bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Complexity of Consistent Query Answering in the Presence of Simple Ontologies
Authors: Bienvenu Meghyn
Published: June 2022

Consistent query answering is a standard approach for producing meaningful query answers when data is inconsistent. Recent work on consistent query answering in the presence of ontologies has shown this problem to be intractable in data complexity even for ontologies expressed in lightweight description logics. In order to better understand the source of this intractability, we investigate the complexity of consistent query answering for simple ontologies consisting only of class subsumption and class disjointness axioms. We show that for conjunctive queries with at most one quantified variable, the problem is first-order expressible; for queries with at most two quantified variables, the problem has polynomial data complexity but may not be first-order expressible; and for three quantified variables, the problem may become co-NP-hard in data complexity. For queries having at most two quantified variables, we further identify a necessary and sufficient condition for first-order expressibility. In order to be able to handle arbitrary conjunctive queries, we propose a novel inconsistency-tolerant semantics and show that under this semantics, first-order expressibility is always guaranteed. We conclude by extending our positive results to DL-Lite ontologies without inverse.
Concept-Based Approach to Word-Sense Disambiguation
Authors: Raviv Ariel , Markovitch Shaul
Published: June 2022

The task of automatically determining the correct sense of a polysemous word has remained a challenge to this day. In our research, we introduce Concept-Based Disambiguation (CBD), a novel framework that utilizes recent semantic analysis techniques to represent both the context of the word and its senses in a high-dimensional space of natural concepts. The concepts are retrieved from a vast encyclopedic resource, thus enriching the disambiguation process with large amounts of domain-specific knowledge. In such concept-based spaces, more comprehensive measures can be applied in order to pick the right sense. Additionally, we introduce a novel representation scheme, denoted anchored representation, that builds a more specific text representation associated with an anchoring word. We evaluate our framework and show that the anchored representation is more suitable to the task of word-sense disambiguation (WSD). Additionally, we show that our system is superior to state-of-the-art methods when evaluated on domain-specific corpora, and competitive with recent methods when evaluated on a general corpus.
Ontology-Based Data Access with Dynamic TBoxes in DL-Lite
Authors: Di Pinto Floriana , De Giacomo Giuseppe , Lenzerini Maurizio , Rosati Riccardo
Published: June 2022

In this paper we introduce the notion of mapping-based knowledge base (MKB) to formalize the situation where both the extensional and the intensional level of the ontology are determined by suitable mappings to a set of (relational) data sources. This allows for making the intensional level of the ontology as dynamic as traditionally the extensional level is. To do so, we resort to the meta-modeling capabilities of higher-order Description Logics, which allow us to see concepts and roles as individuals, and vice versa. The challenge in this setting is to design tractable query answering algorithms. Besides the definition of MKBs, our main result is that answering instance queries posed to MKBs expressed in Hi(DL-LiteR) can be done efficiently. In particular, we define a query rewriting technique that produces first-order (SQL) queries to be posed to the data sources.
Transportability of Causal Effects: Completeness Results
Authors: Bareinboim Elias , Pearl Judea
Published: June 2022

The study of transportability aims to identify conditions under which causal information learned from experiments can be reused in a different environment where only passive observations can be collected. The theory introduced in [Pearl and Bareinboim, 2011] (henceforth [PB, 2011]) defines formal conditions for such transfer but falls short of providing an effective procedure for deciding, given assumptions about differences between the source and target domains, whether transportability is feasible. This paper provides such procedure. It establishes a necessary and sufficient condition for deciding when causal effects in the target domain are estimable from both the statistical information available and the causal information transferred from the experiments. The paper further provides a complete algorithm for computing the transport formula, that is, a way of fusing experimental and observational information to synthesize an estimate of the desired causal relation.
On Maxsum Fair Cake Divisions
Authors: Brams Steven , Feldman Michal , Lai John , Morgenstern Jamie , Procaccia Ariel
Published: June 2022

We consider the problem of selecting fair divisions of a heterogeneous divisible good among a set of agents. Recent work (Cohler et al., AAAI 2011) focused on designing algorithms for computing maxsum—social welfare maximizing—allocations under the fairness notion of envy-freeness. Maxsum allocations can also be found under alternative notions such as equitability. In this paper, we examine the properties of these allocations. In particular, We provide conditions for when maxsum envy-free or equitable allocations are Pareto optimal and give examples where fairness with Pareto optimality is not possible. We also prove that maxsum envy-free allocations have weakly greater welfare than maxsum equitable allocations when agents have structured valuations, and we derive an approximate version of this inequality for general valuations.
The Price of Neutrality for the Ranked Pairs Method
Authors: Brill Markus , Fischer Felix
Published: June 2022

The complexity of the winner determination problem has been studied for almost all common voting rules. A notable exception, possibly caused by some confusion regarding its exact definition, is the method of ranked pairs. The original version of the method, due to Tideman, yields a social preference function that is irresolute and neutral. A variant introduced subsequently uses an exogenously given tie-breaking rule and therefore fails neutrality. The latter variant is the one most commonly studied in the area of computational social choice, and it is easy to see that its winner determination problem is computationally tractable. We show that by contrast, computing the set of winners selected by Tideman's original ranked pairs method is NP-complete, thus revealing a trade-off between tractability and neutrality. In addition, several known results concerning the hardness of manipulation and the complexity of computing possible and necessary winners are shown to follow as corollaries from our findings.
Possible Winners in Noisy Elections
Authors: Wojtas Krzysztof , Faliszewski Piotr
Published: June 2022

We consider the problem of predicting winners in elections given complete knowledge about all possible candidates, all possible voters (together with their preferences), but in the case where it is uncertain either which candidates exactly register for the election or which voters cast their votes. Under reasonable assumptions our problems reduce to counting variants of election control problems. We either give polynomial-time algorithms or prove #P-completeness results for counting variants of control by adding/deleting candidates/voters for Plurality, k-Approval, Approval, Condorcet, and Maximin voting rules.
A Hybrid Algorithm for Coalition Structure Generation
Authors: Rahwan Talal , Michalak Tomasz , Jennings Nicholas
Published: June 2022

The current state-of-the-art algorithm for optimal coalition structure generation is IDP-IP — an algorithm that combines IDP (a dynamic programming algorithm due to Rahwan and Jennings, AAAI'08) with IP (a tree-search algorithm due to Rahwan et al., JAIR'09). In this paper we analyse IDP-IP, highlight its limitations, and then develop a new approach for combining IDP with IP that overcomes these limitations.
Evaluating Resistance to False-Name Manipulations in Elections
Authors: Waggoner Bo , Xia Lirong , Conitzer Vincent
Published: June 2022

In many mechanisms (especially online mechanisms), a strategic agent can inﬂuence the outcome by creating multiple false identities. We consider voting settings where the mechanism designer cannot completely prevent false-name manipulation, but may use false-name-limiting methods such as CAPTCHAs to inﬂuence the amount and characteristics of such manipulation. Such a designer would prefer, ﬁrst, a high probability of obtaining the “correct” outcome, and second, a statistical method for evaluating the correctness of the outcome. In this paper, we focus on settings with two alternatives. We model voters as independently drawing a number of identities from a distribution that may be inﬂuenced by the choice of the false-name-limiting method. We give a criterion for the evaluation and comparison of these distributions. Then, given the results of an election in which false-name manipulation may have occurred, we propose and justify a statistical test for evaluating the outcome.
A Parameterized Runtime Analysis of Evolutionary Algorithms for the Euclidean Traveling Salesperson Problem
Authors: Sutton Andrew , Neumann Frank
Published: June 2022

We contribute to the theoretical understanding of evolutionary algorithms and carry out a parameterized analysis of evolutionary algorithms for the Euclidean traveling salesperson problem (Euclidean TSP). We exploit structural properties related to the optimization process of evolutionary algorithms for this problem and use them to bound the runtime of evolutionary algorithms. Our analysis studies the runtime in dependence of the number of inner points $k$ and shows that simple evolutionary algorithms solve the Euclidean TSP in expected time O(nk(2k-1)!).  Moreover, we show that, under reasonable geometric constraints, a locally optimal 2-opt tour can be found by randomized local search in expected time $O(n2kk!).
Ensemble Feature Weighting Based on Local Learning and Diversity
Authors: Li Yun , Gao Suyan , Chen Songcan
Published: June 2022

Recently, besides the performance, the stability (robustness, i.e., the variation in feature selection results due to small changes in the data set) of feature selection is received more attention. Ensemble feature selection where multiple feature selection outputs are combined to yield more robust results without sacrificing the performance is an effective method for stable feature selection. In order to make further improvements of the performance (classification accuracy), the diversity regularized ensemble feature weighting framework is presented, in which the base feature selector is based on local learning with logistic loss for its robustness to huge irrelevant features and small samples. At the same time, the sample complexity of the proposed ensemble feature weighting algorithm is analyzed based on the VC-theory. The experiments on different kinds of data sets show that the proposed ensemble method can achieve higher accuracy than other ensemble ones and other stable feature selection strategy (such as sample weighting) without sacrificing stability
Towards Discovering What Patterns Trigger What Labels
Authors: Li Yu-Feng , Hu Ju-Hua , Jiang Yuang , Zhou Zhi-Hua
Published: June 2022

In many real applications, especially those involving data objects with complicated semantics, it is generally desirable to discover the relation between patterns in the input space and labels corresponding to different semantics in the output space. This task becomes feasible with MIML (Multi-Instance Multi-Label learning), a recently developed learning framework, where each data object is represented by multiple instances and is allowed to be associated with multiple labels simultaneously. In this paper, we propose KISAR, an MIML algorithm that is able to discover what instances trigger what labels. By considering the fact that highly relevant labels usually share some patterns, we develop a convex optimization formulation and provide an alternating optimization solution. Experiments show that KISAR is able to discover reasonable relations between input patterns and output labels, and achieves performances that are highly competitive with many state-of-the-art MIML algorithms.
Kernel-Based Reinforcement Learning on Representative States
Authors: Kveton Branislav , Theocharous Georgios
Published: June 2022

Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batch-mode reinforcement learning (RL) with continuous state variables. The method is an approximation to kernel-based RL on a set of k representative states. Similarly to kernel-based RL, our solution is a fixed point of a kernelized Bellman operator and can approximate the optimal solution to an arbitrary level of granularity. Unlike kernel-based RL, our method is fast. In particular, our policies can be computed in O(n) time, where n is the number of training examples. The time complexity of kernel-based RL is Ω(n2). We introduce our method, analyze its convergence, and compare it to existing work. The method is evaluated on two existing control problems with 2 to 4 continuous variables and a new problem with 64 variables. In all cases, we outperform state-of-the-art results and offer simpler solutions.
Efficient Online Learning for Large-Scale Sparse Kernel Logistic Regression
Authors: Zhang Lijun , Jin Rong , Chen Chun , Bu Jiajun , He Xiaofei
Published: June 2022

In this paper, we study the problem of large-scale Kernel Logistic Regression (KLR). A straightforward approach is to apply stochastic approximation to KLR. We refer to this approach as non-conservative online learning algorithm because it updates the kernel classifier after every received training example, leading to a dense classifier. To improve the sparsity of the KLR classifier, we propose two conservative online learning algorithms that update the classifier in a stochastic manner and generate sparse solutions. With appropriately designed updating strategies, our analysis shows that the two conservative algorithms enjoy similar theoretical guarantee as that of the non-conservative algorithm. Empirical studies on several benchmark data sets demonstrate that compared to batch-mode algorithms for KLR, the proposed conservative online learning algorithms are able to produce sparse KLR classifiers, and achieve similar classification accuracy but with significantly shorter training time. Furthermore, both the sparsity and classification accuracy of our methods are comparable to those of the online kernel SVM.
Leveraging Domain Knowledge in Multitask Bayesian Network Structure Learning
Authors: Oyen Diane , Lane Terran
Published: June 2022

Network structure learning algorithms have aided network discovery in fields such as bioinformatics, neuroscience, ecology and social science. However, challenges remain in learning informative networks for related sets of tasks because the search space of Bayesian network structures is characterized by large basins of approximately equivalent solutions. Multitask algorithms select a set of networks that are near each other in the search space, rather than a score-equivalent set of networks chosen from independent regions of the space. This selection preference allows a domain expert to see only differences supported by the data. However, the usefulness of these algorithms for scientific datasets is limited because existing algorithms naively assume that all pairs of tasks are equally related. We introduce a framework that relaxes this assumption by incorporating domain knowledge about task-relatedness into the learning objective. Using our framework, we introduce the first multitask Bayesian network algorithm that leverages domain knowledge about the relatedness of tasks. We use our algorithm to explore the effect of task-relatedness on network discovery and show that our algorithm learns networks that are closer to ground truth than naive algorithms and that our algorithm discovers patterns that are interesting.
Design and Optimization of an Omnidirectional Humanoid Walk: A Winning Approach at the RoboCup 2011 3D Simulation Competition
Authors: MacAlpine Patrick , Barrett Samuel , Urieli Daniel , Vu Victor , Stone Peter
Published: June 2022

This paper presents the design and learning architecture for an omnidirectional walk used by a humanoid robot soccer agent acting in the RoboCup 3D simulation environment. The walk, which was originally designed for and tested on an actual Nao robot before being employed in the 2011 RoboCup 3D simulation competition, was the crucial component in the UT Austin Villa team winning the competition in 2011. To the best of our knowledge, this is the first time that robot behavior has been conceived and constructed on a real robot for the end purpose of being used in simulation.  The walk is based on a double linear inverted pendulum model, and multiple sets of its parameters are optimized via a novel framework. The framework optimizes parameters for different tasks in conjunction with one another, a little-understood problem with substantial practical significance.  Detailed experiments show that the UT Austin Villa agent significantly outperforms all the other agents in the competition with the optimized walk being the key to its success.
Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching
Authors: Treeratpituk Pucktada , Giles C. Lee
Published: June 2022

Personal names are important and common information in many data sources, ranging from social networks and news articles to patient records and scientific documents.They are often used as queries for retrieving records and also as key information for linking documents from multiple sources. Matching personal names can be challenging due to variations in spelling and various formatting of names. While many approximated name matching techniques have been proposed, most are generic string-matching algorithms. Unlike other types of proper names, personal names are highly cultural. Many ethnicities have their own unique naming systems and identifiable characteristics. In this paper we explore such relationships between ethnicities and personal names to improve the name matching performance. First, we propose a name-ethnicity classifier based on the multinomial logistic regression. Our model can effectively identify name-ethnicity from personal names in Wikipedia, which we use to define name-ethnicity, to within 85\% accuracy.Next, we propose a novel alignment-based name matching algorithm, based on Smith–Waterman algorithm and logistic regression.Different name matching models are then trained for different name-ethnicity groups.Our preliminary experimental result on DBLP's disambiguated author dataset yields a performance of 99\% precision and 89\% recall.Surprisingly, textual features carry more weight than phonetic ones in name-ethnicity classification.
Agent-Human Coordination with Communication Costs Under Uncertainty
Authors: Frieder Asaf , Lin Raz , Kraus Sarit
Published: June 2022

Coordination in mixed agent-human environments is an important, yet not a simple, problem. Little attention has been given to the issues raised in teams that consist of both computerized agents and people. In such situations different considerations are in order, as people tend to make mistakes and they are affected by cognitive, social and cultural factors. In this paper we present a novel agent designed to proficiently coordinate with a human counterpart. The agent uses a neural network model that is based on a pre-existing knowledge base which allows it to achieve an efficient modeling of a human's decisions and predict their behavior. A novel communication mechanism which takes into account the expected effect of communication on the other member will allow communication costs to be minimized. In extensive simulations involving more than 200 people we investigated our approach and showed that our agent achieves better coordination when involved, compared to settings in which only humans or another state-of-the-art agent are involved.
An Object-Based Bayesian Framework for Top-Down Visual Attention
Authors: Borji Ali , Sihite Dicky , Itti Laurent
Published: June 2022

We introduce a new task-independent framework to model top-down overt visual attention based on graph-ical models for probabilistic inference and reasoning. We describe a Dynamic Bayesian Network (DBN) that infers probability distributions over attended objects and spatial locations directly from observed data. Probabilistic inference in our model is performed over object-related functions which are fed from manual annotations of objects in video scenes or by state-of-the-art object detection models. Evaluating over ∼3 hours (appx. 315,000 eye fixations and 12,600 saccades) of observers playing 3 video games (time-scheduling, driving, and flight combat), we show that our approach is significantly more predictive of eye fixations compared to: 1) simpler classifier-based models also developed here that map a signature of a scene (multi-modal information from gist, bottom-up saliency, physical actions, and events) to eye positions, 2) 14 state-of-the-art bottom-up saliency models, and 3) brute-force algorithms such as mean eye position. Our results show that the proposed model is more effective in employing and reasoning over spatio-temporal visual data.
Automated Strategies for Determining Rewards for Human Work
Authors: Azaria Amos , Aumann Yonatan , Kraus Sarit
Published: June 2022

We consider the problem of designing automated strategies for interactions with human subjects, where the humans must be rewarded for performing certain tasks of interest. We focus on settings where there is a single task that must be performed many times by different humans (e.g. answering a questionnaire), and the humans require a fee for performing the task. In such settings, our objective is to minimize the average cost for effectuating the completion of the task. We present two automated strategies for designing efficient agents for the problem, based on two different models of human behavior. The first, the Reservation Price Based Agent (RPBA), is based on the concept of a reservation price, and the second, the No Bargaining Agent (NBA), uses principles from behavioral science. The performance of the agents has been tested in extensive experiments with real human subjects, where NBA outperforms both RPBA and strategies developed by human experts.
Learning to Learn: Algorithmic Inspirations from Human Problem Solving
Authors: Kapoor Ashish , Lee Bongshin , Tan Desney , Horvitz Eric
Published: June 2022

We harness the ability of people to perceive and interact with visual patterns in order to enhance the performance of a machine learning method. We show how we can collect evidence about how people optimize the parameters of an ensemble classification system using a tool that provides a visualization of misclassification costs. Then, we use these observations about human attempts to minimize cost in order to extend the performance of a state-of-the-art ensemble classification system. The study highlights opportunities for learning from evidence collected about human problem solving to refine and extend automated learning and inference.
Sense Sentiment Similarity: An Analysis
Authors: Mohtarami Mitra , Amiri Hadi , Lan Man , Tran Thanh Phu , Tan Chew Lim
Published: June 2022

This paper describes an emotion-based approach to acquire sentiment similarity of word pairs with respect to their senses. Sentiment similarity indicates the similarity between two words from their underlying sentiments. Our approach is built on a model which maps from senses of words to vectors of twelve basic emotions. The emotional vectors are used to measure the sentiment similarity of word pairs. We show the utility of measuring sentiment similarity in two main natural language processing tasks, namely, indirect yes/no question answer pairs (IQAP) Inference and sentiment orientation (SO) prediction. Extensive experiments demonstrate that our approach can effectively capture the sentiment similarity of word pairs and utilize this information to address the above mentioned tasks.
Action Selection for MDPs: Anytime AO* Versus UCT
Authors: Bonet Blai , Geffner Hector
Published: June 2022

In the presence of non-admissible heuristics, A* and other best-first algorithms can be converted into anytime optimal algorithms over OR graphs, by simply continuing the search after the first solution is found. The same trick, however, does not work for best-first algorithms over AND/OR graphs, that must be able to expand leaf nodes of the explicit graph that are not necessarily part of the best partial solution. Anytime optimal variants of AO* must thus address an exploration-exploitation tradeoff: they cannot just ”exploit”, they must keep exploring as well. In this work, we develop one such variant of AO* and apply it to finite-horizon MDPs. This Anytime AO* algorithm eventually delivers an optimal policy while using non-admissible random heuristics that can be sampled, as when the heuristic is the cost of a base policy that can be sampled with rollouts. We then test Anytime AO* for action selection over large infinite-horizon MDPs that cannot be solved with existing off-line heuristic search and dynamic programming algorithms, and compare it with UCT.
Bayesian Unification of Sound Source Localization and Separation with Permutation Resolution
Authors: Otsuka Takuma , Ishiguro Katsuhiko , Sawada Hiroshi , Okuno Hiroshi
Published: June 2022

Sound source localization and separation with permutation resolution are essential for achieving a computational auditory scene analysis system that can extract useful information from a mixture of various sounds. Because existing methods cope separately with these problems despite their mutual dependence, the overall result with these approaches can be degraded by any failure in one of these components. This paper presents a unified Bayesian framework to solve these problems simultaneously where localization and separation are regarded as a clustering problem. Experimental results confirm that our method outperforms state-of-the-art methods in terms of the separation quality with various setups including practical reverberant environments.
Efficient Optimization of Control Libraries
Authors: Dey Debadeepta , Liu Tian , Sofman Boris , Bagnell James
Published: June 2022

A popular approach to high dimensional control problems in robotics uses a library of candidate “maneuvers” or “trajectories”. The library is either evaluated on a fixed number of candidate choices at runtime (e.g. path set selection for planning) or by iterating through a sequence of feasible choices until success is achieved (e.g. grasp selection). The performance of the library relies heavily on the content and order of the sequence of candidates. We propose a provably efficient method to optimize such libraries, leveraging recent advances in optimizing submodular functions of sequences. This approach is demonstrated on two important problems: mobile robot navigation and manipulator grasp set selection. In the first case, performance can be improved by choosing a subset of candidates which optimizes the metric under consideration (cost of traversal). In the second case, performance can be optimized by minimizing the depth in the list that is searched before a successful candidate is found. Our method can be used in both on-line and batch settings with provable performance guarantees, and can be run in an anytime manner to handle real-time constraints.
Improving Request Compliance through Robot Affect
Authors: Moshkina Lilia
Published: June 2022

This paper describes design and results of a human-robot interaction study aimed at determining the extent to which affective robotic behavior can influence participants' compliance with a humanoid robot’s request in the context of a mock-up search-and-rescue setting. The results of the study argue for inclusion of affect into robotic systems, showing that nonverbal expressions of negative mood (nervousness) and fear by the robot improved the participants' compliance with its request to evacuate, causing them to respond earlier and faster.
Symmetric Rendezvous in Planar Environments With and Without Obstacles
Authors: Ozsoyeller Deniz , Isler Volkan , Beveridge Andrew
Published: June 2022

We study the symmetric rendezvous search problem in which two robots that are unaware of each other’s locations try to meet as quickly as possible. In the symmetric version of this problem, the robots are required to execute the same strategy. First, we present a symmetric rendezvous strategy for the robots that are initially placed on the open plane and analyze its competitive performance. We show that the competitive complexity of our strategy is O(d/R) where d is the initial distance between the robots and R is the communication radius. Second, we extend the symmetric rendezvous strategy for the open plane to unknown environments with polygonal obstacles. The extended strategy guarantees a complete coverage of the environment. We analyze the strategy for square, translating robots and show that the competitive ratio of the extended strategy is O(d/D) where D is the length of the sides of the robots. In obtaining this result, we also obtain an upper bound on covering arbitrary polygonal environments which may be of independent interest.
A Search Algorithm for Latent Variable Models with Unbounded Domains
Authors: Chiang Michael , Poole David
Published: June 2022

This paper concerns learning and prediction with probabilistic models where the domain sizes of latent variables have no a priori upper-bound. Current approaches represent prior distributions over latent variables by stochastic processes such as the Dirichlet process, and rely on Monte Carlo sampling to estimate the model from data. We propose an alternative approach that searches over the domain size of latent variables, and allows arbitrary priors over the their domain sizes. We prove error bounds for expected probabilities, where the error bounds diminish with increasing search scope. The search algorithm can be truncated at any time. We empirically demonstrate the approach for topic modelling of text documents.
Sequential Decision Making with Rank Dependent Utility: A Minimax Regret Approach
Authors: Jeantet Gildas , Perny Patrice , Spanjaard Olivier
Published: June 2022

This paper is devoted to sequential decision making with Rank Dependent expected Utility (RDU). This decision criterion generalizes Expected Utility and enables to model a wider range of observed (rational) behaviors. In such a sequential decision setting, two conflicting objectives can be identified in the assessment of a strategy: maximizing the performance viewed from the initial state (optimality), and minimizing the incentive to deviate during implementation (deviation-proofness). In this paper, we propose a minimax regret approach taking these two aspects into account, and we provide a search procedure to determine an optimal strategy for this model. Numerical results are presented to show the interest of the proposed approach in terms of optimality, deviation-proofness and computability.
Online Sequence Alignment for Real-Time Audio Transcription by Non-Experts
Authors: Lasecki Walter , Miller Christopher , Borrello Donato , Bigham Jeffrey
Published: June 2022

Real-time transcription provides deaf and hard of hearing people visual access to spoken content, such as classroom instruction, and other live events. Currently, the only reliable source of real-time transcriptions are expensive, highly-trained experts who are able to keep up with speaking rates. Automatic speech recognition is cheaper but produces too many errors in realistic settings. We introduce a new approach in which partial captions from multiple non-experts are combined to produce a high-quality transcription in real-time. We demonstrate the potential of this approach with data collected from 20 non-expert captionists.
Informed Initial Policies for Learning in Dec-POMDPs
Authors: Kraemer Landon , Banerjee Bikramjit
Published: June 2022

Decentralized partially observable Markov decision processes (Dec-POMDPs) offer a formal model for planning in cooperative multiagent systems where agents operate with noisy sensors and actuators, and local information. Prevalent Dec-POMDP solution techniques have mostly been centralized and have assumed knowledge of the model. In real world scenarios, however, solving centrally may not be an option and model parameters maybe unknown. To address this, we propose a distributed, model-free algorithm for learning Dec-POMDP policies, in which agents take turns learning, with each agent not currently learning following a static policy. For agents that have not yet learned a policy, this static policy must be initialized. We propose a principled method for learning such initial policies through interaction with the environment. We show that by using such informed initial policies, our alternate learning algorithm can find near-optimal policies for two benchmark problems.
Detection and Prediction of Adverse and Anomalous Events in Medical Robots
Authors: Liang Kai , Cao Feng , Bai Zhuofu , Renfrew Mark , Çavuşoğu M. , Podgurski Andy , Ray Soumya
Published: June 2022

Adverse and anomalous (A&A) events are a serious concern in medical robots. We describe a system that can rapidly detect such events and predict their occurrence. As part of this system, we describe simulation, data collection and user interface tools we build for a robot for small animal biopsies. The data we collect consists of both the hardware state of the robot and variables in the software controller. We use this data to train dynamic Bayesian network models of the joint hardware-software state-space dynamics of the robot. Our empirical evaluation shows that (i) our models can accurately model normal behavior of the robot, (ii) they can rapidly detect anomalous behavior once it starts, (iii) they can accurately predict a future A&A event within a time window of it starting and (iv) the use of additional software variables beyond the hardware state of the robot is important in being able to detect and predict certain kinds of events.
Durability of self-healing cementitious systems with encapsulated polyurethane evaluated with a new pre-standard test method
Authors: Anglani Giovanni , Van Mullem Tim , Tulliani Jean-Marc , Van Tittelboom Kim , De Belie Nele , Antonaci Paola
Published: June 2022

This work reports on the self-healing capabilities of mortar specimens with polyurethane encapsulated in two types of cementitious macro-capsules, by comparison with the performance of mortar specimens using the same healing agent encapsulated in glass capsules, as tested in an inter-laboratory testing campaign following a pre-standard procedure. This comparison was performed with a twofold objective of checking the robustness of such pre-standard procedure for varying types of capsules and testing the effectiveness of a new type of cementitious capsule that has never been used before in durability tests. The testing procedure was developed in the framework of the EU COST Action SARCOS. First, the specimens were pre-cracked via three-point bending followed by an active crack width control technique. Then, the self-healing effect was characterised in terms of water permeability reduction. The cementitious capsules offered equivalent or better performance compared to the glass capsules used in the inter-laboratory testing. The average sealing efficiency for the specimens containing cementitious capsules ranged from 54 to 74%, while for glass macro-capsules it was equal to 56%. It was also observed that when applying the pre-standard procedure to test specimens containing capsules with comparable size and geometric arrangement, the same results were obtained in different repetitions of the test. The results obtained confirmed the possibility to use the cementitious capsules as a valid macro-encapsulation system, offering additional advantages compared to glass capsules. The repeatability of the results corroborated the robustness of the adopted testing procedure, highlighting its potential for further standardisation.
Infiltration behaviour into sand of bentonite slurry with guar gum
Authors: Lei H. , Liu X. , Shi F. , Ma C.
Published: June 2022

Bentonite slurry is widely used in various projects due to its excellent engineering properties. The behaviour of slurry infiltration, especially filter cake formation, is directly related to the effect of slurry. This paper proposes an improved apparatus to investigate the infiltration of a novel bentonite slurry, especially in the dynamic infiltration process of the slurry-driven shield, which provides significant references for tunnel excavation. According to monitoring indicators of water discharge volume, pore-water pressure and electrical conductivity, the hydraulic conductivity of filter cake and the major zone of infiltration were analysed. Besides, the results of scanning electron microscopy reveal the micro-interaction between slurry and the soil. The results illustrate that the bentonite slurry with guar gum could immediately form an external filter cake with an infiltrated zone. The external filter cake formed again with a denser bentonite clay platelet network after excavation in the condition of relatively short excavation distance. Therefore, this kind of slurry could rapidly stabilise the excavation surface in slurry-driven shield tunnelling.
Isolated aphasic status epilepticus: CT perfusion, SPECT and EEG reveal neurovascular coupling and support the differential diagnosis
Authors: Manganotti Paolo , Furlanis Giovanni , Cova Maria Assunta , Olivo Sasha , Dore Franca , Sartori Arianna , Naccarato Marcello
Published: June 2022

Objective. Among the clinical manifestations of stroke mimics, isolated aphasia is one of the most challenging due to its aetiopathogenic diagnosis. This short communication describes a specific perfusion and brain oscillatory pattern in a challenging case of prolonged isolated aphasia caused by status epilepticus(SE), jointly investigated by computed tomography (CT) perfusion, single‐photon emission computerized tomography (SPECT)/CT and EEG qualitative and quantitative analysis.

Methods. We discuss the different patterns of perfusion neuroimaging and EEG between SE and ischaemic stroke or postictal (Todd's)‐related isolated aphasia, and propose these differences as a basis to support the differential diagnosis.

Results. The pattern associated with SE was characterized by focal hyperperfusion on CT perfusion maps (the left mean transit time was shorter with >10% asymmetry, and left cerebral blood volume and cerebral blood flow increased or slightly altered, relative to the contralateral side) and SPECT (focal left temporal hyperperfusion), without any early ischaemic signs on non‐enhanced CT, while the EEG showed a predominant left hemispheric slow delta power. The aforementioned perfusion pattern contrasts with postictal epileptic Todd's phenomenon, which is characterized by hypoperfusion on CT perfusion (the mean transit time is prolonged and cerebral blood volume and cerebral blood flow are reduced, compared to the contralateral hemisphere) and SPECT (focal hypoperfusion), not restricted to the specific vascular territories.

Significance. CT perfusion patterns may add valuable information to support the differential diagnosis of status epilepticus, rather than acute ischaemic stroke or postictal Todd's phenomenon, in cases with challenging symptoms of prolonged isolated aphasia.
Valoración crítica, una perspectiva en el siglo XXI: Capitalismo, socialismo y democracia
Authors: Boada Antonio
Published: June 2022

El presente texto ofrece un análisis de la obra de Schumpeter titulada Capitalismo, socialismo y democracia con la finalidad de ofrecer una perspectiva, no solo desde el punto de vista económico sino además social. Para ello se consideraron las tesis de las obras de Marx (1984) y Schumpeter (1983) que establecen una visión moderna para el siglo XXI.Mediante la teoría de horizonte de expectativas establecidas por Jauss desde el año 1967 (Broitman, 2015), se propone, en esta oportunidad, la interpretación de la época de Schumpeter y Marx como una suma de comportamientos, conocimientos e ideas preconcebidas que encuentra una obra en el momento de su aparición y que constituye el contexto de su valoración, estableciendo de forma sinérgica una contraposición con la realidad posmoderna del siglo XXI.
Effect of Game-based Learning using Live Streaming on Learners' Interest, Immersion, Satisfaction, and Instructors' Perception
Authors: Lee Woo-Hyun , Shim Han-Moi , Kim Hyung-Gi
Published: June 2022

The method of using games in class has been recognized for its effectiveness through the efforts of many researchers, and has spread mainly in the United States and Europe, and the number of cases implemented in actual education has increased. Nevertheless, many people have preconceived notions about games and distrust the effects of game-based learning and show negative attitudes. In fact, there are limitations and disadvantages of game-based learning, and it is difficult to overcome them. More than half of the inconveniences or problems of teachers who want to try game-based learning revealed by the 2009 survey have not been solved yet. Due to the sudden spread of COVID-19, the Ministry of Education has issued a policy to convert classes at schools and academies into non-face-to-face. The full-scale non-face-to-face class, which was conducted based on the high penetration rate of smartphones and Internet penetration in Korea, allows all students to take non-face-to-face learning using the Internet, It can be interpreted that even if there are students who cannot take classes, the right to equality is recognized because there are very few. Non-face-to-face classes have the characteristics that instructors and learners will be spatially separated, spatially simultaneous, interactive, and teaching and learning behavior will be mediated by the screen. And these features are common to live streaming. Taking advantage of this, this study aims to approach improving the perception of game-based learning as an advantage of "watching games" beyond "playing games" by using live streaming for game-based learning. This study conducted game-based learning and game streaming learning for 210 third-year middle school students, compared the effects of game-based learning on learning interest, immersion, and satisfaction with the results of previous studies to verify the design and prove the combined effect of live streaming and game-based learning. In addition, in-depth interviews with instructors who conducted game-based learning confirmed that awareness of the introduction, value, and finance of game-based learning improved.
[RETRACTED] Summer Trim Keto Gummies Reviews- (Burn Fat Quickly)Does Really Work! v1
Authors: not provided billihilton
Published: June 2022

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The Summer Trim Keto Gummies are made from 100 BHB which makes them pleasant and solid to give solid fat hardship influences. Most of the time, it’s difficult to paste to a ketosis weight loss plan, and hundreds of people fall off in advance than they achieve their weight loss dreams. Additionally, it can take some weeks to peer the extraordinary consequences. The Summer Trim Keto Gummies are crafted from one hundred BHB which makes them comfortable and effective to provide powerful fats loss consequences.
Immunohistochemistry v1
Authors: Nelson Alexandra
Published: June 2022

This protocol describes immunohistochemical staining of fixed brain sections.
Deep Brain Stimulation (DBS) Implant v1
Authors: Nelson Alexandra
Published: June 2022

This protocols describing the steps for making implants used for deep brain stimulation (DBS) in rodents.
[RETRACTED] Condor CBD Gummies Reviews [Shark Tank Episode Alert]- Price for Sale & Website? v1
Authors: not provided officialnaomi
Published: June 2022

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Struggling with poor mental health conditions are not easy and tricky to treat. Untreated mental disorders may lead to severe emotional, behavioral and physical health issues. Sometimes,
[RETRACTED] Simply Health ACV Keto Gummies Real or Fraud? v1
Authors: keto Gummies Acv
Published: June 2022

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Simply Health ACV Keto Gummies Real or Fraud?
[RETRACTED] ACV KETO GUMMIES - {CANADA - UNITED STATES} SCAM? Stunning REPORT APPLE CIDER VINEGAR ULTIMATE PROS CONS LEGIT? v1
Authors: keto Gummies Acv
Published: June 2022

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The ACV 10X Keto Gummies are delicious confections that you can eat without any problem. It is comprised of normal spices.
Ex Vivo Electrophysiology v1
Authors: Nelson Alexandra
Published: June 2022

This protocol describes preparation of brain slices, setup of electrophysiology rig, and solutions for collecting whole cell and cell attached recordings.
Topographical Organization, Morphology, and Density Analysis of Substance P (SP)-IR axons in the Whole Mouse Stomach v1
Authors: Nguyen Duyen
Published: June 2022

This protocol describes the process of mapping the topographical organization of substance P (SP) immuno-reactive axons and terminals in the mouse stomach. Stomachs were removed from the abdominal cavities, layers of the organ were separated and gone under immunohistochemistry as whole mounts, then scanned using confocal and/or Zeiss microscopes. The stomach was reconstructed into a 2D maximum projection image and was evaluated for density of SP-IR axons. This analysis method was performed on the stomach as a whole as well as on separated layers (myenteric plexus, circular and longitudinal muscles).
SAPROCAN: Saracatinib (AZD0530) and docetaxel in metastatic, castrate-refractory prostate cancer: a phase I/randomised phase II study by the UK NCRI Prostate Clinical Studies Group v1
Authors: Jones Robert
Published: June 2022

Design:Phase I followed by a phase II randomised placebo controlled trial. Aims:To provide preliminary evidence regarding whether the addition of saracatinib (AZD0530) to first line docetaxel plus prednisolone will increase progression free survival in patients with metastatic, castrate refractory prostate cancer. Outcome Measures: Phase I: Safety and tolerability Phase II: Progression free survival Treatment: All patients will receive docetaxel by iv infusion once every 3 weeks for a maximum of 10 cycles with prednisolone 5mg bd po daily from the first day of docetaxel up to at least day 21 of the final dose. Phase I Patients in the phase I component of the study will commence once-daily saracatinib (AZD0530) on day 11 of the first cycle of docetaxel (dose as specified) po. They will continue until disease progression is confirmed. Phase II Patients in the phase II component of the study will be randomly assigned to receive either saracatinib (AZD0530) once daily by mouth at a dose to be defined in phase I or a matching placebo. This will be taken by mouth starting 7 days prior to the first dose of docetaxel and stopping when disease progression is confirmed. Duration of treatment Up to 10 cycles of docetaxel will be given. Prednisolone should be continued until 21 days after the last dose of docetaxel but may continue beyond this at the discretion of the investigator. Saracatinib should be continued until disease progression or withdrawal. The expected median duration of therapy is around 37 weeks and is unlikely to exceed 68 weeks. Duration of study participation Study participation will commence when the patient gives written informed consent and will continue until both disease progression and resolution of study drug toxicity have occurred. Survival information will continue to be collected after these events have occurred until the study is completed.
[RETRACTED] Condor CBD Gummies (Shark Tank Pros Cons Side Effect) Shocking Results Reviews is worth it! Beware 2022 v1
Authors: not provided autoteq
Published: June 2022

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Struggling with poor mental health conditions are not easy and tricky to treat. Untreated mental disorders may lead to severe emotional, behavioral and physical health issues.