bluuebunny/crossref_metadata_2025 huggingface dataset:
Parameters of the Blood Oxidant/Antioxidant System in Elderly Patients with Acute Poisoning
Authors: Simonova A. Yu. , Ilyashenko K. K. , Klychnikova E. V. , Evseev A. K. , Potskhveriya M. M. , Belova M. V. , Tazina E. V. , Shabanov A. S. , Kuzovlev A. N.
Published: June 2022

The aim of the study was to assess the oxidant/antioxidant status in elderly patients in the early period of acute poisoning by psychotropic drugs or corrosive substances.

Material and methods. An open prospective observational study with retrospective control was conducted in 80 patients (age ≥ 60 years) with acute poisoning, of which 49 patients aged 72.1±9.55 years had psychotropic drug poisoning (PDP) and 31 subjects aged 73.0±10.3 years had corrosive substance poisoning (CSP). Patients with mild poisoning were excluded from the study. The control group consisted of 39 volunteers aged 68.3±6.3 years. Total antioxidant status (TAS), blood levels of malondialdehyde (MDA), stable nitric oxide metabolites (nitrite/nitrate, NOx), and oxidative stress index (MDA/TAS) were measured on days 1, 3 and 5 after hospital admission.

Results. When analyzing the changes in the parameters of the oxidant/antioxidant system, we observed lower values of the studied parameters in patients with both PDP and CSP compared to the control group. In patients with PDP, several parameters were reduced: MDA by 1.2 times on days 1 and 3 (P=0.002; P=0.008, respectively), NOx by 1.7 times (P<0.001) at all stages of the study, MDA/TAS by 2.4–2.9 times (P<0.001). In patients with CSP, MDA level decreased by 1.1–1.2 times at all study timepoints (P=0.003; P=0.010; P=0.046, respectively), NOx dropped 1.4–1.6-fold (P=0.012; P=0.004; P=0.023, respectively), and MDA/TAS decreased by 2.3–2.4 times (P<0.001). While comparing patients with favorable and fatal outcome, we found that in survived patients an increase of MDA/TAS along with growing NOx level was seen by day 5 with no signiﬁcant changes of MDA and TAS, while in non-survivors MDA/TAS dropped continuously due to progressive fall of NOx level, reaching values 2.8–2.9 times (P<0.001) lower than those of the controls.

Conclusion. In elderly patients with acute poisonings due to psychotropic drugs or corrosive substances, an inadequate response of the oxidant/antioxidant system occurs manifesting as a reduced blood level of peroxidation products with simultaneous normal or slightly decreased concentration of antioxidant protection system components. Thus, the oxidative stress develops, which contributes to the death of the patients.
The Nexus among Agriculture Sector Development and Environmental Degradation in Emerging Economies
Authors: Altınöz Buket
Published: June 2022

This paper aims to determine the effect of agriculture sectordevelopment on CO2 emissions in 10 big emerging marketeconomies. This relationship is tested for the first time for emergingeconomies by using the panel quantile regression approach. Theresults suggest that agricultural value-added mitigate emissionsin lower, middle-lower, and upper quantile levels. This resultimplies that the agricultural sector is an effective policy toolin reducing pollution in these countries. Economic growth andnatural resources rent have a positive impact on pollution. Financialdevelopment only has an emission-reducing effect at the middleupperquantile level, while globalization has a negative impact onCO2 emissions both in the middle-upper and upper quantiles.These results justify the consideration of heterogeneous effects andallow clear policy implications. Moreover, the critical importanceof agricultural policies for environmental quality in these countriesis emphasized with concrete evidence.JEL Classification: O13, Q56, C33
The Adolescent’s Right to Confidential Care When Considering Abortion
Authors:   AMERICAN ACADEMY OF PEDIATRICS,   COMMITTEE ON ADOLESCENCE
Published: June 2022

The American Academy of Pediatrics reaffirms its position that the rights of adolescents under 18 years of age to confidential care when considering legal medical and surgical abortion therapies should be protected. Most adolescents voluntarily involve parents and other trusted adults in decisions regarding pregnancy termination and should be encouraged to do so when safe and appropriate. The legal climate surrounding abortion law is rapidly becoming more restrictive and threatens to adversely impact adolescents. Mandatory parental involvement, the judicial bypass procedure, and general restrictive abortion policies pose risks to adolescents’ health by causing delays in accessing medical care, increasing volatility within a family, and limiting their pregnancy options. These harms underscore the importance of adolescents’ access to confidential abortion care. This statement presents a summary of pertinent current information related to the impact of legislation requiring mandatory parental involvement in an adolescent’s decision to obtain abortion services.
Evolution of the Generalized Scheme of Preferences
Authors: Bagdasaryan Kniaz M.
Published: June 2022

Introduction. The revision of the old U.S. rules of assigning developing country status under the law of countervailing duties in 2020 theoretically symbolizes another stage in the evolution of the Generalized Scheme of Preferences, but practically means a new form of battle for international competitiveness. There is no common criterion for determination of developing countries, either at the level of international organizations, including the World Trade Organization, or at the level of donor countries, while the list of least developed countries is determined by the United Nations, and they automatically become recipients of tariff preferences under the Generalized Scheme of Preferences. In this regard, the main idea and purpose of the study is to identify the applied aspects of the development of the international system of GSP and criteria for identifying developing countries to improve the system of tariff preferences EAEU and increase the competitiveness of goods produced in the domestic market.

Materials and Methods. The paper reviews established rules within the GSP and features of the functioning of this system in the U.S., Canada, Japan, Britain and such integration groupings as the European Union, Commonwealth of Independent States and EAEU. To identify main aspects of this transformation it has been used the method of cross-country comparison, as well as methods of systematic and comparative analysis of relevant international and national legal acts and scientific papers.

Results. We have revealed an international trend that is being formed by the leading economic actors to establish new rules for the functioning of the GSP and the criterion for determining the beneficiary countries. This trend will obviously affect the general rules of international trade within the framework of the World Trade Organization and to some extent will bring changes in its architecture.

Discussion and Conclusion. The process of active unilateral revision of established for half a century international trade rules beyond the framework of international organizations gives to the problem a new systemic character and opens up an opportunity for future studies. It is obvious that we are on the beginning of entire international economic relations system transformation, which at the first stage will be accompanied by negative processes and will require an adequate response from the Russian state authorities, coordinating bodies of the EAEU and interested major economic agents.
Radiocarbon dating of the Funnel Beaker culture settlement in Vynnyky-Lysivka (Western Ukraine)
Authors:   Ivan Krypiakevych Institute of Ukrainian Studies of the National Academy of Sciences of Ukraine, Lviv, Ukraine, Hawinskyj Andryi , Rybicka Małgorzata ,   Institute of Archeology of the Rzeszów University, Poland, Król Dariusz ,   Institute of Archeology of the Rzeszów University, Poland
Published: June 2022

In the 1950s, a settlement of the Funnel Beaker culture (FBC) was discovered on the Lysivka Hill in Vynnyky (Fig. 1), located in the centre of the West Ukrainian territory of this culture (Hawinskyj, Rybicka 2021). Currently, it belongs to a small group of FBC sites in Western Ukraine that have been excavated. The information presented by M. Peleshschyshyn, who conducted excavations in Vynnyky-Lysivka, regarding the relationship between the FBC community and the Tripolye culture (TC) was particularly interesting (see Rybicka 2017). He believed that Vynnyky recorded the coexistence of their population (Peleshschyshyn 1998a, 191). The burnt daub clusters discovered at that time could probably have been the remains of dwellings. The aim of the research carried out since 2016 was to verify the hypotheses of M. Peleshschyshyn (1998a) in the context of the FBC-TC relationship (Hawinskyj, Rybicka 2021) and to establish the date of the settlement usage in Vynnyky-Lysivka. Radiocarbon dating obtained for the samples from the features from the northern zone of the settlement, allow the researchers to be placed Vynnyky-Lysivka in the period 3500–3360 BC and synchronized with the third phase of the southeastern FBC group.
Social Role-Aware Emotion Contagion in Image Social Networks
Authors: Yang Yang , Jia Jia , Wu Boya , Tang Jie
Published: June 2022

Psychological theories suggest that emotion represents the state of mind and instinctive responses of one’s cognitive system (Cannon 1927). Emotions are a complex state of feeling that results in physical and psychological changes that influence our behavior. In this paper, we study an interesting problem of emotion contagion in social networks. In particular, by employing an image social network (Flickr) as the basis of our study, we try to unveil how users’ emotional statuses influence each other and how users’ positions in the social network affect their influential strength on emotion. We develop a probabilistic framework to formalize the problem into a role-aware contagion model. The model is able to predict users’ emotional statuses based on their historical emotional statuses and social structures. Experiments on a large Flickr dataset show that the proposed model significantly outperforms (+31% in terms of F1-score) several alternative methods in predicting users’ emotional status. We also discover several intriguing phenomena. For example, the probability that a user feels happy is roughly linear to the number of friends who are also happy; but taking a closer look, the happiness probability is superlinear to the number of happy friends who act as opinion leaders (Page et al. 1999) in the network and sublinear in the number of happy friends who span structural holes (Burt 2001). This offers a new opportunity to understand the underlying mechanism of emotional contagion in online social networks.
Computing Rational Decisions In Extensive Games With Limited Foresight
Authors: Turrini Paolo
Published: June 2022

We introduce a class of extensive form games whereplayers might not be able to foresee the possible consequences of their decisions and form a model of theiropponents which they exploit to achieve a more profitable outcome. We improve upon existing models ofgames with limited foresight, endowing players with theability of higher order reasoning and proposing a novelsolution concept to address intuitions coming from realgame play. We analyse the resulting equilibria, devisingan effective procedure to compute them.
Sequence-Form and Evolutionary Dynamics: Realization Equivalence to Agent Form and Logit Dynamics
Authors: Gatti Nicola , Restelli Marcello
Published: June 2022

Evolutionary game theory provides the principal tools to model the dynamics of multi-agent learning algorithms. While there is a long-standing literature on evolutionary game theory in strategic-form games, in the case of extensive-form games few results are known and the exponential size of the representations currently adopted makes the evolutionary analysis of such games unaffordable. In this paper, we focus on dynamics for the sequence form of extensive-form games, providing three dynamics: one realization equivalent to the normal-form logit dynamic, one realization equivalent to the agent-form replicator dynamic, and one realization equivalent to the agent-form logit dynamic. All the considered dynamics require polynomial time and space, providing an exponential compression w.r.t. the dynamics currently known and providing thus tools that can be effectively employed in practice. Moreover, we use our tools to compare the agent-form and normal-form dynamics and to provide new "hybrid" dynamics.
One Size Does Not Fit All: A Game-Theoretic Approach for Dynamically and Effectively Screening for Threats
Authors: Brown Matthew , Sinha Arunesh , Schlenker Aaron , Tambe Milind
Published: June 2022

An effective way of preventing attacks in secure areas is to screen for threats (people, objects) before entry, e.g., screening of airport passengers. However, screening every entity at the same level may be both ineffective and undesirable. The challenge then is to find a dynamic approach for randomized screening, allowing for more effective use of limited screening resources, leading to improved security. We address this challenge with the following contributions: (1) a threat screening game (TSG) model for general screening domains; (2) an NP-hardness proof for computing the optimal strategy of TSGs; (3) a scheme for decomposing TSGs into subgames to improve scalability; (4) a novel algorithm that exploits a compact game representation to efficiently solve TSGs, providing the optimal solution under certain conditions; and (5) an empirical comparison of our proposed algorithm against the current state-of-the-art optimal approach for large-scale game-theoretic resource allocation problems.
Reinstating Combinatorial Protections for Manipulation and Bribery in Single-Peaked and Nearly Single-Peaked Electorates
Authors: Menon Vijay , Larson Kate
Published: June 2022

Understanding when and how computational complexity can be used to protect elections against different manipulative actions has been a highly active research area over the past two decades. A recent body of work, however, has shown that many of the NP-hardness shields, previously obtained, vanish when the electorate has single-peaked or nearly single-peaked preferences. In light of these results, we investigate whether it is possible to reimpose NP-hardness shields for such electorates by allowing the voters to specify partial preferences instead of insisting they cast complete ballots. In particular, we show that in single-peaked and nearly single-peaked electorates, if voters are allowed to submit top-truncated ballots, then the complexity of manipulation and bribery for many voting rules increases from being in P to being NP-complete.
Optimizing Personalized Email Filtering Thresholds to Mitigate Sequential Spear Phishing Attacks
Authors: Zhao Mengchen , An Bo , Kiekintveld Christopher
Published: June 2022

Highly targeted spear phishing attacks are increasingly common, and have been implicated in many major security breeches. Email filtering systems are the first line of defense against such attacks. These filters are typically configured with uniform thresholds for deciding whether or not to allow a message to be delivered to a user. However, users have very significant differences in both their susceptibility to phishing attacks as well as their access to critical information and credentials that can cause damage. Recent work has considered setting personalized thresholds for individual users based on a Stackelberg game model. We consider two important extensions of the previous model. First, in our model user values can be substitutable, modeling cases where multiple users provide access to the same information or credential. Second, we consider attackers who make sequential attack plans based on the outcome of previous attacks. Our analysis starts from scenarios where there is only one credential and then extends to more general scenarios with multiple credentials. For single-credential scenarios, we demonstrate that the optimal defense strategy can be found by solving a binary combinatorial optimization problem called PEDS. For multiple-credential scenarios, we formulate it as a bilevel optimization problem for finding the optimal defense strategy and then reduce it to a single level optimization problem called PEMS using complementary slackness conditions. Experimental results show that both PEDS and PEMS lead to significant higher defender utilities than two existing benchmarks in different parameter settings. Also, both PEDS and PEMS are more robust than the existing benchmarks considering uncertainties.
Blind, Greedy, and Random: Algorithms for Matching and Clustering Using Only Ordinal Information
Authors: Anshelevich Elliot , Sekar Shreyas
Published: June 2022

We study the Maximum Weighted Matching problem in a partial information setting where the agents' utilities for being matched to other agents are hidden and the mechanism only has access to ordinal preference information. Our model is motivated by the fact that in many settings, agents cannot express the numerical values of their utility for different outcomes, but are still able to rank the outcomes in their order of preference. Specifically, we study problems where the ground truth exists in the form of a weighted graph, and look to design algorithms that approximate the true optimum matching using only the preference orderings for each agent (induced by the hidden weights) as input. If no restrictions are placed on the weights, then one cannot hope to do better than the simple greedy algorithm, which yields a half optimal matching. Perhaps surprisingly, we show that by imposing a little structure on the weights, we can improve upon the trivial algorithm significantly: we design a 1.6-approximation algorithm for instances where the hidden weights obey the metric inequality. Our algorithm is obtained using a simple but powerful framework that allows us to combine greedy and random techniques in unconventional ways. These results are the first non-trivial ordinal approximation algorithms for such problems, and indicate that we can design robust matchings even when we are agnostic to the precise agent utilities.
Refining Subgames in Large Imperfect Information Games
Authors: Moravcik Matej , Schmid Martin , Ha Karel , Hladik Milan , Gaukrodger Stephen
Published: June 2022

The leading approach to solving large imperfect information games is to pre-calculate an approximate solution using a simplified abstraction of the full game; that solution is then used to play the original, full-scale game. The abstraction step is necessitated by the size of the game tree. However, as the original game progresses, the remaining portion of the tree (the subgame) becomes smaller. An appealing idea is to use the simplified abstraction to play the early parts of the game and then, once the subgame becomes tractable, to calculate a solution using a finer-grained abstraction in real time, creating a combined final strategy. While this approach is straightforward for perfect information games, it is a much more complex problem for imperfect information games. If the subgame is solved locally, the opponent can alter his play in prior to this subgame to exploit our combined strategy. To prevent this, we introduce the notion of subgame margin, a simple value with appealing properties. If any best response reaches the subgame, the improvement of exploitability of the combined strategy is (at least) proportional to the subgame margin. This motivates subgame refinements resulting in large positive margins. Unfortunately, current techniques either neglect subgame margin (potentially leading to a large negative subgame margin and drastically more exploitable strategies), or guarantee only non-negative subgame margin (possibly producing the original, unrefined strategy, even if much stronger strategies are possible). Our technique remedies this problem by maximizing the subgame margin and is guaranteed to find the optimal solution. We evaluate our technique using one of the top participants of the AAAI-14 Computer Poker Competition, the leading playground for agents in imperfect information setting
Ad Auctions and Cascade Model: GSP Inefficiency and Algorithms
Authors: Farina Gabriele , Gatti Nicola
Published: June 2022

The design of the best economic mechanism for Sponsored Search Auctions (SSAs) is a central task in computational mechanism design/game theory. Two open questions concern (i) the adoption of user models more accurate than the currently used one and (ii) the choice between Generalized Second Price auction (GSP) and Vickrey–Clark–Groves mechanism (VCG). In this paper, we provide some contributions to answer these questions. We study Price of Anarchy (PoA) and Price of Stability (PoS) over social welfare and auctioneer’s revenue of GSP w.r.t. the VCG when the users follow the famous cascade model. Furthermore, we provide exact, randomized, and approximate algorithms, showing that in real–world settings (Yahoo! Webscope A3 dataset, 10 available slots) optimal allocations can be found in less than 1s with up to 1,000 ads, and can be approximated in less than 20ms even with more than 1,000 ads with an average accuracy greater than 99%.
Using Correlated Strategies for Computing Stackelberg Equilibria in Extensive-Form Games
Authors: Cermak Jiri , Bosansky Branislav , Durkota Karel , Lisy Viliam , Kiekintveld Christopher
Published: June 2022

Strong Stackelberg Equilibrium (SSE) is a fundamental solution concept in game theory in which one player commits to a strategy, while the other player observes this commitment and plays a best response. We present a new algorithm for computing SSE for two-player extensive-form general-sum games with imperfect information (EFGs) where computing SSE is an NP-hard problem. Our algorithm is based on a correlated version of SSE, known as Stackelberg Extensive-Form Correlated Equilibrium (SEFCE). Our contribution is therefore twofold: (1) we give the first linear program for computing SEFCE in EFGs without chance, (2) we repeatedly solve and modify this linear program in a systematic search until we arrive to SSE. Our new algorithm outperforms the best previous algorithms by several orders of magnitude.
Personalized Alert Agent for Optimal User Performance
Authors: Shvartzon Avraham , Azaria Amos , Kraus Sarit , Goldman Claudia , Meyer Joachim , Tsimhoni Omer
Published: June 2022

Preventive maintenance is essential for the smooth operation of any equipment. Still, people occasionally do not maintain their equipment adequately. Maintenance alert systems attempt to remind people to perform maintenance. However, most of these systems do not provide alerts at the optimal timing, and nor do they take into account the time required for maintenance or compute the optimal timing for a specific user. We model the problem of maintenance performance, assuming maintenance is time consuming. We solve the optimal policy for the user, i.e., the optimal timing for a user to perform maintenance. This optimal strategy depends on the value of user's time, and thus it may vary from user to user and may change over time. %We present a game Based on the solved optimal strategy we present a personalized maintenance agent, which, depending on the value of user's time, provides alerts to the user when she should perform maintenance. In an experiment using a spaceship computer game, we show that receiving alerts from the personalized alert agent significantly improves user performance.
Submodular Optimization with Routing Constraints
Authors: Zhang Haifeng , Vorobeychik Yevgeniy
Published: June 2022

Submodular optimization, particularly under cardinality or cost constraints, has received considerable attention, stemming from its breadth of application, ranging from sensor placement to targeted marketing. However, the constraints faced in many real domains are more complex. We investigate an important and very general class of problems of maximizing a submodular function subject to general cost constraints, especially focusing on costs coming from route planning. Canoni- cal problems that motivate our framework include mobile robotic sensing, and door-to-door marketing. We propose a generalized cost-benefit (GCB) greedy al- gorithm for our problem, and prove bi-criterion approximation guarantees under significantly weaker assumptions than those in related literature. Experimental evaluation on realistic mobile sensing and door-to-door marketing problems, as well as using simulated networks, show that our algorithm achieves significantly higher utility than state-of-the-art alternatives, and has either lower or competitive running time.
Two Efficient Local Search Algorithms for Maximum Weight Clique Problem
Authors: Wang Yiyuan , Cai Shaowei , Yin Minghao
Published: June 2022

The Maximum Weight Clique problem (MWCP) is an important generalization of the Maximum Clique problem with wide applications. This paper introduces two heuristics and develops two local search algorithms for MWCP. Firstly, we propose a heuristic called strong configuration checking (SCC), which is a new variant of a recent powerful strategy called configuration checking (CC) for reducing cycling in local search. Based on the SCC strategy, we develop a local search algorithm named LSCC. Moreover, to improve the performance on massive graphs, we apply a low-complexity heuristic called Best from Multiple Selection (BMS) to select the swapping vertex pair quickly and effectively. The BMS heuristic is used to improve LSCC, resulting in the LSCC+BMS algorithm. Experiments show that the proposed algorithms outperform the state-of-the-art local search algorithm MN/TS and its improved version MN/TS+BMS on the standard benchmarks namely DIMACS and BHOSLIB, as well as a wide range of real world massive graphs.
Tiebreaking Strategies for A* Search: How to Explore the Final Frontier
Authors: Asai Masataro , Fukunaga Alex
Published: June 2022

Despite recent improvements in search techniques for cost-optimal classical planning, the exponential growth of the size of the search frontier in A* is unavoidable. We investigate tiebreaking strategies for A*, experimentally analyzing the performance of standard tiebreaking strategies that break ties according to the heuristic value of the nodes. We find that tiebreaking has a significant impact on search algorithm performance when there are zero-cost operators that induce large plateau regions in the search space. We develop a new framework for tiebreaking based on a depth metric which measures distance from the entrance to the plateau, and propose a new, randomized strategy which significantly outperforms standard strategies on domains with zero-cost actions.
Local Search for Hard SAT Formulas: The Strength of the Polynomial Law
Authors: Liu Sixue , Papakonstantinou Periklis
Published: June 2022

Random k-CNF formulas at the anticipated k-SAT phase-transition point are prototypical hard k-SAT instances. We develop a stochastic local search algorithm and study it both theoretically and through a large-scale experimental study. The algorithm comes as a result of a systematic study that contrasts rates at which a certain measure concentration phenomenon occurs. This study yields a new stochastic rule for local search. A strong point of our contribution is the conceptual simplicity of our algorithm. More importantly, the empirical results overwhelmingly indicate that our algorithm outperforms the state-of-the-art. This includes a number of winners and medalist solvers from the recent SAT Competitions.
Locally Adaptive Translation for Knowledge Graph Embedding
Authors: Jia Yantao , Wang Yuanzhuo , Lin Hailun , Jin Xiaolong , Cheng Xueqi
Published: June 2022

Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE and TransH, learn embedding representation by defining a global margin-based loss function over the data. However, the optimal loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidate loss functions, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this paper, we propose a locally adaptive translation method for knowledge graph embedding, called TransA, to find the optimal loss function by adaptively determining its margin over different knowledge graphs. Experiments on two benchmark data sets demonstrate the superiority of the proposed method, as compared to the-state-of-the-art ones.
On the Containment of SPARQL Queries under Entailment Regimes
Authors: Chekol Melisachew Wudage
Published: June 2022

Most description logics (DL) query languages allow instance retrieval from an ABox. However, SPARQL is a schema query language allowing access to the TBox (in addition to the ABox). Moreover, its entailment regimes enable to take into account knowledge inferred from knowledge bases in the query answering process. This provides a new perspective for the containment problem. In this paper, we study the containment of SPARQL queries over OWL EL axioms under entailment. OWL EL is the language used by many large scale ontologies and is based on EL++. The main contribution is a novel approach to rewriting queries using SPARQL property paths and the μ-calculus in order to reduce containment test under entailment into validity check in the μ-calculus.
Learning Abductive Reasoning Using Random Examples
Authors: Juba Brendan
Published: June 2022

We consider a new formulation of abduction in which degrees of "plausibility" of explanations, along with the rules of the domain, are learned from concrete examples (settings of attributes). Our version of abduction thus falls in the "learning to reason" framework of Khardon and Roth. Such approaches enable us to capture a natural notion of "plausibility" in a domain while avoiding the extremely difficult problem of specifying an explicit representation of what is "plausible." We specifically consider the question of which syntactic classes of formulas have efficient algorithms for abduction. We find that the class of k-DNF explanations can be found in polynomial time for any fixed k; but, we also find evidence that even weak versions of our abduction task are intractable for the usual class of conjunctions. This evidence is provided by a connection to the usual, inductive PAC-learning model proposed by Valiant. We also consider an exception-tolerant variant of abduction. We observe that it is possible for polynomial-time algorithms to tolerate a few adversarially chosen exceptions, again for the class of k-DNF explanations. All of the algorithms we study are particularly simple, and indeed are variants of a rule proposed by Mill.
A First-Order Logic of Probability and Only Knowing in Unbounded Domains
Authors: Belle Vaishak , Lakemeyer Gerhard , Levesque Hector
Published: June 2022

Only knowing captures the intuitive notion that the beliefs of an agent are precisely those that follow from its knowledge base. It has previously been shown to be useful in characterizing knowledge-based reasoners, especially in a quantified setting. While this allows us to reason about incomplete knowledge in the sense of not knowing whether a formula is true or not, there are many applications where one would like to reason about the degree of belief in a formula. In this work, we propose a new general first-order account of probability and only knowing that admits knowledge bases with incomplete and probabilistic specifications. Beliefs and non-beliefs are then shown to emerge as a direct logical consequence of the sentences of the knowledge base at a corresponding level of specificity.
Global Model Checking on Pushdown Multi-Agent Systems
Authors: Chen Taolue , Song Fu , Wu Zhilin
Published: June 2022

Pushdown multi-agent systems, modeled by pushdown game structures (PGSs), are an important paradigm of infinite-state multi-agent systems. Alternating-time temporal logics are well-known specification formalisms for multi-agent systems, where the selective path quantifier is introduced to reason about strategies of agents. In this paper, we investigate model checking algorithms for variants of alternating-time temporal logics over PGSs, initiated by Murano and Perelli at IJCAI'15. We first give a triply exponential-time model checking algorithm for ATL* over PGSs. The algorithm is based on the saturation method, and is the first global model checking algorithm with a matching lower bound. Next, we study the model checking problem for the alternating-time mu-calculus. We propose an exponential-time global model checking algorithm which extends similar algorithms for pushdown systems and modal mu-calculus. The algorithm admits a matching lower bound, which holds even for the alternation-free fragment and ATL.
Multi-Variable Agents Decomposition for DCOPs
Authors: Fioretto Ferdinando , Yeoh William , Pontelli Enrico
Published: June 2022

The application of DCOP models to large problems faces two main limitations: (i) Modeling limitations, as each agent can handle only a single variable of the problem; and (ii) Resolution limitations, as current approaches do not exploit the local problem structure withineach agent. This paper proposes a novel Multi-Variable Agent (MVA) DCOP decompositiontechnique, which: (i) Exploits the co-locality of each agent's variables, allowing us to adopt efficient centralized techniques within each agent; (ii) Enables the use of hierarchical parallel models and proposes the use of GPUs; and (iii) Reduces the amount of computation and communication required in several classes of DCOP algorithms.
Learning for Decentralized Control of Multiagent Systems in Large, Partially-Observable Stochastic Environments
Authors: Liu Miao , Amato Christopher , Anesta Emily , Griffith John , How Jonathan
Published: June 2022

Decentralized partially observable Markov decision processes (Dec-POMDPs) provide a general framework for multiagent sequential decision-making under uncertainty. Although Dec-POMDPs are typically intractable to solve for real-world problems, recent research on macro-actions (i.e., temporally-extended actions) has significantly increased the size of problems that can be solved. However, current methods assume the underlying Dec-POMDP model is known a priori or a full simulator is available during planning time. To accommodate more realistic scenarios, when such information is not available, this paper presents a policy-based reinforcement learning approach, which learns the agent policies based solely on trajectories generated by previous interaction with the environment (e.g., demonstrations). We show that our approach is able to generate valid macro-action controllers and develop an expectationmaximization (EM) algorithm (called Policy-based EM or PoEM), which has convergence guarantees for batch learning. Our experiments show PoEM is a scalable learning method that can learn optimal policies and improve upon hand-coded “expert” solutions.
Exploiting an Oracle That Reports AUC Scores in Machine Learning Contests
Authors: Whitehill Jacob
Published: June 2022

In machine learning contests such as the ImageNet Large Scale Visual Recognition Challenge and the KDD Cup, contestants can submit candidate solutions and receive from an oracle (typically the organizers of the competition) the accuracy of their guesses compared to the ground-truth labels. One of the most commonly used accuracy metrics for binary classification tasks is the Area Under the Receiver Operating Characteristics Curve (AUC). In this paper we provide proofs-of-concept of how knowledge of the AUC of a set of guesses can be used, in two different kinds of attacks, to improve the accuracy of those guesses. On the other hand, we also demonstrate the intractability of one kind of AUC exploit by proving that the number of possible binary labelings of n examples for which a candidate solution obtains a AUC score of c grows exponentially in n, for every c in (0,1).
Submodular Asymmetric Feature Selection in Cascade Object Detection
Authors: Yu Baosheng , Fang Meng , Tao Dacheng , Yin Jie
Published: June 2022

A cascade classifier has turned out to be effective insliding-window based real-time object detection. In acascade classifier, node learning is the key process,which includes feature selection and classifier design. Previous algorithms fail to effectively tackle the asymmetry and intersection problems existing in cascade classification, thereby limiting the performance of object detection. In this paper, we improve current feature selection algorithm by addressing both asymmetry and intersection problems. We formulate asymmetric feature selection as a submodular function maximization problem. We then propose a new algorithm SAFS with formal performance guarantee to solve this problem.We use face detection as a case study and perform experiments on two real-world face detection datasets. The experimental results demonstrate that our algorithm SAFS outperforms the state-of-art feature selection algorithms in cascade object detection, such as FFS and LACBoost.
Learning Deep Convolutional Neural Networks for X-Ray Protein Crystallization Image Analysis
Authors: Yann Margot , Tang Yichuan
Published: June 2022

Obtaining a protein's 3D structure is crucial to the understanding of its functions and interactions with other proteins. It is critical to accelerate the protein crystallization process with improved accuracy for understanding cancer and designing drugs. Systematic high-throughput approaches in protein crystallization have been widely applied, generating a large number of protein crystallization-trial images. Therefore, an efficient and effective automatic analysis for these images is a top priority. In this paper, we present a novel system, CrystalNet, for automatically labeling outcomes of protein crystallization-trial images. CrystalNet is a deep convolutional neural network that automatically extracts features from X-ray protein crystallization images for classification. We show that (1) CrystalNet can provide real-time labels for crystallization images effectively, requiring approximately 2 seconds to provide labels for all 1536 images of crystallization microassay on each plate; (2) compared with the state-of-the-art classification systems in crystallization image analysis, our technique demonstrates an improvement of 8% in accuracy, and achieve 90.8% accuracy in classification. As a part of the high-throughput pipeline which generates millions of images a year, CrystalNet can lead to a substantial reduction of labor-intensive screening.
Learning with Marginalized Corrupted Features and Labels Together
Authors: Li Yingming , Yang Ming , Xu Zenglin , Zhang Zhongfei
Published: June 2022

Tagging has become increasingly important in many real-world applications noticeably including web applications, such as web blogs and resource sharing systems. Despite this importance, tagging methods often face difficult challenges such as limited training samples and incomplete labels, which usually lead to degenerated performance on tag prediction. To improve the generalization performance, in this paper, we propose Regularized Marginalized Cross-View learning (RMCV) by jointly modeling on attribute noise and label noise. In more details, the proposed model constructs infinite training examples with attribute noises from known exponential-family distributions and exploits label noise via marginalized denoising autoencoder. Therefore, the model benefits from its robustness and alleviates the problem of tag sparsity. While RMCV is a general method for learning tagging, in the evaluations we focus on the specific application of multi-label text tagging. Extensive evaluations on three benchmark data sets demonstrate that RMCV outstands with a superior performance in comparison with state-of-the-art methods.
Learning to Appreciate the Aesthetic Effects of Clothing
Authors: Jia Jia , Huang Jie , Shen Guangyao , He Tao , Liu Zhiyuan , Luan Huanbo , Yan Chao
Published: June 2022

How do people describe clothing? The words like “formal”or "casual" are usually used. However, recent works often focus on recognizing or extracting visual features (e.g., sleeve length, color distribution and clothing pattern) from clothing images accurately. How can we bridge the gap between the visual features and the aesthetic words? In this paper, we formulate this task to a novel three-level framework: visual features(VF) - image-scale space (ISS) - aesthetic words space(AWS). Leveraging the art-field image-scale space served as an intermediate layer, we first propose a Stacked Denoising Autoencoder Guided by CorrelativeLabels (SDAE-GCL) to map the visual features to the image-scale space; and then according to the semantic distances computed byWordNet::Similarity, we map the most often used aesthetic words in online clothing shops to the image-scale space too. Employing upper body menswear images downloaded from several global online clothing shops as experimental data, the results indicate that the proposed three-level framework can help to capture the subtle relationship between visual features and aesthetic words better compared to several baselines. To demonstrate that our three-level framework and its implementation methods are universally applicable, we finally present some interesting analyses on the fashion trend of menswear in the last 10 years.
Towards Domain Adaptive Vehicle Detection in Satellite Image by Supervised Super-Resolution Transfer
Authors: Cao Liujuan , Ji Rongrong , Wang Cheng , Li Jonathan
Published: June 2022

Vehicle detection in satellite image has attracted extensive research attentions with various emerging applications.However, the detector performance has been significantly degenerated due to the low resolutions of satellite images, as well as the limited training data.In this paper, a robust domain-adaptive vehicle detection framework is proposed to bypass both problems.Our innovation is to transfer the detector learning to the high-resolution aerial image domain,where rich supervision exists and robust detectors can be trained.To this end, we first propose a super-resolution algorithm using coupled dictionary learning to ``augment'' the satellite image region being tested into the aerial domain.Notably, linear detection loss is embedded into the dictionary learning, which enforces the augmented region to be sensitive to the subsequent detector training.Second, to cope with the domain changes, we propose an instance-wised detection using Exemplar Support Vector Machines (E-SVMs), which well handles the intra-class and imaging variations like scales, rotations, and occlusions.With comprehensive experiments on large-scale satellite image collections, we demonstrate that the proposed framework can significantly boost the detection accuracy over several state-of-the-arts.
On the Performance of GoogLeNet and AlexNet Applied to Sketches
Authors: Ballester Pedro , Araujo Ricardo
Published: June 2022

This work provides a study on how Convolutional Neural Networks, trained to identify objects primarily in photos, perform when applied to more abstract representations of the same objects. Our main goal is to better understand the generalization abilities of these networks and their learned inner representations. We show that both GoogLeNet and AlexNet networks are largely unable to recognize abstract sketches that are easily recognizable by humans. Moreover, we show that the measured efficacy vary considerably across different classes and we discuss possible reasons for this.
Instilling Social to Physical: Co-Regularized Heterogeneous Transfer Learning
Authors: Wei Ying , Zhu Yin , Leung Cane , Song Yangqiu , Yang Qiang
Published: June 2022

Ubiquitous computing tasks, such as human activity recognition (HAR), are enabling a wide spectrum of applications, ranging from healthcare to environment monitoring. The success of a ubiquitous computing task relies on sufﬁcient physical sensor data with groundtruth labels, which are always scarce due to the expensive annotating process. Meanwhile, social media platforms provide a lot of social or semantic context information. People share what they are doing and where they are frequently in the messages they post. This rich set of socially shared activities motivates us to transfer knowledge from social media to address the sparsity issue of labelled physical sensor data. In order to transfer the knowledge of social and semantic context, we propose a Co-Regularized Heterogeneous Transfer Learning (CoHTL) model, which builds a common semantic space derived from two heterogeneous domains. Our proposed method outperforms state-of-the-art methods on two ubiquitous computing tasks, namely human activity recognition and region function discovery.
Drosophila Gene Expression Pattern Annotations via Multi-Instance Biological Relevance Learning
Authors: Wang Hua , Deng Cheng , Zhang Hao , Gao Xinbo , Huang Heng
Published: June 2022

Recent developments in biologyhave produced a large number of gene expression patterns, many of which have been annotated textually with anatomical and developmental terms. These terms spatially correspond to local regions of the images, which are attached collectively to groups of images. Because one does not know which term is assigned to which region of which image in the group, the developmental stage classification and anatomical term annotation turn out to be a multi-instance learning (MIL) problem, which considers input as bags of instances and labels are assigned to the bags. Most existing MIL methods routinely use the Bag-to-Bag (B2B) distances, which, however, are often computationally expensive and may not truly reflect the similarities between the anatomical and developmental terms. In this paper, we approach the MIL problem from a new perspective using the Class-to-Bag (C2B) distances, which directly assesses the relations between annotation terms and image panels. Taking into account the two challenging properties of multi-instance gene expression data, high heterogeneity and weak label association, we computes the C2B distance by introducing class specific distance metrics and locally adaptive significance coefficients.We apply our new approach to automatic gene expression pattern classification and annotation on the Drosophila melanogaster species. Extensive experiments have demonstrated the effectiveness of our new method.
Column Sampling Based Discrete Supervised Hashing
Authors: Kang Wang-Cheng , Li Wu-Jun , Zhou Zhi-Hua
Published: June 2022

By leveraging semantic (label) information, supervised hashing has demonstrated better accuracy than unsupervised hashing in many real applications. Because the hashing-code learning problem is essentially a discrete optimization problem which is hard to solve, most existing supervised hashing methods try to solve a relaxed continuous optimization problem by dropping the discrete constraints. However, these methods typically suffer from poor performance due to the errors caused by the relaxation. Some other methods try to directly solve the discrete optimization problem. However, they are typically time-consuming and unscalable. In this paper, we propose a novel method, called column sampling based discrete supervised hashing (COSDISH), to directly learn the discrete hashing code from semantic information. COSDISH is an iterative method, in each iteration of which several columns are sampled from the semantic similarity matrix and then the hashing code is decomposed into two parts which can be alternately optimized in a discrete way. Theoretical analysis shows that the learning (optimization) algorithm of COSDISH has a constant-approximation bound in each step of the alternating optimization procedure. Empirical results on datasets with semantic labels illustrate that COSDISH can outperform the state-of-the-art methods in real applications like image retrieval.