bluuebunny/crossref_metadata_2025 huggingface dataset:
Stage Conscious Attention Network (SCAN): A Demonstration-Conditioned Policy for Few-Shot Imitation
Authors: Yeh Jia-Fong , Chung Chi-Ming , Su Hung-Ting , Chen Yi-Ting , Hsu Winston H.
Published: July 2022

In few-shot imitation learning (FSIL), using behavioral cloning (BC) to solve unseen tasks with few expert demonstrations becomes a popular research direction. The following capabilities are essential in robotics applications: (1) Behaving in compound tasks that contain multiple stages. (2) Retrieving knowledge from few length-variant and misalignment demonstrations. (3) Learning from an expert different from the agent. No previous work can achieve these abilities at the same time. In this work, we conduct FSIL problem under the union of above settings and introduce a novel stage conscious attention network (SCAN) to retrieve knowledge from few demonstrations simultaneously. SCAN uses an attention module to identify each stage in length-variant demonstrations. Moreover, it is designed under demonstration-conditioned policy that learns the relationship between experts and agents. Experiment results show that SCAN can perform in complicated compound tasks without fine-tuning and provide the explainable visualization. Project page is at https://sites.google.com/view/scan-aaai2022.
AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators
Authors: Yin Yihang , Wang Qingzhong , Huang Siyu , Xiong Haoyi , Zhang Xiang
Published: July 2022

Contrastive learning has been widely applied to graph representation learning, where the view generators play a vital role in generating effective contrastive samples. Most of the existing contrastive learning methods employ pre-defined view generation methods, e.g., node drop or edge perturbation, which usually cannot adapt to input data or preserve the original semantic structures well. To address this issue, we propose a novel framework named Automated Graph Contrastive Learning (AutoGCL) in this paper. Specifically, AutoGCL employs a set of learnable graph view generators orchestrated by an auto augmentation strategy, where every graph view generator learns a probability distribution of graphs conditioned by the input. While the graph view generators in AutoGCL preserve the most representative structures of the original graph in generation of every contrastive sample, the auto augmentation learns policies to introduce adequate augmentation variances in the whole contrastive learning procedure. Furthermore, AutoGCL adopts a joint training strategy to train the learnable view generators, the graph encoder, and the classifier in an end-to-end manner, resulting in topological heterogeneity yet semantic similarity in the generation of contrastive samples. Extensive experiments on semi-supervised learning, unsupervised learning, and transfer learning demonstrate the superiority of our AutoGCL framework over the state-of-the-arts in graph contrastive learning. In addition, the visualization results further confirm that the learnable view generators can deliver more compact and semantically meaningful contrastive samples compared against the existing view generation methods. Our code is available at https://github.com/Somedaywilldo/AutoGCL.
BM-NAS: Bilevel Multimodal Neural Architecture Search
Authors: Yin Yihang , Huang Siyu , Zhang Xiang
Published: July 2022

Deep neural networks (DNNs) have shown superior performances on various multimodal learning problems. However, it often requires huge efforts to adapt DNNs to individual multimodal tasks by manually engineering unimodal features and designing multimodal feature fusion strategies. This paper proposes Bilevel Multimodal Neural Architecture Search (BM-NAS) framework, which makes the architecture of multimodal fusion models fully searchable via a bilevel searching scheme. At the upper level, BM-NAS selects the inter/intra-modal feature pairs from the pretrained unimodal backbones. At the lower level, BM-NAS learns the fusion strategy for each feature pair, which is a combination of predefined primitive operations. The primitive operations are elaborately designed and they can be flexibly combined to accommodate various effective feature fusion modules such as multi-head attention (Transformer) and Attention on Attention (AoA). Experimental results on three multimodal tasks demonstrate the effectiveness and efficiency of the proposed BM-NAS framework. BM-NAS achieves competitive performances with much less search time and fewer model parameters in comparison with the existing generalized multimodal NAS methods. Our code is available at https://github.com/Somedaywilldo/BM-NAS.
Hindsight Network Credit Assignment: Efficient Credit Assignment in Networks of Discrete Stochastic Units
Authors: Young Kenny
Published: July 2022

Training neural networks with discrete stochastic variables presents a unique challenge. Backpropagation is not directly applicable, nor are the reparameterization tricks used in networks with continuous stochastic variables. To address this challenge, we present Hindsight Network Credit Assignment (HNCA), a novel gradient estimation algorithm for networks of discrete stochastic units. HNCA works by assigning credit to each unit based on the degree to which its output influences its immediate children in the network. We prove that HNCA produces unbiased gradient estimates with reduced variance compared to the REINFORCE estimator, while the computational cost is similar to that of backpropagation. We first apply HNCA in a contextual bandit setting to optimize a reward function that is unknown to the agent. In this setting, we empirically demonstrate that HNCA significantly outperforms REINFORCE, indicating that the variance reduction implied by our theoretical analysis is significant and impactful. We then show how HNCA can be extended to optimize a more general function of the outputs of a network of stochastic units, where the function is known to the agent. We apply this extended version of HNCA to train a discrete variational auto-encoder and empirically show it compares favourably to other strong methods. We believe that the ideas underlying HNCA can help stimulate new ways of thinking about efficient credit assignment in stochastic compute graphs.
Tailor Versatile Multi-Modal Learning for Multi-Label Emotion Recognition
Authors: Zhang Yi , Chen Mingyuan , Shen Jundong , Wang Chongjun
Published: July 2022

Multi-modal Multi-label Emotion Recognition (MMER) aims to identify various human emotions from heterogeneous visual, audio and text modalities. Previous methods mainly focus on projecting multiple modalities into a common latent space and learning an identical representation for all labels, which neglects the diversity of each modality and fails to capture richer semantic information for each label from different perspectives. Besides, associated relationships of modalities and labels have not been fully exploited. In this paper, we propose versaTile multi-modAl learning for multI-labeL emOtion Recognition (TAILOR), aiming to refine multi-modal representations and enhance discriminative capacity of each label. Specifically, we design an adversarial multi-modal refinement module to sufficiently explore the commonality among different modalities and strengthen the diversity of each modality. To further exploit label-modal dependence, we devise a BERT-like cross-modal encoder to gradually fuse private and common modality representations in a granularity descent way, as well as a label-guided decoder to adaptively generate a tailored representation for each label with the guidance of label semantics. In addition, we conduct experiments on the benchmark MMER dataset CMU-MOSEI in both aligned and unaligned settings, which demonstrate the superiority of TAILOR over the state-of-the-arts.
Robust Action Gap Increasing with Clipped Advantage Learning
Authors: Zhang Zhe , Gan Yaozhong , Tan Xiaoyang
Published: July 2022

Advantage Learning (AL) seeks to increase the action gap between the optimal action and its competitors, so as to improve the robustness to estimation errors. However, the method becomes problematic when the optimal action induced by the approximated value function does not agree with the true optimal action. In this paper, we present a novel method, named clipped Advantage Learning (clipped AL), to address this issue. The method is inspired by our observation that increasing the action gap blindly for all given samples while not taking their necessities into account could accumulate more errors in the performance loss bound, leading to a slow value convergence, and to avoid that, we should adjust the advantage value adaptively. We show that our simple clipped AL operator not only enjoys fast convergence guarantee but also retains proper action gaps, hence achieving a good balance between the large action gap and the fast convergence. The feasibility and effectiveness of the proposed method are verified empirically on several RL benchmarks with promising performance.
Error-Based Knockoffs Inference for Controlled Feature Selection
Authors: Zhao Xuebin , Chen Hong , Wang Yingjie , Li Weifu , Gong Tieliang , Wang Yulong , Zheng Feng
Published: July 2022

Recently, the scheme of model-X knockoffs was proposed as a promising solution to address controlled feature selection under high-dimensional finite-sample settings. However, the procedure of model-X knockoffs depends heavily on the coefficient-based feature importance and only concerns the control of false discovery rate (FDR). To further improve its adaptivity and flexibility, in this paper, we propose an error-based knockoff inference method by integrating the knockoff features, the error-based feature importance statistics, and the stepdown procedure together. The proposed inference procedure does not require specifying a regression model and can handle feature selection with theoretical guarantees on controlling false discovery proportion (FDP), FDR, or k-familywise error rate (k-FWER). Empirical evaluations demonstrate the competitive performance of our approach on both simulated and real data.
Participatory Budgeting with Donations and Diversity Constraints
Authors: Chen Jiehua , Lackner Martin , Maly Jan
Published: July 2022

Participatory budgeting (PB) is a democratic process where citizens jointly decide on how to allocate public funds to indivisible projects. In this work, we focus on PB processes where citizens may provide additional money to projects they want to see funded. We introduce a formal framework for this kind of PB with donations. Our framework also allows for diversity constraints, meaning that each project belongs to one or more types, and there are lower and upper bounds on the number of projects of the same type that can be funded. We propose three general classes of methods for aggregating the citizens’ preferences in the presence of donations and analyze their axiomatic properties. Furthermore, we investigate the computational complexity of determining the outcome of a PB process with donations and of finding a citizen’s optimal donation strategy.
When Can the Defender Effectively Deceive Attackers in Security Games?
Authors: Nguyen Thanh , Xu Haifeng
Published: July 2022

This paper studies defender patrol deception in general Stackelberg security games (SSGs), where a defender attempts to alter the attacker's perception of the defender's patrolling intensity so as to influence the attacker's decision making. We are interested in understanding the complexity and effectiveness of optimal defender deception under different attacker behavior models. Specifically, we consider three different attacker strategies of response (to the defender's deception) with increasing sophistication, and design efficient polynomial-time algorithms to compute the equilibrium for each. Moreover, we prove formal separation for the effectiveness of patrol deception when facing an attacker of increasing sophistication, until it becomes even harmful to the defender when facing the most intelligent attacker we consider. Our results shed light on when and how deception should be used in SSGs. We conduct extensive experiments to illustrate our theoretical results in various game settings.
Decentralized Mean Field Games
Authors: Subramanian Sriram Ganapathi , Taylor Matthew E. , Crowley Mark , Poupart Pascal
Published: July 2022

Multiagent reinforcement learning algorithms have not been widely adopted in large scale environments with many agents as they often scale poorly with the number of agents. Using mean field theory to aggregate agents has been proposed as a solution to this problem. However, almost all previous methods in this area make a strong assumption of a centralized system where all the agents in the environment learn the same policy and are effectively indistinguishable from each other. In this paper, we relax this assumption about indistinguishable agents and propose a new mean field system known as Decentralized Mean Field Games, where each agent can be quite different from others. All agents learn independent policies in a decentralized fashion, based on their local observations. We define a theoretical solution concept for this system and provide a fixed point guarantee for a Q-learning based algorithm in this system. A practical consequence of our approach is that we can address a `chicken-and-egg' problem in empirical mean field reinforcement learning algorithms. Further, we provide Q-learning and actor-critic algorithms that use the decentralized mean field learning approach and give stronger performances compared to common baselines in this area. In our setting, agents do not need to be clones of each other and learn in a fully decentralized fashion. Hence, for the first time, we show the application of mean field learning methods in fully competitive environments, large-scale continuous action space environments, and other environments with heterogeneous agents. Importantly, we also apply the mean field method in a ride-sharing problem using a real-world dataset. We propose a decentralized solution to this problem, which is more practical than existing centralized training methods.
Multi-Agent Incentive Communication via Decentralized Teammate Modeling
Authors: Yuan Lei , Wang Jianhao , Zhang Fuxiang , Wang Chenghe , Zhang ZongZhang , Yu Yang , Zhang Chongjie
Published: July 2022

Effective communication can improve coordination in cooperative multi-agent reinforcement learning (MARL). One popular communication scheme is exchanging agents' local observations or latent embeddings and using them to augment individual local policy input. Such a communication paradigm can reduce uncertainty for local decision-making and induce implicit coordination. However, it enlarges agents' local policy spaces and increases learning complexity, leading to poor coordination in complex settings. To handle this limitation, this paper proposes a novel framework named Multi-Agent Incentive Communication (MAIC) that allows each agent to learn to generate incentive messages and bias other agents' value functions directly, resulting in effective explicit coordination. Our method firstly learns targeted teammate models, with which each agent can anticipate the teammate's action selection and generate tailored messages to specific agents. We further introduce a novel regularization to leverage interaction sparsity and improve communication efficiency. MAIC is agnostic to specific MARL algorithms and can be flexibly integrated with different value function factorization methods. Empirical results demonstrate that our method significantly outperforms baselines and achieves excellent performance on multiple cooperative MARL tasks.
Incorporating Item Frequency for Differentially Private Set Union
Authors: Carvalho Ricardo Silva , Wang Ke , Gondara Lovedeep Singh
Published: July 2022

We study the problem of releasing the set union of users' items subject to differential privacy. Previous approaches consider only the set of items for each user as the input. We propose incorporating the item frequency, which is typically available in set union problems, to boost the utility of private mechanisms. However, using the global item frequency over all users would largely increase privacy loss. We propose to use the local item frequency of each user to approximate the global item frequency without incurring additional privacy loss.
Local item frequency allows us to design greedy set union mechanisms that are differentially private, which is impossible for previous greedy proposals. Moreover, while all previous works have to use uniform sampling to limit the number of items each user would contribute to, our construction eliminates the sampling step completely and allows our mechanisms to consider all of the users' items.
Finally, we propose to transfer the knowledge of the global item frequency from a public dataset into our mechanism, which further boosts utility even when the public and private datasets are from different domains. We evaluate the proposed methods on multiple real-life datasets.
Shaping Noise for Robust Attributions in Neural Stochastic Differential Equations
Authors: Jha Sumit Kumar , Ewetz Rickard , Velasquez Alvaro , Ramanathan Arvind , Jha Susmit
Published: July 2022

Neural SDEs with Brownian motion as noise lead to smoother attributions than traditional ResNets. Various attribution methods such as saliency maps, integrated gradients, DeepSHAP and DeepLIFT have been shown to be more robust for neural SDEs than for ResNets using the recently proposed sensitivity metric. In this paper, we show that neural SDEs with adaptive attribution-driven noise lead to even more robust attributions and smaller sensitivity metrics than traditional neural SDEs with Brownian motion as noise. In particular, attribution-driven shaping of noise leads to 6.7%, 6.9% and 19.4% smaller sensitivity metric for integrated gradients computed on three discrete approximations of neural SDEs with standard Brownian motion noise: stochastic ResNet-50, WideResNet-101 and ResNeXt-101 models respectively. The neural SDE model with adaptive attribution-driven noise leads to 25.7% and 4.8% improvement in the SIC metric over traditional ResNets and Neural SDEs with Brownian motion as noise. To the best of our knowledge, we are the first to propose the use of attributions for shaping the noise injected in neural SDEs, and demonstrate that this process leads to more robust attributions than traditional neural SDEs with standard Brownian motion as noise.
Do Feature Attribution Methods Correctly Attribute Features?
Authors: Zhou Yilun , Booth Serena , Ribeiro Marco Tulio , Shah Julie
Published: July 2022

Feature attribution methods are popular in interpretable machine learning. These methods compute the attribution of each input feature to represent its importance, but there is no consensus on the definition of "attribution", leading to many competing methods with little systematic evaluation, complicated in particular by the lack of ground truth attribution. To address this, we propose a dataset modification procedure to induce such ground truth. Using this procedure, we evaluate three common methods: saliency maps, rationales, and attentions. We identify several deficiencies and add new perspectives to the growing body of evidence questioning the correctness and reliability of these methods applied on datasets in the wild. We further discuss possible avenues for remedy and recommend new attribution methods to be tested against ground truth before deployment. The code and appendix are available at https://yilunzhou.github.io/feature-attribution-evaluation/.
Learning to Solve Routing Problems via Distributionally Robust Optimization
Authors: Jiang Yuan , Wu Yaoxin , Cao Zhiguang , Zhang Jie
Published: July 2022

Recent deep models for solving routing problems always assume a single distribution of nodes for training, which severely impairs their cross-distribution generalization ability. In this paper, we exploit group distributionally robust optimization (group DRO) to tackle this issue, where we jointly optimize the weights for different groups of distributions and the parameters for the deep model in an interleaved manner during training. We also design a module based on convolutional neural network, which allows the deep model to learn more informative latent pattern among the nodes. We evaluate the proposed approach on two types of well-known deep models including GCN and POMO. The experimental results on the randomly synthesized instances and the ones from two benchmark dataset (i.e., TSPLib and CVRPLib) demonstrate that our approach could significantly improve the cross-distribution generalization performance over the original models.
Learning Probably Approximately Complete and Safe Action Models for Stochastic Worlds
Authors: Juba Brendan , Stern Roni
Published: July 2022

We consider the problem of learning action models for planning in unknown stochastic environments that can be defined using the Probabilistic Planning Domain Description Language (PPDDL). As input, we are given a set of previously executed trajectories, and the main challenge is to learn an action model that has a similar goal achievement probability to the policies used to create these trajectories. To this end, we introduce a variant of PPDDL in which there is uncertainty about the transition probabilities, specified by an interval for each factor that contains the respective true transition probabilities. Then, we present SAM+, an algorithm that learns such an imprecise-PPDDL environment model. SAM+ has a polynomial time and sample complexity, and guarantees that with high probability, the true environment is indeed captured by the defined intervals. We prove that the action model SAM+ outputs has a goal achievement probability that is almost as good or better than that of the policies used to produced the training trajectories. Then, we show how to produce a PPDDL model based on this imprecise-PPDDL model that has similar properties.
Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate
Authors: Mi Lu , Wang Hao , Tian Yonglong , He Hao , Shavit Nir N
Published: July 2022

Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas. However, most state-of-the-art deep learning models either fail to obtain uncertainty estimation or need significant modification (e.g., formulating a proper Bayesian treatment) to obtain it. Most previous methods are not able to take an arbitrary model off the shelf and generate uncertainty estimation without retraining or redesigning it. To address this gap, we perform a systematic exploration into training-free uncertainty estimation for dense regression, an unrecognized yet important problem, and provide a theoretical construction justifying such estimations. We propose three simple and scalable methods to analyze the variance of outputs from a trained network under tolerable perturbations: infer-transformation, infer-noise, and infer-dropout. They operate solely during the inference, without the need to re-train, re-design, or fine-tune the models, as typically required by state-of-the-art uncertainty estimation methods. Surprisingly, even without involving such perturbations in training, our methods produce comparable or even better uncertainty estimation when compared to training-required state-of-the-art methods. Code is available at https://github.com/lumi9587/train-free-uncertainty.
Towards Robust Off-Policy Learning for Runtime Uncertainty
Authors: Xu Da , Ye Yuting , Ruan Chuanwei , Yang Bo
Published: July 2022

Off-policy learning plays a pivotal role in optimizing and evaluating policies prior to the online deployment. However, during the real-time serving, we observe varieties of interventions and constraints that cause inconsistency between the online and offline setting, which we summarize and term as runtime uncertainty. Such uncertainty cannot be learned from the logged data due to its abnormality and rareness nature. To assert a certain level of robustness, we perturb the off-policy estimators along an adversarial direction in view of the runtime uncertainty. It allows the resulting estimators to be robust not only to observed but also unexpected runtime uncertainties. Leveraging this idea, we bring runtime-uncertainty robustness to three major off-policy learning methods: the inverse propensity score method, reward-model method, and doubly robust method. We theoretically justify the robustness of our methods to runtime uncertainty, and demonstrate their effectiveness using both the simulation and the real-world online experiments.
Efficient Optimal Transport Algorithm by Accelerated Gradient Descent
Authors: An Dongsheng , Lei Na , Xu Xiaoyin , Gu Xianfeng
Published: July 2022

Optimal transport (OT) plays an essential role in various areas like machine learning and deep learning.
However, computing discrete optimal transport plan for large scale problems with adequate accuracy and efficiency is still highly challenging.
Recently, methods based on the Sinkhorn algorithm add an entropy regularizer to the prime problem and get a trade off between efficiency and accuracy.
In this paper, we propose a novel algorithm to further improve the efficiency and accuracy based on Nesterov's smoothing technique.
Basically, the non-smooth c-transform of the Kantorovich potential is approximated by the smooth Log-Sum-Exp function, which finally smooths the original non-smooth Kantorovich dual functional. The smooth Kantorovich functional can be optimized by the fast proximal gradient algorithm (FISTA) efficiently. Theoretically, the computational complexity of the proposed method is lower than
current estimation of the Sinkhorn algorithm in terms of the precision.
Empirically, compared with the Sinkhorn algorithm, our experimental results demonstrate that the proposed method achieves faster convergence and better accuracy with the same parameter.
An Exact Algorithm with New Upper Bounds for the Maximum k-Defective Clique Problem in Massive Sparse Graphs
Authors: Gao Jian , Xu Zhenghang , Li Ruizhi , Yin Minghao
Published: July 2022

The Maximum k-Defective Clique Problem (MDCP), as a clique relaxation model, has been used to solve various problems. Because it is a hard computational task, previous works can hardly solve the MDCP for massive sparse graphs derived from real-world applications. In this work, we propose a novel branch-and-bound algorithm to solve the MDCP based on several new techniques. First, we propose two new upper bounds of the MDCP as well as corresponding reduction rules to remove redundant vertices and edges. The proposed reduction rules are particularly useful for massive graphs. Second, we present another new upper bound by counting missing edges between fixed vertices and an unfixed vertex for cutting branches. We perform extensive computational experiments to evaluate our algorithm. Experimental results show that our reduction rules are very effective for removing redundant vertices and edges so that graphs are reduced greatly. Also, our algorithm can solve benchmark instances efficiently, and it has significantly better performance than state-of-the-art algorithms.
Provable Sensor Sets for Epidemic Detection over Networks with Minimum Delay
Authors: Heavey Jack , Cui Jiaming , Chen Chen , Prakash B. Aditya , Vullikanti Anil
Published: July 2022

The efficient detection of outbreaks and other cascading phenomena is a fundamental problem in a number of domains, including disease spread, social networks, and infrastructure networks. In such settings, monitoring and testing a small group of pre-selected nodes from the susceptible population (i.e., a sensor set) is often the preferred testing regime. We study the problem of selecting a sensor set that minimizes the delay in detection---we refer to this as the MinDelSS problem. Prior methods for minimizing the detection time rely on greedy algorithms using submodularity. We show that this approach can sometimes lead to a worse approximation for minimizing the detection time than desired. We also show that MinDelSS is hard to approximate within an O(n^(1-1/g))-factor for any constant g greater than or equal to 2 for a graph with n nodes. This instead motivates seeking a bicriteria approximations. We present the algorithm RoundSensor, which gives a rigorous worst case O(log(n))-factor for the detection time, while violating the budget by a factor of O(log^2(n)). Our algorithm is based on the sample average approximation technique from stochastic optimization, combined with linear programming and rounding. We evaluate our algorithm on several networks, including hospital contact networks, which validates its effectiveness in real settings.
Towards Automated Discovery of God-Like Folk Algorithms for Rubik’s Cube
Authors: Katz Garrett E. , Tahir Naveed
Published: July 2022

We present a multi-objective meta-search procedure that constructs candidate algorithms for state-space search puzzles like Rubik's cube. The candidate algorithms take the form of macro databases, i.e., rule tables that specify sequences of actions to perform in different states. Rules are repeatedly applied until the puzzle is solved. The objectives favor candidates that are god-like (solving the puzzle in fewer steps) and folk-like (having fewer rules in the macro database). We build each candidate with a non-deterministic rule table construction, and then optimize over the non-deterministic choice points to find candidates near the Pareto-optimal trades-offs between godliness and folksiness. We prove that the rule table construction is correct: it always terminates and solves every state at termination. This is verified empirically on the full 2x2x2 "pocket" cube, where correct (but unoptimized) constructions take under one hour and the total number of rules is less than 10% the number of possible states. We also empirically assess the multi-objective optimization on restricted variants of the cube with up to 29K possible states, showing relative improvements in the objectives between 14-20%. Avenues for scaling up the method in future work are discussed.
Split Moves for Monte-Carlo Tree Search
Authors: Kowalski Jakub , Mika Maksymilian , Pawlik Wojciech , Sutowicz Jakub , Szykuła Marek , Winands Mark H. M.
Published: July 2022

In many games, moves consist of several decisions made by the player. These decisions can be viewed as separate moves, which is already a common practice in multi-action games for efficiency reasons. Such division of a player move into a sequence of simpler / lower level moves is called splitting. So far, split moves have been applied only in forementioned straightforward cases, and furthermore, there was almost no study revealing its impact on agents' playing strength. Taking the knowledge-free perspective, we aim to answer how to effectively use split moves within Monte-Carlo Tree Search (MCTS) and what is the practical impact of split design on agents' strength. This paper proposes a generalization of MCTS that works with arbitrarily split moves. We design several variations of the algorithm and try to measure the impact of split moves separately on efficiency, quality of MCTS, simulations, and action-based heuristics. The tests are carried out on a set of board games and performed using the Regular Boardgames General Game Playing formalism, where split strategies of different granularity can be automatically derived based on an abstract description of the game. The results give an overview of the behavior of agents using split design in different ways. We conclude that split design can be greatly beneficial for single- as well as multi-action games.
PEA*+IDA*: An Improved Hybrid Memory-Restricted Algorithm
Authors: Messa Frederico , Pereira André Grahl
Published: July 2022

It is well-known that the search algorithms A* and Iterative Deepening A* (IDA*) can fail to solve state-space tasks optimally due to time and memory limits. The former typically fails in memory-restricted scenarios and the latter in time-restricted scenarios. Therefore, several algorithms were proposed to solve state-space tasks optimally using less memory than A* and less time than IDA*, such as A*+IDA*, a hybrid memory-restricted algorithm that combines A* and IDA*. In this paper, we present a hybrid memory-restricted algorithm that combines Partial Expansion A* (PEA*) and IDA*. This new algorithm has two phases, the same structure as the A*+IDA* algorithm. The first phase of PEA*+IDA* runs PEA* until it reaches a memory limit, and the second phase runs IDA* without duplicate detection on each node of PEA*'s Open. First, we present a model that shows how PEA*+IDA* can perform better than A*+IDA* although pure PEA* usually makes more expansions than pure A*. Later, we perform an experimental evaluation using three memory limits and show that, compared to A*+IDA* on classical planning domains, PEA*+IDA* has higher coverage and expands fewer nodes. Finally, we experimentally analyze both algorithms and show that having higher F-limits and better priority-queue composition given by PEA* have a considerable impact on the performance of the algorithms.
Hibernated Backdoor: A Mutual Information Empowered Backdoor Attack to Deep Neural Networks
Authors: Ning Rui , Li Jiang , Xin Chunsheng , Wu Hongyi , Wang Chonggang
Published: July 2022

We report a new neural backdoor attack, named Hibernated Backdoor, which is stealthy, aggressive and devastating. The backdoor is planted in a hibernated mode to avoid being detected. Once deployed and fine-tuned on end-devices, the hibernated backdoor turns into the active state that can be exploited by the attacker. To the best of our knowledge, this is the first hibernated neural backdoor attack. It is achieved by maximizing the mutual information (MI) between the gradients of regular and malicious data on the model. We introduce a practical algorithm to achieve MI maximization to effectively plant the hibernated backdoor. To evade adaptive defenses, we further develop a targeted hibernated backdoor, which can only be activated by specific data samples and thus achieves a higher degree of stealthiness. We show the hibernated backdoor is robust and cannot be removed by existing backdoor removal schemes. It has been fully tested on four datasets with two neural network architectures, compared to five existing backdoor attacks, and evaluated using seven backdoor detection schemes. The experiments demonstrate the effectiveness of the hibernated backdoor attack under various settings.
Sparsification of Decomposable Submodular Functions
Authors: Rafiey Akbar , Yoshida Yuichi
Published: July 2022

Submodular functions are at the core of many machine learning and data mining tasks. The underlying submodular functions for many of these tasks are decomposable, i.e., they are sum of several simple submodular functions. In many data intensive applications, however, the number of underlying submodular functions in the original function is so large that we need prohibitively large amount of time to process it and/or it does not even fit in the main memory. To overcome this issue, we introduce the notion of sparsification for decomposable submodular functions whose objective is to obtain an accurate approximation of the original function that is a (weighted) sum of only a few submodular functions. Our main result is a polynomial-time randomized sparsification algorithm such that the expected number of functions used in the output is independent of the number of underlying submodular functions in the original function. We also study the effectiveness of our algorithm under various constraints such as matroid and cardinality constraints. We complement our theoretical analysis with an empirical study of the performance of our algorithm.
A Novel Approach to Solving Goal-Achieving Problems for Board Games
Authors: Shih Chung-Chin , Wu Ti-Rong , Wei Ting Han , Wu I-Chen
Published: July 2022

Goal-achieving problems are puzzles that set up a specific situation with a clear objective. An example that is well-studied is the category of life-and-death (L&D) problems for Go, which helps players hone their skill of identifying region safety. Many previous methods like lambda search try null moves first, then derive so-called relevance zones (RZs), outside of which the opponent does not need to search. This paper first proposes a novel RZ-based approach, called the RZ-Based Search (RZS), to solving L&D problems for Go. RZS tries moves before determining whether they are null moves post-hoc. This means we do not need to rely on null move heuristics, resulting in a more elegant algorithm, so that it can also be seamlessly incorporated into AlphaZero's super-human level play in our solver. To repurpose AlphaZero for solving, we also propose a new training method called Faster to Life (FTL), which modifies AlphaZero to entice it to win more quickly. We use RZS and FTL to solve L&D problems on Go, namely solving 68 among 106 problems from a professional L&D book while a previous state-of-the-art program TSUMEGO-EXPLORER solves 11 only. Finally, we discuss that the approach is generic in the sense that RZS is applicable to solving many other goal-achieving problems for board games.
Civil engineers designing buildings need to get up to speed on competencies required
Authors: Carpenter John
Published: July 2022

Civil engineers designing buildings need to ensure they meet the evolving competency requirements of the Building Safety Act. John Carpenter, former secretary of the Standing Committee on Structural Safety, says this includes an understanding of soft hazards.
The research needed to help society adapt and be more resilient to climate change
Authors: Webb Sarah
Published: July 2022

Climate change is a global problem requiring global solutions, including new research into how to improve adaptation and resilience of communities. Sarah Webb of the Natural Environment Research Council reports on its recent assessment of environmental research priorities.
Unified modelling framework of flowslide triggering and runout
Authors: Chen Yanni , Buscarnera Giuseppe
Published: July 2022

Hydro-mechanical forcing, including sediment deposition, seismic loading, rainfall events and phreatic level rise, can alter the pore water pressure regime and trigger ground failure. In loose sands, such phenomena can manifest with rapid downslope movements displaying signatures of liquefaction and commonly referred to as ‘flowslides’. These can have catastrophic outcomes, yet the use of constitutive models for soils able to quantify the likelihood of liquefaction is usually restricted to triggering analyses, while the quantification of runout distance and velocity tends to be conducted through computationally expensive models based on rheological laws for fluids. To enable a seamless transition from the triggering to the runout stage, in this paper, a spatially condensed modelling framework of low computational cost is proposed, which allows for straightforward use of soil constitutive models in the analysis of the entire life cycle of a flowslide. The proposed framework couples the dynamic motion of a landslide mass with the pore fluid transients taking place within a pre-defined liquefiable zone. The constitutive law modulates the feedback between pore fluid diffusion and landslide movement, encompassing elastoplastic and viscoplastic soil models within the same formulation. Simulations show that partial drainage in the liquefiable zone has considerable effects on the triggering stresses initiating a flowslide. Specifically, the results imply that liquefaction risks are sharply suppressed when the ratio between the characteristic times of loading and pore pressure diffusion falls above a critical threshold influenced by the inelastic deformation response of the soil. Furthermore, it is shown that all the dynamics of flowslide propagation are dominated by the ratio between the characteristic timescales related to fluid consolidation, wave propagation and soil reaction time (i.e. soil viscosity). Although viscosity is not necessary to carry out propagation analyses, ignoring it leads to unrealistic estimates of peak velocity and runout distance. As a result, the model predicts that sand viscosity reflecting the emergence of a microscopic inertial regime is required to explain the flowslide propagation characteristics reported in the literature.
Sekizinci Sınıf Öğrencilerinin Kareköklü Sayılar Konusundaki Bilgiyi Oluşturma Süreçlerinin İncelenmesi
Authors: DİNÇ Yakup , YENİLMEZ Kürşat
Published: July 2022

Bu çalışmanın amacı 8. sınıf öğrencilerinin kareköklü sayılar konusundaki bilgiyi oluşturma süreçlerinin incelenmesidir. Araştırmada farklı matematik başarısına sahip öğrencilerin bilgiyi oluşturma süreçlerini incelemek amaçlandığından araştırma deseni olarak nitel araştırma yöntemlerinden durum çalışması deseni kullanılmıştır. Araştırmanın katılımcılarını dokuz sekizinci sınıf öğrencisi oluşturmaktadır. Bu öğrencilerden üç öğrenci ile pilot uygulama, altı öğrenci ile esas uygulama gerçekleştirilmiştir. Sekizinci sınıf öğrencilerinin kareköklü sayı kavramını oluşturma süreçlerinin incelenmesi amacıyla dört uygulama sorusu hazırlanmıştır. Öğrenciler soruları cevaplarken yarı yapılandırılmış görüşme sırasında kaydedilen video kayıtları ve katılımcı gözlem tekniği ile elde edilen araştırmacı notları araştırmanın veri toplama araçlarını oluşturmaktadır. Araştırmanın verileri betimsel analiz yöntemi kullanılarak analiz edilmiştir. Araştırmadan elde edilen bulgulara göre, irrasyonel sayı kavramı için araştırmaya katılan dokuz öğrenciden matematik başarısı iyi olan pilot uygulamada bir öğrenci ile esas uygulamada bir öğrenci olmak üzere toplam iki öğrenci oluşturma basamağına ulaşmışlardır. Matematik başarısı orta seviyede olan öğrenciler tanıma ve kullanma basamaklarına ulaşırken matematik başarısı düşük seviyede olan öğrencilerden bazıları tanıma veya kullanma basamaklarına da ulaşamamışlardır.
Clase, capital y distinción en The Whitsun Weddings de Philip Larkin
Authors: Pirnajmuddin Hossein , Asadi Amjad Fazel , Shahpoori Arani Fatemeh
Published: July 2022

Con la publicación de las cartas de Philip Larkin, se reveló que su afirmación de celebrar el lector común y el estilo de vida inglés común, en contraste con las actitudes de los elitistas modernistas, no era del todo sincera. Algunos poemas de su colección The Whitsun Weddings (Las bodas de Pentecostés) describen en particular la ambivalencia de Larkin hacia cuestiones de clase social, habitus y distinción. Haciendo uso de la teorización de la sociedad y la cultura de Pierre Bourdieu, este artículo explora cuestiones de clase social, la integración de los personajes en su clase social y habitus, y formas de capital que figuran en cuatro poemas de la colección de Larkin. Se examinan cuatro poemas («Mr. Bleaney», «Dockery and Son», «For Sidney Bechet» y «The Whitsun Weddings») para ver cómo Larkin vincula personajes comunes a su espacio social al describir los tipos de capital que poseen o carecen y cómo, irónicamente, los hablantes intentan mantener la distancia –para marcar distinción– mientras profesan simpatía y comprensión. Por lo tanto, se arroja algo de luz sobre la ambivalencia de Larkin sobre la gente común y las clases bajas, así como sobre la idea misma de distinción.
When Modern Housing Built Optimistic Suburbia: A Comparative Analysis Between Lisbon and Luanda
Authors: Rodrigues Inês
Published: July 2022

Throughout the 1960s, the urban peripheries in several Portuguese colonial cities embarked on a profound process of transformation. With different urban histories and distant geographical contexts, Lisbon and Luanda were united by urban planning and public policies defined by the Estado Novo’s response to the lack of housing supply. The neighbourhoods that expressed modern affiliation in their architecture witnessed profound changes brought about by the April 25th Revolution and the consequent process of democracy in Portugal (1974) and independence in Angola (1975). This article proposes a comparative analysis of middle-class housing complexes, demystifying the urban peripheries by an optimistic architecture that helped shape the built environment and echoed its time’s urban and political concerns. It analyses four case studies, taking into account their inherent characteristics (urban layout, architecture, and interior design), their significance as a testimony to the social and political aspirations of the time, and the quality of life and lifestyles of their current population. It draws on sociological surveys and analysis of plans, photographs, and maps to carry out a broader picture of modern housing through the work of Fernando Silva in Lisbon and Fernão Simões de Carvalho in Luanda. Based on current research, this article aims to assess the resilience of these neighbourhoods by analysing the housing landscape from an urban and architectural perspective. By mapping the changes after 50 years of use, the intention is to understand how they have adapted to current conditions (urban and social) and support future actions.
An Early Neolithic Sanctuary in the Eastern Irtysh Basin
Authors: Molodin V. I. , Mylnikova L. N. , Nesterova M. S. , Kobeleva L. S. , Selin D. V.
Published: July 2022

We describe the findings of excavations at an unusual sanctuary in the Baraba forest-steppe. It is a structure consisting of a ditch encircling the presumed sacral space, and a system of pits containing non-utilitarian artifacts. Pits in the bottom of the ditch indicate wooden structures, which are not preserved. Descriptions of the features are provided. Artifacts are related to household, manufacturing, and ritual. On the basis of stratigraphy and radiocarbon analysis, relative and absolute chronology is assessed. The site dates to the 7th–6th millennia BC and is associated with the Barabinskaya culture. Parallels with Mesolithic and Neolithic sanctuaries and ritual sites in the Eurasian taiga zone are listed.
Philip Johan Tabbert von Strahlenberg: An 18th Century Swedish Prisoner’s Research in Siberia
Authors: Borisenko A. Y.
Published: July 2022

This article analyzes the work done by the Swedish prisoner of the Great Northern War Philip Johan Tabbert von Strahlenberg during his stay in Siberia and aimed at exploring ancient and traditional cultures of Western Siberia and the Minusinsk Basin. A brief overview of earlier studies is presented. The conditions of Strahlenberg’s work are outlined. His main interests, from his arrival in Siberia’s capital until his return to Sweden, concerned the cartography, ethnology, and archaeology of the environs of Tobolsk and of the entire Western Siberia, Minusinsk Basin, and Southern Siberian highlands in particular. Some episodes in Strahlenberg’s activities as a researcher and collector are described with a focus on the difficulties he experienced, specifically when collecting ancient artifacts and written documents. Certain results of his research are highlighted. From the modern standpoint, the article examines the significance of Strahlenberg’s work for Russian archaeology at the stage when its basis of sources was being formed. His place among the first experts in Western and Southern Siberian ancient and traditional cultures is assessed. The key role in the organization and consolidation of research in the remote fringes of the Russian State in the early 1700s belonged to the Russian Academy of Sciences.
Endomarketing em instituições de ensino superior: proposição do modelo gestão, encanto e motivação
Authors: Eich Ferreira Morgana , Barth Mauricio
Published: July 2022

O presente trabalho tem como objetivos (a) investigar e analisar as ações de Endomarketing em uma Instituição de Ensino Superior (IES) localizada em Novo Hamburgo/RS e, ainda, (b) propor um modelo que permita analisar tais ações e que possa, futuramente, ser aplicado em outras empresas. Para tanto, fundamentado em conceitos, objetivos e instrumentos de endomarketing, o estudo apresenta o Modelo Gestão, Encanto e Motivação desenvolvido e, posteriormente, utilizado como técnica de análise na instituição selecionada. Ao fim do trabalho, constata-se que a IES escolhida busca, por meio de suas ações, maior aproximação com seus colaboradores e que, ainda, o modelo desenvolvido mostra-se apropriado para tais conclusões.