bluuebunny/crossref_metadata_2025 huggingface dataset:
Basing Decisions on Sentences in Decision Diagrams
Authors: Xue Yexiang , Choi Arthur , Darwiche Adnan
Published: June 2022

The Sentential Decision Diagram (SDD) is a recently proposed representation of Boolean functions, containing Ordered Binary Decision Diagrams (OBDDs) as a distinguished subclass. While OBDDs are characterized by total variable orders, SDDs are characterized by dissections of variable orders, known as vtrees. Despite this generality, SDDs retain a number of properties, such as canonicity and a polytime apply operator, that have been critical to the practical success of OBDDs. Moreover, upper bounds on the size of SDDs were also given, which are tighter than comparable upper bounds on the size of OBDDs. In this paper, we analyze more closely some of the theoretical properties of SDDs and their size. In particular, we consider the impact of basing decisions on sentences (using dissections as in SDDs), in comparison to basing decisions on variables (using total variable orders as in OBDDs). Here, we identify a class of Boolean functions where basing decisions on sentences using dissections of a variable order can lead to exponentially more compact SDDs, compared to OBDDs based on the same variable order. Moreover, we identify a fundamental property of the decompositions that underlie SDDs and use it to show how certain changes to a vtree can also lead to exponential differences in the size of an SDD.
Compiling Model-Based Diagnosis to Boolean Satisfaction
Authors: Metodi Amit , Stern Roni , Kalech Meir , Codish Mike
Published: June 2022

This paper introduces an encoding of Model Based Diagnosis (MBD) to Boolean Satisfaction (SAT) focusing on minimal cardinality diagnosis. The encoding is based on a combination of sophisticated MBD preprocessing algorithms and SAT compilation techniques which together provide concise CNF formula. Experimental evidence indicates that our approach is superior to all published algorithms for minimal cardinality MBD. In particular, we can determine, for the first time, minimal cardinality diagnoses for the entire standard ISCAS-85 benchmark. Our results open the way to improve the state-of-the-art on a range of similar MBD problems.
Computing Stackelberg Equilibria in Discounted Stochastic Games
Authors: Vorobeychik Yevgeniy , Singh Satinder
Published: June 2022

Stackelberg games increasingly influence security policies deployed in real-world settings. Much of the work to date focuses on devising a fixed randomized strategy for the defender, accounting for an attacker who optimally responds to it. In practice, defense policies are often subject to constraints and vary over time, allowing an attacker to infer characteristics of future policies based on current observations. A defender must therefore account for an attacker's observation capabilities in devising a security policy. We show that this general modeling framework can be captured using stochastic Stackelberg games (SSGs), where a defender commits to a dynamic policy to which the attacker devises an optimal dynamic response. We then offer the following contributions. 1) We show that Markov stationary policies suffice in SSGs, 2) present a finite-time mixed-integer non-linear program for computing a Stackelberg equilibrium in SSGs, and 3) present a mixed-integer linear program to approximate it. 4) We illustrate our algorithms on a simple SSG representing an adversarial patrolling scenario, where we study the impact of attacker patience and risk aversion on optimal defense policies.
A Dynamic Rationalization of Distance Rationalizability
Authors: Boutilier Craig , Procaccia Ariel
Published: June 2022

Distance rationalizability is an intuitive paradigm for developing and studying voting rules: given a notion of consensus and a distance function on preference profiles, a rationalizable voting rule selects an alternative that is closest to being a consensus winner. Despite its appeal, distance rationalizability faces the challenge of connecting the chosen distance measure and consensus notion to an operational measure of social desirability. We tackle this issue via the decision-theoretic framework of dynamic social choice, in which a social choice Markov decision process (MDP) models the dynamics of voter preferences in response to winner selection. We show that, for a prominent class of distance functions, one can construct a social choice MDP, with natural preference dynamics and rewards, such that a voting rule is (votewise) rationalizable with respect to the unanimity consensus for a given distance function iff it is a (deterministic) optimal policy in the MDP. This provides an alternative rationale for distance rationalizability, demonstrating the equivalence of rationalizable voting rules in a static sense and winner selection to maximize societal utility in a dynamic process.
Computing the Nucleolus of Matching, Cover and Clique Games
Authors: Chen Ning , Lu Pinyan , Zhang Hongyang
Published: June 2022

In cooperative games, a key question is to find a division of payoffs to coalition members in a fair manner. Nucleolus is one of such solution concepts that provides a stable solution for the grand coalition. We study the computation of the nucleolus of a number of cooperative games, including fractional matching games and fractional edge cover games on general weighted graphs, as well as vertex cover games and clique games on weighted bipartite graphs. Our results are on the positive side---we give efficient algorithms to compute the nucleolus, as well as the least core, of all of these games.
Congestion Games with Agent Failures
Authors: Meir Reshef , Tennenholtz Moshe , Bachrach Yoram , Key Peter
Published: June 2022

We propose a natural model for agent failures in congestion games. In our model, each of the agents may fail to participate in the game, introducing uncertainty regarding the set of active agents. We examine how such uncertainty may change the Nash equilibria (NE) of the game. We prove that although the perturbed game induced by the failure model is not always a congestion game, it still admits at least one pure Nash equilibrium. Then, we turn to examine the effect of failures on the maximal social cost in any NE of the perturbed game. We show that in the limit case where failure probability is negligible new equilibria never emerge, and that the social cost may decrease but it never increases. For the case of non-negligible failure probabilities, we provide a full characterization of the maximal impact of failures on the social cost under worst-case equilibrium outcomes.
Negotiation in Exploration-Based Environment
Authors: Sofer Israel , Sarne David , Hassidim Avinatan
Published: June 2022

This paper studies repetitive negotiation over the execution of an exploration process between two self-interested, fully rational agents in a full information environmentwith side payments. A key aspect of the protocolis that the exploration’s execution may interleaves ith the negotiation itself, inflicting some degradationon the exploration’s flexibility. The advantage of this form of negotiation is in enabling the agents supervising that the exploration’s execution takes place in its agreedform as negotiated. We show that in many cases, much of the computational complexity of the new protocol can be eliminated by solving an alternative negotiation scheme according to which the parties first negotiate theexploration terms as a whole and then execute it. As demonstrated in the paper, the solution characteristics of the new protocol are somehow different from thoseof legacy negotiation protocols where the execution of the agreement reached through the negotiation is completely separated from the negotiation process. Furthermore, if the agents are given the option to control some of the negotiation protocol parameters, the resulting exploration may be suboptimal. In particular we show that the increase in an agent’s expected utility in such casesis unbounded and so is the resulting decrease in the social welfare. Surprisingly, we show that further increasingone of the agents’ level of control in some of thenegotiation parameters enables bounding the resultingdecrease in the social welfare.
Influence-Based Abstraction for Multiagent Systems
Authors: Oliehoek Frans , Witwicki Stefan , Kaelbling Leslie
Published: June 2022

This paper presents a theoretical advance by which factored POSGs can be decomposed into local models. We formalize the interface between such local models as the influence agents can exert on one another; and we prove that this interface is sufficient for decoupling them. The resulting influence-based abstraction substantially generalizes previous work on exploiting weakly-coupled agent interaction structures. Therein lie several important contributions. First, our general formulation sheds new light on the theoretical relationships among previous approaches, and promotes future empirical comparisons that could come by extending them beyond the more specific problem contexts for which they were developed. More importantly, the influence-based approaches that we generalize have shown promising improvements in the scalability of planning for more restrictive models. Thus, our theoretical result here serves as the foundation for practical algorithms that we anticipate will bring similar improvements to more general planning contexts, and also into other domains such as approximate planning, decision-making in adversarial domains, and online learning.
Teaching Machines to Learn by Metaphors
Authors: Levy Omer , Markovitch Shaul
Published: June 2022

Humans have an uncanny ability to learn new concepts with very few examples. Cognitive theories have suggested that this is done by utilizing prior experience of related tasks. We propose to emulate this process in machines, by transforming new problems into old ones. These transformations are called metaphors. Obviously, the learner is not given a metaphor, but must acquire one through a learning process. We show that learning metaphors yield better results than existing transfer learning methods. Moreover, we argue that metaphors give a qualitative assessment of task relatedness.
Manifold Warping: Manifold Alignment over Time
Authors: Vu Hoa , Carey Clifton , Mahadevan Sridhar
Published: June 2022

Knowledge transfer is computationally challenging, due in part to the curse of dimensionality, compounded by source and target domains expressed using different features (e.g., documents written in different languages). Recent work on manifold learning has shown that data collected in real-world settings often have high-dimensional representations, but lie on low-dimensional manifolds. Furthermore, data sets collected from similar generating processes often present different high-dimensional views, even though their underlying manifolds are similar. The ability to align these data sets and extract this common structure is critical for many transfer learning tasks. In this paper, we present a novel framework for aligning two sequentially-ordered data sets, taking advantage of a shared low-dimensional manifold representation. Our approach combines traditional manifold alignment and dynamic time warping algorithms using alternating projections. We also show that the previously-proposed canonical time warping algorithm is a special case of our approach. We provide a theoretical formulation as well as experimental results on synthetic and real-world data, comparing manifold warping to other alignment methods.
Investigating the Effectiveness of Laplacian-Based Kernels in Hub Reduction
Authors: Suzuki Ikumi , Hara Kazuo , Shimbo Masashi , Matsumoto Yuji , Saerens Marco
Published: June 2022

A “hub” is an object closely surrounded by, or very similar to, many other objects in the dataset. Recent studies by Radovanovi´c et al. indicate that in high dimensional spaces, hubs almost always emerge, and objects close to the data centroid tend to become hubs. In this paper, we show that the family of kernels based on the graph Laplacian makes all objects in the dataset equally similar to the centroid, and thus they are expected to make less hubs when used as a similarity measure. We investigate this hypothesis using both synthetic and real-world data. It turns out that these kernels suppress hubs in some cases but not always, and the results seem to be affected by the size of the data—a factor not discussed previously. However, for the datasets in which hubs are indeed reduced by the Laplacian-based kernels, these kernels work well in ranking and classification tasks. This result suggests that the amount of hubs, which can be readily computed in an unsupervised fashion, can be a yardstick of whether Laplacian-based kernels work effectively for a given data.
Sparse Probabilistic Relational Projection
Authors: Li Wu-Jun , Yeung Dit-Yan
Published: June 2022

Probabilistic relational PCA (PRPCA) can learn a projection matrix to perform dimensionality reduction for relational data. However, the results learned by PRPCA lack interpretability because each principal component is a linear combination of all the original variables. In this paper, we propose a novel model, called sparse probabilistic relational projection (SPRP), to learn a sparse projection matrix for relational dimensionality reduction. The sparsity in SPRP is achieved by imposing on the projection matrix a sparsity-inducing prior such as the Laplace prior or Jeffreys prior. We propose an expectation-maximization (EM) algorithm to learn the parameters of SPRP. Compared with PRPCA, the sparsity in SPRP not only makes the results more interpretable but also makes the projection operation much more efficient without compromising its accuracy. All these are verified by experiments conducted on several real applications.
Hierarchical Double Dirichlet Process Mixture of Gaussian Processes
Authors: Tayal Aditya , Poupart Pascal , Li Yuying
Published: June 2022

We consider an infinite mixture model of Gaussian processes that share mixture components between non-local clusters in data. Meeds and Osindero (2006) use a single Dirichlet process prior to specify a mixture of Gaussian processes using an infinite number of experts. In this paper, we extend this approach to allow for experts to be shared non-locally across the input domain. This is accomplished with a hierarchical double Dirichlet process prior, which builds upon a standard hierarchical Dirichlet process by incorporating local parameters that are unique to each cluster while sharing mixture components between them. We evaluate the model on simulated and real data, showing that sharing Gaussian process components non-locally can yield effective and useful models for richly clustered non-stationary, non-linear data.
Learning from Demonstration for Goal-Driven Autonomy
Authors: Weber Ben , Mateas Michael , Jhala Arnav
Published: June 2022

Goal-driven autonomy (GDA) is a conceptual model for creating an autonomous agent that monitors a set of expectations during plan execution, detects when discrepancies occur, builds explanations for the cause of failures, and formulates new goals to pursue when planning failures arise. While this framework enables the development of agents that can operate in complex and dynamic environments, implementing the logic for each of the subtasks in the model requires substantial domain engineering. We present a method using case-based reasoning and intent recognition in order to build GDA agents that learn from demonstrations. Our approach reduces the amount of domain engineering necessary to implement GDA agents and learns expectations, explanations, and goals from expert demonstrations. We have applied this approach to build an agent for the real-time strategy game StarCraft. Our results show that integrating the GDA conceptual model into the agent greatly improves its win rate.
A Spin-Glass Model for Semi-Supervised Community Detection
Authors: Eaton Eric , Mansbach Rachael
Published: June 2022

Current modularity-based community detection methods show decreased performance as relational networks become increasingly noisy. These methods also yield a large number of diverse community structures as solutions, which is problematic for applications that impose constraints on the acceptable solutions or in cases where the user is focused on specific communities of interest. To address both of these problems, we develop a semi-supervised spin-glass model that enables current community detection methods to incorporate background knowledge in the forms of individual labels and pairwise constraints. Unlike current methods, our approach shows robust performance in the presence of noise in the relational network, and the ability to guide the discovery process toward specific community structures. We evaluate our algorithm on several benchmark networks and a new political sentiment network representing cooperative events between nations that was mined from news articles over six years.
Strategic Advice Provision in Repeated Human-Agent Interactions
Authors: Azaria Amos , Rabinovich Zinovi , Kraus Sarit , Goldman Claudia , Gal Ya'akov
Published: June 2022

This paper addresses the problem of automated advice provision in settings that involve repeated interactions between people and computer agents. This problem arises in many real world applications such as route selection systems and office assistants. To succeed in such settings agents must reason about how their actions in the present influence people's future actions. This work models such settings as a family of repeated bilateral games of incomplete information called ``choice selection processes'', in which players may share certain goals, but are essentially self-interested. The paper describes several possible models of human behavior that were inspired by behavioral economic theories of people's play in repeated interactions. These models were incorporated into several agent designs to repeatedly generate offers to people playing the game. These agents were evaluated in extensive empirical investigations including hundreds of subjects that interacted with computers in different choice selections processes. The results revealed that an agent that combined a hyperbolic discounting model of human behavior with a social utility function was able to outperform alternative agent designs, including an agent that approximated the optimal strategy using continuous MDPs and an agent using epsilon-greedy strategies to describe people's behavior. We show that this approach was able to generalize to new people as well as choice selection processes that were not used for training. Our results demonstrate that combining computational approaches with behavioral economics models of people in repeated interactions facilitates the design of advice provision strategies for a large class of real-world settings.
Sembler: Ensembling Crowd Sequential Labeling for Improved Quality
Authors: Wu Xian , Fan Wei , Yu Yong
Published: June 2022

Many natural language processing tasks, such as named entity recognition (NER), part of speech (POS) tagging, word segmentation, and etc., can be formulated as sequential data labeling problems. Building a sound labeler requires very large number of correctly labeled training examples, which may not always be possible. On the other hand, crowdsourcing provides an inexpensive yet efficient alternative to collect manual sequential labeling from non-experts. However the quality of crowd labeling cannot be guaranteed, and three kinds of errors are typical: (1) incorrect annotations due to lack of expertise (e.g., labeling gene names from plain text requires corresponding domain knowledge); (2) ignored or omitted annotations due to carelessness or low confidence; (3) noisy annotations due to cheating or vandalism. To correct these mistakes, we present Sembler, a statistical model for ensembling crowd sequential labelings. Sembler considers three types of statistical information: (1) the majority agreement that proves the correctness of an annotation; (2) correct annotation that improves the credibility of the corresponding annotator; (3) correct annotation that enhances the correctness of other annotations which share similar linguistic or contextual features. We evaluate the proposed model on a real Twitter and a synthetical biological data set, and find that Sembler is particularly accurate when more than half of annotators make mistakes.
The Complexity of Planning Revisited — A Parameterized Analysis
Authors: Bäckström Christer , Chen Yue , Jonsson Peter , Ordyniak Sebastian , Szeider Stefan
Published: June 2022

The early classifications of the computational complexity of planning under various restrictions in STRIPS (Bylander) and SAS+ (Bäckström and Nebel) have influenced following research in planning in many ways. We go back and reanalyse their subclasses, but this time using the more modern tool of parameterized complexity analysis. This provides new results that together with the old results give a more detailed picture of the complexity landscape. We demonstrate separation results not possible with standard complexity theory, which contributes to explaining why certain cases of planning have seemed simpler in practice than theory has predicted. In particular, we show that certain restrictions of practical interest are tractable in the parameterized sense of the term, and that a simple heuristic is sufficient to make a well-known partial-order planner exploit this fact.
Incremental Management of Oversubscribed Vehicle Schedules in Dynamic Dial-A-Ride Problems
Authors: Rubinstein Zachary , Smith Stephen , Barbulescu Laura
Published: June 2022

In this paper, we consider the problem of feasibly integrating new pick-up and delivery requests into existing vehicle itineraries in a dynamic, dial-a-ride problem (DARP) setting. Generalizing from previous work in oversubscribed task scheduling, we define a controlled iterative repair search procedure for finding an alternative set of vehicle itineraries in which the overall solution has been feasibly extended to include newly received requests. We first evaluate the performance of this technique on a set of DARP feasibility benchmark problems from the literature. We then consider its use on a real-world DARP problem, where it is necessary to accommodate all requests and constraints must be relaxed when a request cannot be feasibly integrated. For this latter analysis, we introduce a constraint relaxation post processing step and consider the performance impact of using our controlled iterative search over the current greedy search approach.
Symbolic Dynamic Programming for Continuous State and Action MDPs
Authors: Zamani Zahra , Sanner Scott , Fang Cheng
Published: June 2022

Many real-world decision-theoretic planning problemsare naturally modeled using both continuous state andaction (CSA) spaces, yet little work has provided ex-act solutions for the case of continuous actions. Inthis work, we propose a symbolic dynamic program-ming (SDP) solution to obtain the optimal closed-formvalue function and policy for CSA-MDPs with mul-tivariate continuous state and actions, discrete noise,piecewise linear dynamics, and piecewise linear (or re-stricted piecewise quadratic) reward. Our key contribu-tion over previous SDP work is to show how the contin-uous action maximization step in the dynamic program-ming backup can be evaluated optimally and symboli-cally — a task which amounts to symbolic constrainedoptimization subject to unknown state parameters; wefurther integrate this technique to work with an efﬁcientand compact data structure for SDP — the extendedalgebraic decision diagram (XADD). We demonstrateempirical results on a didactic nonlinear planning exam-ple and two domains from operations research to showthe ﬁrst automated exact solution to these problems.
Model Learning and Real-Time Tracking Using Multi-Resolution Surfel Maps
Authors: Stückler Jörg , Behnke Sven
Published: June 2022

For interaction with its environment, a robot is required to learn models of objects and to perceive these models in the livestreams from its sensors. In this paper, we propose a novel approach to model learning and real-time tracking. We extract multi-resolution 3D shape and texture representations from RGB-D images at high frame-rates. An efficient variant of the iterative closest points algorithm allows for registering maps in real-time on a CPU. Our approach learns full-view models of objects in a probabilistic optimization framework in which we find the best alignment between multiple views. Finally, we track the pose of the camera with respect to the learned model by registering the current sensor view to the model. We evaluate our approach on RGB-D benchmarks and demonstrate its accuracy, efficiency, and robustness in model learning and tracking. We also report on the successful public demonstration of our approach in a mobile manipulation task.
Time-Consistency of Optimization Problems
Authors: Osogami Takayuki , Morimura Tetsuro
Published: June 2022

We study time-consistency of optimization problems, where we say that an optimization problem is time-consistent if its optimal solution, or the optimal policy for choosing actions, does not depend on when the optimization problem is solved. Time-consistency is a minimal requirement on an optimization problem for the decisions made based on its solution to be rational. We show that the return that we can gain by taking "optimal" actions selected by solving a time-inconsistent optimization problem can be surely dominated by that we could gain by taking "suboptimal" actions. We establish sufficient conditions on the objective function and on the constraints for an optimization problem to be time-consistent. We also show when the sufficient conditions are necessary. Our results are relevant in stochastic settings particularly when the objective function is a risk measure other than expectation or when there is a constraint on a risk measure.
A Tractable First-Order Probabilistic Logic
Authors: Domingos Pedro , Webb William
Published: June 2022

Tractable subsets of first-order logic are a central topic in AI research. Several of these formalisms have been used as the basis for first-order probabilistic languages. However, these are intractable, losing the original motivation. Here we propose the first non-trivially tractable first-order probabilistic language. It is a subset of Markov logic, and uses probabilistic class and part hierarchies to control complexity. We call it TML (Tractable Markov Logic). We show that TML knowledge bases allow for efficient inference even when the corresponding graphical models have very high treewidth. We also show how probabilistic inheritance, default reasoning, and other inference patterns can be carried out in TML. TML opens up the prospect of efficient large-scale first-order probabilistic inference.
A Bucket Elimination Approach for Determining Strong Controllability of Temporal Plans with Uncontrollable Choices
Authors: Rodrigues Quemel e Assis Santana Pedro , Williams Brian
Published: June 2022

This work presents a new algorithm based on the Bucket Elimination framework that efficiently determines strong controllability of temporal plans formulated as Labeled Simple Temporal Networks with Uncertainty (LSTNU) with controllable and uncontrollable plan branches (choices).
Recommending Related Microblogs: A Comparison Between Topic and WordNet based Approaches
Authors: Chen Xing , Li Lin , Xu Guandong , Yang Zhenglu , Kitsuregawa Masaru
Published: June 2022

Computing similarity between short microblogs is an important step in microblog recommendation. In this paper, we investigate a topic based approach and a WordNet based approach to estimate similarity scores between microblogs and recommend top related ones to users. Empirical study is conducted to compare their recommendation effectiveness using two evaluation measures. The results show that the WordNet based approach has relatively higher precision than that of the topic based approach using 548 tweets as dataset. In addition, the Kendall tau distance between two lists recommended by WordNet and topic approaches is calculated. Its average of all the 548 pair lists tells us the two approaches have the relative high disaccord in the ranking of related tweets.
Computing Game-Theoretic Solutions and Applications to Security
Authors: Conitzer Vincent
Published: June 2022

The multiagent systems community has adopted game theory as a framework for the design of systems of multiple self-interested agents. For this to be effective, efficient algorithms must be designed to compute the solutions that game theory prescribes. In this paper, I summarize some of the state of the art on this topic, focusing particularly on how this line of work has contributed to several highly visible deployed security applications, developed at the University of Southern California.
Interactive Narrative: A Novel Application of Artificial Intelligence for Computer Games
Authors: Riedl Mark , Bulitko Vadim
Published: June 2022

Game Artificial Intelligence (Game AI) is a sub-discipline of Artificial Intelligence (AI) and Machine Learning (ML) that explores the ways in which AI and ML can augment player experiences in computer games. Storytelling is an integral part of many modern computer games; within games stories create context, motivate the player, and move the action forward. Interactive Narrative is the use of AI to create and manage stories within games, creating the perception that the player is a character in a dynamically unfolding and responsive story. This paper introduces Game AI and focuses on the open research problems of Interactive Narrative.
An Undergraduate Course in the Intersection of Computer Science and Economics
Authors: Conitzer Vincent
Published: June 2022

In recent years, major research advances have taken place in the intersection of computer science and economics, but this material has so far been taught primarily at the graduate level. This paper describes a novel semester-long undergraduate-level course in the intersection of computer science and economics at Duke University, titled “CPS 173: Computational Microeconomics.”
Teaching Problem-Solving in Algorithms and AI
Authors: Torrey Lisa
Published: June 2022

This paper suggests some teaching strategies for Algorithms and AI courses. These courses can have a common goal of teaching complex problem-solving techniques. Based on my experience teaching undergraduates in a small liberal-arts college, the paper offers concrete ideas for working toward this goal. These ideas are supported by relevant studies in cognitive science and education. Together, they provide a plan for structuring lessons and assignments to help student become better problem-solvers.
Microplastic pollution in aquatic environments in Africa: status and research opportunities
Authors: Mogusu Emmanuel O , Mushumbusi Cornelius , Minja Gileard , Kimaro Didas
Published: June 2022

The global demand for plastics has increased majorly because of their versatility, light weight, strength and cost–effectiveness. Africa is ranked top in mismanagement of plastic waste, resulting in the plastic problem in the environment. Nevertheless, plastics produce microplastics through degradation and fragmentation of plastic debris largely from anthropogenic sources. Microplastics have become ubiquitous in the natural environment, and the terrestrial environment is the major source. The propensity of microplastics to adsorb and concentrate persistent organic pollutants (POPs) provides potential health effects in the different trophic levels of organisms in both aquatic and territorial environments. Thus, the fate of microplastics is increasingly becoming a global concern. Despite the numerous global studies on the impact of microplastics in the environment, there are insufficient data available on the occurrence and distribution of microplastics and associated health effects in aquatic ecosystems in Africa. The reviewed research articles from 2000 to 2021 provide a summary of the current knowledge on the occurrence and distribution of microplastics, analytical approaches used to detect and quantify microplastics, associated health effects and mitigation measures through government policy to ban plastic use in Africa. The findings presented provide a platform for future research to focus on the associated effects of adsorbed and concentrated POPs on microplastics in aquatic environments in Africa. With the evidence presented, policymakers will make more informed decisions on the future of plastics in Africa. The authors recommend improving information and expanding knowledge through research on the fate and potential ecological impact of microplastics in aquatic environments in Africa.
Interfacial behavior of intravitreally injected drugs in silicone-oil-filled eye models
Authors: Ayalon Anfisa , Rubowitz Alexander , Roy Pritam Kumar , Shoval Shraga , Legchenkova Irina , Bormashenko Edward
Published: June 2022

This paper is devoted to the interfacial aspects of the intraocular behavior, migration and distribution of commonly injected ophthalmic drugs in eyes filled with medical-grade 1300 cSt silicone oil used as a retinal tamponade agent. Novel in vitro and ex vivo models were created for studying the physical properties of the retinal surface and interfacial spreading of the ophthalmic drugs over retinas. In vitro model experiments showed that the droplets of all tested drugs sank rapidly in the silicone oil to contact with the plasma-treated glass and then rapidly spread over the glass surface. In the ex vivo model, the migration phase was followed by contact with and rapid spread/absorption by the retinal interface. The wetting behavior of drugs under contact with the glass substrate and retinas was similar. The characteristic timescales of drug spreading, controlled by the viscous dissipation, were close. All tested drugs migrated to the retinal surface and rapidly spread across the retinal surface. This suggests that intravitreal drugs might be used effectively in eyes filled with silicone oil tamponade, as they rapidly migrate to and spread over the retinal surface.
Self-propelled drops on hydrophilic microfinned surfaces
Authors: Zhou Qianbing , Jia Zhihai , Xiong Xuejiao , Wang Jiao , Dai Xinran
Published: June 2022

Straight and curved hydrophilic microfinned surfaces are prepared in this work by photolithography and sputtering coating techniques using silicon wafers as substrates. The behavioral characteristics of drops on these surfaces are discussed by using image-processing technology. The experimental results show that when a drop is placed on the straight microfinned surface, the front contact line of the drop can move, while the rear contact line remains fixed. On the curved microfinned surface, however, both the front and the rear contact line can move. The drop can be self-propelled directionally from the region with larger roughness to the region with smaller roughness. The characteristics of velocity and acceleration on both surfaces are analyzed. A theoretical model is proposed by analyzing the energy conversion and compared with the experimental results. This study provides a novel microstructured surface for enhancing the heat-transfer performance of condensers.
The neural networks underlying the illusion of time dilation: case study and literature survey
Authors: Sheikh Mubbahsir , Koubeissi Mohamad , Constable R. Todd , Spencer Dennis D. , Alkawadri Rafeed
Published: June 2022

This is the first known case report of a profound illusionary time dilation produced by direct electrical cortical stimulation. The patient with drug‐resistant epilepsy was undergoing invasive EEG evaluation for the localization of the focus prior to resection. Stimulation of the non‐dominant claustrum/insula and inferior right frontal gyrus correlated with the equivalent of a pacemaker of an internal clock in the brain. The site exhibited strong outflow connectivity with a wide region of the right fronto‐parietal executive network and pre‐motor system. The patient described the experience as unique and fascinating, and underwent resection involving the face motor area where seizures arose from, with a substantial decrease in seizure burden >90%. Extensive brain networks are employed in evaluation/assessment of time, which may be modulated via electrical stimulation. This is the first of such an incident. Further studies may investigate the potential benefit of neuromodulation of these brain regions in the management of conditions in which time‐perception may be impaired.
Cortical unihemispheric brain edema (CUBE) due to a multi‐system inflammatory syndrome in adults (MIS‐A)
Authors: Maramattom Boby Varkey , Rao Tejaswi
Published: June 2022

Unilateral hemispheric edema may occur with or without seizures. It is a rare entity, found more often in children, than in adults. As many of these patients develop pronounced unilateral complete cortical hemispheric (uni‐hemispheric) edema on MRI, we have used the term “cortical unihemispheric brain edema” (CUBE) to describe this entity. We describe a case of CUBE occurring along with a post COVID multi‐system inflammatory syndrome in adults (MIS‐A). A 30‐year‐old man was admitted with status epilepticus. He was found to have CUBE and features of MIS‐A. He had a history of mild COVID‐19 illness one month earlier. He was noted to have persistently elevated inflammatory markers and multiorgan dysfunction compatible with MIS‐A. We review the cases of CUBE in the literature and describe the features and etiology of this uncommon syndrome. Furthermore, we discuss the differences between two types of CUBE; cytotoxic CUBE (CUBE‐C) and vasogenic CUBE (CUBE‐V).
Design Principles for Integrating Gamification into Distance Learning Programs in Higher Education: A Mixed Method Study
Authors: Sümer Murat , Aydın Cengiz Hakan
Published: June 2022

This study aims to address the issue of low completion and high dropout rates in online and distance learning through play and gamification. The general aim of this study is to provide a set of gamification design principles for researchers and practitioners on how gamification can be used in online and distance learning programs in higher education. More specifically, the study intended to seek the answers whether student participation in the online and distance learning programs can be increased by the use of gamification, how the gamification influences the students’ academic performances in the online and distance learning programs and what learners think about integrating gamification into the open and distance learning programs in higher education. The study was designed as a sequential exploratory research, which is one of the mixed research methods. Findings indicate that integrating gamification into the online and distance learning programs has a significant impact on increasing students' visits to the learning environment. In addition, when the pre-gamification and post-gamification scenarios were compared in terms of students accessing to the content in the online and distance learning environment, it was discovered that there was a statistically significant increase. It can be also said that there is significant difference regarding to academic performances for gamified situations. Lastly, gamification is said to be fun by students and to contribute positively to their motivation. According to this, gamification has a positive effect on learners' e-learning behaviors and should be used more in online and distance learning programs.
Serious Games in Spatial Planning: Strengths, Limitations and Support Frameworks
Authors: Sousa Micael , Pais Antunes António , Pinto Nuno , Zagalo Nelson
Published: June 2022

Serious games are being developed in many fields of research and proving to be valuable practical tools. In the planning field, games can help approach complexity and engage more participants through first-person experimentation. This paper presents an overview of the main strengths, limitations of applying serious games in Spatial Planning. The paper also discusses available support frameworks. And it proposes some guidelines for researchers and practitioners that want to profit from game usage. Serious Games can be powerful tools but can easily lead to failure processes, which demand previous systematic analysis of what planners may do and expect from games.
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Published: June 2022

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Phenotyping C. elegans behaviour on E. coli bacteria supplemented with enterobactin, iron or paraquat v1
Authors: Moore Saul
Published: June 2022

Testing whether Keio Collection E. coli mutants supplemented with exogenous enterobactin, iron(III) chloride, iron(III) sulfate or paraquat elicit a behavioural response when fed to C. elegans.
[RETRACTED] Condor CBD Gummies:-Price, Safe or Scam or Legitimate? (Spam Or Legit) v1
Authors: CBD Gummies Condor
Published: June 2022

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Condor CBD Gummies are an effective solution to safeguard from several illnesses. These edibles or gummies are usually utilized to ease psychological, physical, and emotional issues.
[RETRACTED] HardHs4 Male Enhancement [CHECK RESULTS?] The Real Reason Everyone Is Buying! v1
Authors: A G
Published: June 2022

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The adequacy of these items has not been affirmed by FDA, or Health Canada endorsed research. These items are not expected to analyze, treat, fix or forestall any illness and give no sort of get-rich cash conspire. Analyst isn't liable for estimating mistakes. Check item deals page at conclusive costs.
[RETRACTED] Derma PGX Cream Review *Trustworthy Solution* Gives You Glowing Skin Quickly Works! v1
Authors: not provided paulgoddson
Published: June 2022

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[RETRACTED] Eagle Hemp CBD Gummies – Pain In Each Area Gone! v1
Authors: Hemp CBD Gummies Eagle
Published: June 2022

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[RETRACTED] Biotics 8 Reviews (ALERT FRAUD) Does This Biotics 8 Formula Really Works? Read Shocking Report! v1
Authors: not provided health
Published: June 2022

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[RETRACTED] SimpliHealth ACV Keto Gummies Shark Tank, Price, Benefits 2022 v1
Authors: not provided simplihealthketogummies
Published: June 2022

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SimpliHealth ACV Keto Gummies Shark Tank, Price, Benefits 2022
Keio Acute Response Antioxidant Rescue- Round 2 v1
Authors: Moore Saul
Published: June 2022

Phenotyping the acute behavioural response of Caenorhabditis elegans (N2 Bristol) to E. coli single-gene deletion mutants (Keio Collection, BW25113 parent strain), in both the presence and absence of antioxidants (Trolox, NAC, vitamin C and resveratrol). Videos are recorded at 25 fps on the laboratory’s (Hydra) imaging rig, immediately after worms are picked onto imaging plates, for a total of 36 minutes, with blue-light stimulus delivered for 10 seconds at the 30, 31 and 32 minute timepoints.
Canadian Airborne Biodiversity Observatory's Forest Inventory Field Survey Protocol v2
Authors: L Crofts Anna
Published: June 2022

Here, we describe the standardized protocol used by the Canadian Airborne Biodiversity Observatory (CABO) to conduct the field-based surveys of canopy trees at the forested study sites, Parc national du Mont Mégantic and Parc national du Mont Saint Bruno, located in southern Québec, Canada. These field-based surveys were conducted to relate field-based tree biodiversity data with aerial hyperspectral imagery data. Forest inventory plots (~706 m2) were systematically distributed to span the range of conditions present within the imaged areas. Plot dimensions were corrected to account for slope in the field and therefore, plots were circular (15 m radii) when viewed-from-above. Within each plot, we quantified the tree community defined as all individuals whose crowns extended into the general level of the canopy and those whose crowns were below the canopy but had a diameter at breast height (DBH) greater than 9 cm. Individuals were identified to species, positioned in relation to the plot center, and a suite of dendrometric properties (e.g., DBH, height) were quantified. To relate the field surveys with the hyperspectral imaging data, we took high-precision positions of plot centers. All data were collected using Fulcrum, a data collection application.
[RETRACTED] https://www.facebook.com/SimplyHealthKetoGummies/ v1
Authors: is likewise a nutritious part of Simpli ACV Keto Gummies. It
Published: June 2022

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[RETRACTED] You May Visit Official Website ACV 10X Keto Gummies On Half Discount Now!! v1
Authors: not provided angelicasould
Published: June 2022

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Developing Tasks to Foster Thai Students’ Willingness to Present in English
Authors: Sridhanyarat Kietnawin , Suranakkharin Todsapon , Suksawas Wannaprapha , Saengmanee Sawitree
Published: June 2022

It is evident that there are many factors that influence degrees of willingness to present among second language (L2) learners. Within the current context, less proficient students have shown clear signs of reluctance to deliver presentations in English language classrooms. Based on the researchers’ assumption and previous existing literature, there are two variables (i.e., topics for presentation and preparation time for presentation) central to the phenomenon situated. This article describes a pilot study which investigated to what extent a speaking task designed on the basis of two key factors (i.e., speaking topics relevant to students’ background knowledge and extended preparation time) produces a positive impact on Thai students’ degrees of willingness to present in English. Under study, 15 undergraduates in a Thai university were encouraged to deliver a presentation about a secret tourist attraction in their city. Flipped learning was also adapted and utilized as an approach that offered students more preparation time to deliver their presentations. Findings gained from students’ written reflections and a willingness to present scale demonstrated that students’ degrees of willingness to present were positively influenced by the task design. Pedagogical implications are also provided in this study.
Effects of Teaching Styles on Chinese University Students’ English Language Abilities
Authors: Lin Haiming
Published: June 2022

Teaching styles play critical roles in students’ English-as-a-second/foreign-language (ESL/EFL) learning. Yet, the relationship between teaching styles and students’ English language abilities is still under-explored. The present study endeavored to explore the predictive power of teaching styles on Chinese university students’ English language abilities. A total of 1,318 English learners from a university in China were recruited. Perceived teaching styles of English teachers were assessed using the Students’ Perceived Teaching Styles Inventory (SPTSI), while English language abilities were measured based on the English Language Ability Self-Assessment Scale (ELASS). Findings indicated that teaching styles positively predicted English language abilities, and the predictive powers of Type I teaching styles on English language abilities were basically greater than that of Type II teaching styles. Pedagogical implications for university English teaching are discussed.
Ortiz, Yolanda, Tierra de malvas, Vélez-Málaga: Delegación de Cultura del Ayuntamiento de Vélez-Málaga, 2019, 65pp.
Authors: Fernández Villalobos Paula
Published: June 2022

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