogate of the causality of model output, which is able to bridge state-of-the-art interpretability to SSL models and alleviate the high complexity of causality. We realize ECT-SM for vision and ECT-ATT for NLP tasks. Experimental results on real-world data sets validate the highly competitive performance and better explanation of the proposed algorithms.
Graph Game Embedding
Authors: Hong Xiaobin , Zhang Tong , Cui Zhen , Huang Yuge , Shen Pengcheng , Li Shaoxin , Yang Jian
Published: September 2022

Graph embedding aims to encode nodes/edges into low-dimensional continuous features, and has become a crucial tool for graph analysis including graph/node classification, link prediction, etc. In this paper we propose a novel graph learning framework, named graph game embedding, to learn discriminative node representation as well as encode graph structures. Inspired by the spirit of game learning, node embedding is converted to the selection/searching process of player strategies, where each node corresponds to one player and each edge corresponds to the interaction of two players. Then, a utility function, which theoretically satisfies the Nash Equilibrium, is defined to measure the benefit/loss of players during graph evolution. Furthermore, a collaboration and competition mechanism is introduced to increase the discriminant learning ability. Under this graph game embedding framework, considering different interaction manners of nodes, we propose two specific models, named paired game embedding for paired nodes and group game embedding for group interaction. Comparing with existing graph embedding methods, our algorithm possesses two advantages: (1) the designed utility function ensures the stable graph evolution with theoretical convergence and Nash Equilibrium satisfaction; (2) the introduced collaboration and competition mechanism endows the graph game embedding framework with discriminative feature leaning ability by guiding each node to learn an optimal strategy distinguished from others. We test the proposed method on three public datasets about citation networks, and the experimental results verify the effectiveness of our method.
Exploration via State influence Modeling
Authors: Kang Yongxin , Zhao Enmin , Li Kai , Xing Junliang
Published: September 2022

This paper studies the challenging problem of reinforcement learning (RL) in hard exploration tasks with sparse rewards. It focuses on the exploration stage before the agent gets the first positive reward, in which case, traditional RL algorithms
with simple exploration strategies often work poorly. Unlike previous methods using some attribute of a single state as the intrinsic reward to encourage exploration, this work leverages the social influence between different states to permit more efficient exploration. It introduces a general intrinsic reward construction method to evaluate the social influence of states dynamically. Three kinds of social influence are introduced for a state: conformity, power, and authority. By measuring the state’s social influence, agents quickly find the focus state during the exploration process. The proposed RL framework with state social influence evaluation works well in hard exploration task. Extensive experimental analyses and comparisons in Grid Maze and many hard exploration Atari 2600 games demonstrate its high exploration efficiency.
Learning Intact Features by Erasing-Inpainting for Few-shot Classification
Authors: Li Junjie , Wang Zilei , Hu Xiaoming
Published: September 2022

Few-shot classification aims to categorize the samples from unseen classes with only few labeled samples. To address such a challenge, many methods exploit a base set consisting of massive labeled samples to learn an instance embedding function, i.e., image feature extractor, and it is expected to possess good transferability among different tasks. Such characteristics of few-shot learning are essentially different from that of traditional image classification only pursuing to get discriminative image representations. In this paper, we propose to learn intact features by erasing-inpainting for few-shot classification. Specifically,
we argue that extracting intact features of target objects is more transferable, and then propose a novel cross-set erasing-inpainting (CSEI) method. CSEI processes the images in the support set using erasing and inpainting, and then uses them to augment the query set of the same task. Consequently, the feature embedding produced by our proposed method can contain more complete information of target objects. In addition, we propose task-specific feature modulation to make the features adaptive to the current task. The extensive experiments on two widely used benchmarks well demonstrates the effectiveness of our proposed method, which can consistently get considerable performance gains for different baseline methods.
Multi- and interdisciplinary design of urban infrastructure development
Authors: Hooimeijer F.L. , Bricker J.D. , Pel A.J. , Brand A.D. , Van de Ven F.H.M. , Askarinejad A.
Published: September 2022

Due to the environmental crisis, there is a need for a more conscious and integrating design process within the field of urban infrastructure development. Through cooperation between civil engineering and spatial design resilience of the built environment can be increased. Delft University of Technology investigates interdisciplinary design as a method and incorporates this into its MSc-level education of students in the faculties of civil engineering and architecture. The focus of the research was on the reconstruction projects after disasters like hurricanes and tsunamis. By way of surveys of the participating students, the effectiveness of the interdisciplinary design methods used, and the interpretation of the terms multidisciplinary and interdisciplinary are revealed. From survey results about understanding of multidisciplinary and interdisciplinary it can be concluded that interdisciplinary design should entail a conscious and orchestrated process in which the disciplines present their ideas within a shared value system before systematic integration. The challenges are at personal and cognitive levels, an open attitude is necessary to be able to perceive and react, process and understand, retrieve information. Only then decisions on – and production of – appropriate responses come out of co-creation between engineering within the spatial design process.
Determination of optimal technological modes for obtaining manganese alloys from poor carbonate and oxide ores in a jet-emulsion unit
Authors: Rybenko I. A. , Kongoli F.
Published: September 2022

The paper presents the results of development and research of a new resource-saving technology for processing poor oxide and carbonate manganese ores in a unit of jet-emulsion type. The basic principles of creating a jet unit and the technology of processing pulverized manganese ores are considered. For the preliminary reduction of manganese from higher oxides or carbonates decomposition, as well as the removal of moisture from the ore, it is proposed to use a reducing gas, which is a product of the implementation of manganese reduction technology in jet-emulsion unit. Thus, the authors propose to close the process, that is, to create a consistent flow of substance and energy passing through the main jet-emulsion unit and the preparatory unit of the fluidized bed. The main task of calculating the proposed technology is to determine the consumption of crude ore in the fluidized bed unit in order to obtain a given yield of the intermediate product and at the same time to ensure the possibility of complete conversion of high oxides or carbonates of crude ore into lower oxides by reducing gas produced in the main unit. To solve this problem, an optimization task was set and implemented. The first stage is selection of composition and consumption of the reducing gas and determination of consumption of the initial manganese ore, which provides the output of a given amount of the intermediate product. The second stage is solution of the optimization problem for output and composition of the gas, which should ensure the recovery process in the second unit. The paper presents the results of calculating the processing technology in the jet-emulsion unit for oxide ore of the Selezen’skoe and carbonate ore of the Usinskoe deposits. A comparative analysis of two options for processing manganese ores by the proposed technology and by the technology without preliminary recovery and roasting was carried out. The proposed technology of processing manganese ores in a closed-cycle jet-emulsion unit allows one to significantly reduce the specific costs of materials, increase productivity, and significantly reduce the energy intensity of the process compared to the technology of processing poor manganese ores without preliminary reduction or roasting.
Assessment of clinical utility and predictive potential of pre-chemotherapy soluble urokinase plasminogen activator receptor – observational single center study
Authors: Beleva Elina , Stoencheva Snezhana , Deneva Tanya , Nenova Ivanka , Grudeva-Popova Zhanet
Published: September 2022

Alteration of urokinase plasminogen activator receptor (uPAR) in neoplasms is a prerequisite for invasiveness and metastatic ability. In the present study, we aimed to evaluate the relationship of pre-chemotherapy soluble uPAR (suPAR) with the odds for metastasis, lack of disease control, and its predictive ability for progression-free survival (PFS). Baseline plasma suPAR levels were measured by ELISA in 89 patients with various cancers prior to initiation of systemic treatment. Patients were followed prospectively until metastatic progression or death. TCGA Pan-Cancer dataset was mined for available RNAseq expression data of the PLAUR gene in patients with breast, colon, and lung cancer, and therelevant genomic and clinical data were extracted for further analysis. Pre-chemotherapy suPAR levels were significantly associated with white blood cell counts and fibrinogen and were significantly elevated both in patients with metastatic disease and in patients with progression. Increasing suPAR was significantly associated with odds for progression in the prespecified multivariate analysis (odds ratio 2.47, 95% confidence interval 1.3 – 5.11). In univariate Cox regression, suPAR was predictive of shortened progression-free survival (PFS) (hazard ratio 1.065, 95% confidence interval 1.002 – 1.13; p = 0.041). There was a trend towards shortened PFS in patients with higher baseline suPAR levels (cutoff 8.1 ng/mL). In the TCGA lung cancer cohort, PLAUR mRNA expression was significantly associated with shortened PFS in both univariate and multivariate analyses. High PLAUR gene expression conferred significant survival disadvantage only in patients with colon and lung cancer. SuPAR may bear predictive potential for adverse outcomes in cancer, but its utility as a biomarker seems to be more pronounced in cancers with associated inflammatory state.
¿Lejano Oriente como arma para la revolución? Reflexiones sobre el papel de la filosofía oriental en la obra de Byung-Chul Han
Authors: Almeyda Sarmiento Juan David , Botero Bernal Andrés , Aguirre Román Javier Orlando
Published: September 2022

Este escrito tiene por objeto exponer la función de la cultura y el pensamiento del Lejano Oriente, expresado en el budismo zen japonés y chan chino (así como, en general, en la filosofía y cultura chinas), dentro del pensamiento de Byung-Chul Han. Esto último, en tanto que esta dimensión de la obra del coreano ha sido poco profundizada en los análisis que se hacen de él. Este error lleva a equívocos como el afirmar que no existe una propuesta de emancipación en Han o que su trabajo se limita a una descripción pesimista del mundo. Así, la investigación, que parte del método bibliográfico-documental, se divide en tres momentos; el primero, que expone las bases de la filosofía del budismo zen-chan desarrolladas por Han; el segundo, que delimita la ontología de la falsificación haniana; y, el tercero, que analiza las tesis sobre Oriente al interior del proyecto teórico de Han.
Relaciones pedagógicas entre la prensa y la biblioteca escolar: programa Prensa Escuela 25 años
Authors: Bedoya Mazo Sandra Patricia
Published: September 2022

El presente artículo da cuenta del estudio denominado “El lugar de la prensa en la biblioteca escolar: programa Prensa Escuela 25 años”, específicamente, en lo relativo al análisis de las relaciones pedagógicas que se tejen entre la prensa, el desarrollo curricular y la biblioteca escolar. Para ello, se sistematizó la experiencia del programa en seis instituciones educativas del Área Metropolitana de Medellín, Colombia, a partir de las voces de los participantes, mediante entrevistas y grupos focales con profesores, estudiantes y bibliotecarios; también se analizó documentación generada por el programa. Las categorías establecidas constituyeron la referencia para el análisis y la triangulación de la información. Entre los hallazgos se evidenció que el principal beneficio del uso pedagógico de la prensa fue su articulación al desarrollo curricular y la consecuente integración entre las áreas académicas. Por otro lado, los estudiantes desarrollaron la capacidad para identificar y leer contenidos de las diversas tipologías textuales; en algunos casos, se fortalecieron los procesos de expresión y argumentación oral con apoyo de las colecciones bibliotecarias, lo cual favorecía las relaciones entre las bibliotecas y sus usuarios. Es de resaltar que algunos sujetos educativos interpretaron el valor de la información en sus actividades académicas y personales.
MEKANSAL PİRAMİT HAVUZLAMA TABANLI EVRİŞİMLİ SİNİR AĞI İLE OTOMATİK DRONE SINIFLANDIRMA
Authors: KORKMAZ Deniz , AÇIKGÖZ Hakan
Published: September 2022

Hava sahalarının önemli olduğu bölgelerde dronları tespit etmek zorlu bir konu haline gelmiştir. Bu araçların kontrolsüz uçuşları ve konuşlanmaları da istenmeyen bölgelerde çeşitli güvenlik sorunlarına sebep olur. Bu çalışmada, dronları kuşlardan ayırarak etkili bir şekilde sınıflandırabilmek için bir evrişimli sinir ağı (ESA) modeli önerilmiştir. Önerilen model, ön eğitimli AlexNet ile mekansal piramit havuzlama (MPH) yapısı kullanılarak tasarlanmıştır. Böylece, ağın evrişimsel katmanlarından gelen yerel öznitelikler birleştirerek ağın nesne özelliklerini daha kapsamlı bir şekilde öğrenmesi sağlanmış ve önerilen modelin sınıflandırma performansı artırılmıştır. Ayrıca, eğitim görüntülerinde çevrimdışı veri artırma tekniği uygulanarak örnek sayısı artırılmıştır. Önerilen yöntemin performansı AlexNet, ShuffleNet, GoogLeNet ve DarkNet gibi sıklıkla kullanılan ön eğitimli ESA mimarileri ile karşılaştırılmıştır. Gerçekleştirilen deneysel çalışmalarda önerilen yöntemin doğruluk, kesinlik, duyarlılık, özgüllük ve F1-skor değerleri sırasıyla %98.89, %97.83, %100, %97.78 ve %98.90 olarak elde edilmiştir. Çalışmada elde edilen tüm sonuçlar incelendiğinde, önerilen yöntemin farklı ortamlara ait drone görüntülerini kuşlardan ayırarak başarımı yüksek bir şekilde sınıflayabildiğini ortaya koymaktadır.
DC MOTORA UYGULANAN LQR KONTROLCÜ İÇİN AĞIRLIK MATRİSLERİNİN NSGA-II TABANLI ÇOK AMAÇLI OPTİMİZASYONU
Authors: UYGUR Ali Fazıl
Published: September 2022

LQR kontrol yaklaşımıyla, Lineer Zamanla Değişmeyen bir sistemin kararlılığının temin edilmesi yanında, sisteme uygulanacak geri besleme kazancına, karesel bir performans ölçütünün minimizasyonu yoluyla ulaşılıyor olması, bu kontrol yaklaşımını, kapalı çevrim sistemden beklenen performansı şekillendirme kabiliyetine sahip kılmaktadır. Bu noktada, minimize edilecek karesel performans ölçütünün ya da maliyet fonksiyonunun, içerdiği ağırlık matrisleri üzerinden, amaca uygun biçimde oluşturulması kontrolcü performansı bakımından önem arz etmektedir. O yüzden, LQR kontrolcünün tasarımında kullanılan ağırlık matrislerinin optimizasyonu, kontrolcüden beklenen çoklu performans amaçları doğrultusunda gerçekleştirilmelidir. Bu durum çok amaçlı bir optimizasyon problemini doğurmaktadır. Karesel maliyet fonksiyonunun ağırlık matrisleri, optimizasyon yapılmaksızın, deneme yanılma, kutup atama gibi klasik yöntemler yardımıyla ayarlanabilir olsa da bu durum yorucu ve zaman alıcı olabilmektedir. Bu zorluğun üstesinden gelebilmek adına, çeşitli çok amaçlı optimizasyon tekniklerinden istifade edilmesi mümkündür. Yapılan bu çalışma kapsamında, LQR tabanlı optimal DC motor kontrolü amaçlanmıştır. LQR kontrolcü için söz konusu karesel maliyet fonksiyonuna dair Q ve R ağırlık matrisi parametrelerinin ayarlanması gerekmektedir. Çok amaçlı optimizasyon tekniklerinden biri olan ve Non-Dominated Sorted Genetic Algorithm (NSGA-II) olarak bilinen optimizasyon algoritması yardımıyla ilgili parametreler ayarlanmıştır. Elde edilen optimum parametreler kullanılarak sentezlenen LQR kontrolcünün sistem üzerindeki performans bulguları, simülasyon sonuçları ile sunulmuştur.