arXiv subjects

Haibin Wang

Publications and source records attributed to Haibin Wang.

At least 37 records · Page 2Linked to original sources

arXiv2020

Probabilistic Serial Mechanism for Multi-Type Resource Allocation

In multi-type resource allocation (MTRA) problems, there are p $\ge$ 2 types of items, and n agents, who each demand one unit of items of each type, and have strict linear preferences over bundles consisting of one item of each type. For MTRAs with indivisible items, our first result is an impossibility theorem that is in direct contrast to the single type (p = 1) setting: No mechanism, the output of which is always decomposable into a probability distribution over discrete assignments (where no item is split between agents), can satisfy both sd-efficiency and sd-envy-freeness. To circumvent this impossibility result, we consider the natural assumption of lexicographic preference, and provide an extension of the probabilistic serial (PS), called lexicographic probabilistic serial (LexiPS).We prove that LexiPS satisfies sd-efficiency and sd-envy-freeness, retaining the desirable properties of PS. Moreover, LexiPS satisfies sd-weak-strategyproofness when agents are not allowed to misreport their importance orders. For MTRAs with divisible items, we show that the existing multi-type probabilistic serial (MPS) mechanism satisfies the stronger efficiency notion of lexi-efficiency, and is sd-envy-free under strict linear preferences, and sd-weak-strategyproof under lexicographic preferences. We also prove that MPS can be characterized both by leximin-ptimality and by item-wise ordinal fairness, and the family of eating algorithms which MPS belongs to can be characterized by no-generalized-cycle condition.

cs.GT↗

arXiv2019

Deep-learning source localization using multi-frequency magnitude-only data

A deep learning approach based on big data is proposed to locate broadband acoustic sources using a single hydrophone in ocean waveguides with uncertain bottom parameters. Several 50-layer residual neural networks, trained on a huge number of sound field replicas generated by an acoustic propagation model, are used to handle the bottom uncertainty in source localization. A two-step training strategy is presented to improve the training of the deep models. First, the range is discretized in a coarse (5 km) grid. Subsequently, the source range within the selected interval and source depth are discretized on a finer (0.1 km and 2 m) grid. The deep learning methods were demonstrated for simulated magnitude-only multi-frequency data in uncertain environments. Experimental data from the China Yellow Sea also validated the approach.

physics.ao-ph↗

arXiv2018

Data Motif-based Proxy Benchmarks for Big Data and AI Workloads

For the architecture community, reasonable simulation time is a strong requirement in addition to performance data accuracy. However, emerging big data and AI workloads are too huge at binary size level and prohibitively expensive to run on cycle-accurate simulators. The concept of data motif, which is identified as a class of units of computation performed on initial or intermediate data, is the first step towards building proxy benchmark to mimic the real-world big data and AI workloads. However, there is no practical way to construct a proxy benchmark based on the data motifs to help simulation-based research. In this paper, we embark on a study to bridge the gap between data motif and a practical proxy benchmark. We propose a data motif-based proxy benchmark generating methodology by means of machine learning method, which combine data motifs with different weights to mimic the big data and AI workloads. Furthermore, we implement various data motifs using light-weight stacks and apply the methodology to five real-world workloads to construct a suite of proxy benchmarks, considering the data types, patterns, and distributions. The evaluation results show that our proxy benchmarks shorten the execution time by 100s times on real systems while maintaining the average system and micro-architecture performance data accuracy above 90%, even changing the input data sets or cluster configurations. Moreover, the generated proxy benchmarks reflect consistent performance trends across different architectures. To facilitate the community, we will release the proxy benchmarks on the project homepage http://prof.ict.ac.cn/BigDataBench.

cs.DC↗

arXiv2017

A Dwarf-based Scalable Big Data Benchmarking Methodology

Different from the traditional benchmarking methodology that creates a new benchmark or proxy for every possible workload, this paper presents a scalable big data benchmarking methodology. Among a wide variety of big data analytics workloads, we identify eight big data dwarfs, each of which captures the common requirements of each class of unit of computation while being reasonably divorced from individual implementations. We implement the eight dwarfs on different software stacks, e.g., OpenMP, MPI, Hadoop as the dwarf components. For the purpose of architecture simulation, we construct and tune big data proxy benchmarks using the directed acyclic graph (DAG)-like combinations of the dwarf components with different weights to mimic the benchmarks in BigDataBench. Our proxy benchmarks preserve the micro-architecture, memory, and I/O characteristics, and they shorten the simulation time by 100s times while maintain the average micro-architectural data accuracy above 90 percentage on both X86 64 and ARMv8 processors. We will open-source the big data dwarf components and proxy benchmarks soon.

cs.AR↗

arXiv2013

Quantum Private Comparison: A Review

As an important branch of quantum secure multiparty computation, quantum private comparison (QPC) has attracted more and more attention recently. In this paper, according to the quantum implementation mechanism that these protocols used, we divide these protocols into three categories: The quantum cryptography QPC, the superdense coding QPC, and the entanglement swapping QPC. And then, a more in-depth analysis on the research progress, design idea, and substantive characteristics of corresponding QPC categories is carried out, respectively. Finally, the applications of QPC and quantum secure multi-party computation issues are discussed and, in addition, three possible research mainstream directions are pointed out.

cs.CR↗

arXiv2012

C-PASS-PC: A Cloud-driven Prototype of Multi-Center Proactive Surveillance System for Prostate Cancer

Currently there are many clinical trials using paper case report forms as the primary data collection tool. Cloud Computing platforms provide big potential for increasing efficiency through a web-based data collection interface, especially for large-scale multi-center trials. Traditionally, clinical and biological data for multi-center trials are stored in one dedicated, centralized database system running at a data coordinating center (DCC). This paper presents C-PASS-PC, a cloud-driven prototype of multi-center proactive surveillance system for prostate cancer. The prototype is developed in PHP, JQuery and CSS with an Oracle backend in a local Web server and database server and deployed on Google App Engine (GAE) and Google Cloud SQL-MySQL. The deploying process is fast and easy to follow. The C-PASS-PC prototype can be accessed through an SSL-enabled web browser. Our approach proves the concept that cloud computing platforms such as GAE is a suitable and flexible solution in the near future for multi-center clinical trials.

cs.CE↗

arXiv2008

A Neutrosophic Description Logic

Description Logics (DLs) are appropriate, widely used, logics for managing structured knowledge. They allow reasoning about individuals and concepts, i.e. set of individuals with common properties. Typically, DLs are limited to dealing with crisp, well defined concepts. That is, concepts for which the problem whether an individual is an instance of it is yes/no question. More often than not, the concepts encountered in the real world do not have a precisely defined criteria of membership: we may say that an individual is an instance of a concept only to a certain degree, depending on the individual's properties. The DLs that deal with such fuzzy concepts are called fuzzy DLs. In order to deal with fuzzy, incomplete, indeterminate and inconsistent concepts, we need to extend the fuzzy DLs, combining the neutrosophic logic with a classical DL. In particular, concepts become neutrosophic (here neutrosophic means fuzzy, incomplete, indeterminate, and inconsistent), thus reasoning about neutrosophic concepts is supported. We'll define its syntax, its semantics, and describe its properties.

cs.AI↗

arXiv2007

Neutrosophic Relational Data Model

In this paper, we present a generalization of the relational data model based on interval neutrosophic set. Our data model is capable of manipulating incomplete as well as inconsistent information. Fuzzy relation or intuitionistic fuzzy relation can only handle incomplete information. Associated with each relation are two membership functions one is called truth-membership function T which keeps track of the extent to which we believe the tuple is in the relation, another is called falsity-membership function F which keeps track of the extent to which we believe that it is not in the relation. A neutrosophic relation is inconsistent if there exists one tuple a such that T(a) + F(a) > 1 . In order to handle inconsistent situation, we propose an operator called "split" to transform inconsistent neutrosophic relations into pseudo-consistent neutrosophic relations and do the set-theoretic and relation-theoretic operations on them and finally use another operator called "combine" to transform the result back to neutrosophic relation. For this data model, we define algebraic operators that are generalizations of the usual operators such as intersection, union, selection, join on fuzzy relations. Our data model can underlie any database and knowledge-base management system that deals with incomplete and inconsistent information.

cs.DB↗

arXiv2005

Interval Neutrosophic Sets and Logic: Theory and Applications in Computing

This book presents the advancements and applications of neutrosophics. Chapter 1 first introduces the interval neutrosophic sets which is an instance of neutrosophic sets. In this chapter, the definition of interval neutrosophic sets and set-theoretic operators are given and various properties of interval neutrosophic set are proved. Chapter 2 defines the interval neutrosophic logic based on interval neutrosophic sets including the syntax and semantics of first order interval neutrosophic propositional logic and first order interval neutrosophic predicate logic. The interval neutrosophic logic can reason and model fuzzy, incomplete and inconsistent information. In this chapter, we also design an interval neutrosophic inference system based on first order interval neutrosophic predicate logic. The interval neutrosophic inference system can be applied to decision making. Chapter 3 gives one application of interval neutrosophic sets and logic in the field of relational databases. Neutrosophic data model is the generalization of fuzzy data model and paraconsistent data model. Here, we generalize various set-theoretic and relation-theoretic operations of fuzzy data model to neutrosophic data model. Chapter 4 gives another application of interval neutrosophic logic. A soft semantic Web Services agent framework is proposed to faciliate the registration and discovery of high quality semantic Web Services agent. The intelligent inference engine module of soft Semantic Web Services agent is implemented using interval neutrosophic logic.

cs.LO↗

arXiv2004

Interval Neutrosophic Logics: Theory and Applications

In this paper, we present the interval neutrosophic logics which generalizes the fuzzy logic, paraconsistent logic, intuitionistic fuzzy logic and many other non-classical and non-standard logics. We will give the formal definition of interval neutrosophic propositional calculus and interval neutrosophic predicate calculus. Then we give one application of interval neutrosophic logics to do approximate reasoning.

cs.LO↗

arXiv2004

An Extended Generalized Disjunctive Paraconsistent Data Model for Disjunctive Information

This paper presents an extension of generalized disjunctive paraconsistent relational data model in which pure disjunctive positive and negative information as well as mixed disjunctive positive and negative information can be represented explicitly and manipulated. We consider explicit mixed disjunctive information in the context of disjunctive databases as there is an interesting interplay between these two types of information. Extended generalized disjunctive paraconsistent relation is introduced as the main structure in this model. The relational algebra is appropriately generalized to work on extended generalized disjunctive paraconsistent relations and their correctness is established.

cs.DB↗

arXiv2004

Paraconsistent Intuitionistic Fuzzy Relational Data Model

In this paper, we present a generalization of the relational data model based on paraconsistent intuitionistic fuzzy sets. Our data model is capable of manipulating incomplete as well as inconsistent information. Fuzzy relation or intuitionistic fuzzy relation can only handle incomplete information. Associated with each relation are two membership functions one is called truth-membership function $T$ which keeps track of the extent to which we believe the tuple is in the relation, another is called false-membership function which keeps track of the extent to which we believe that it is not in the relation. A paraconsistent intuitionistic fuzzy relation is inconsistent if there exists one tuple $a$ such that $T(a) + F(a) > 1$. In order to handle inconsistent situation, we propose an operator called split to transform inconsistent paraconsistent intuitionistic fuzzy relations into pseudo-consistent paraconsistent intuitionistic fuzzy relations and do the set-theoretic and relation-theoretic operations on them and finally use another operator called combine to transform the result back to paraconsistent intuitionistic fuzzy relation. For this model, we define algebraic operators that are generalisations of the usual operators such as union, selection, join on fuzzy relations. Our data model can underlie any database and knowledge-base management system that deals with incomplete and inconsistent information.

cs.DB↗

arXiv2004

A Generalized Disjunctive Paraconsistent Data Model for Negative and Disjunctive Information

This paper presents a generalization of the disjunctive paraconsistent relational data model in which disjunctive positive and negative information can be represented explicitly and manipulated. There are situations where the closed world assumption to infer negative facts is not valid or undesirable and there is a need to represent and reason with negation explicitly. We consider explicit disjunctive negation in the context of disjunctive databases as there is an interesting interplay between these two types of information. Generalized disjunctive paraconsistent relation is introduced as the main structure in this model. The relational algebra is appropriately generalized to work on generalized disjunctive paraconsistent relations and their correctness is established.

cs.DB↗

arXiv2004

Interval Neutrosophic Sets

Neutrosophic set is a part of neutrosophy which studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. Neutrosophic set is a powerful general formal framework that has been recently proposed. However, neutrosophic set needs to be specified from a technical point of view. To this effect, we define the set-theoretic operators on an instance of neutrosophic set, we call it interval neutrosophic set (INS). We prove various properties of INS, which are connected to the operations and relations over INS. Finally, we introduce and prove the convexity of interval neutrosophic sets.

math.GM↗

arXiv2004

High Scale Study of Possible B \to ϕK_S CP Physics

Some rare decay processes are particularly sensitive to physics beyond the Standard Model (SM) because they have no SM tree contributions. We focus on one of these, $B_{d}\to ϕK_{s}$. Our study is in terms of the high scale effective theory, and high scale models for the underlying theory, while previous studies have been focusing on the low scale effective Lagrangian. We examine phenomenologically the high scale parameter space with full calculations, but largely report the results in terms of mass insertion techniques since they are then easily pictured. We also determine the ranges of different mass insertions that could produce large non-SM CP effects. Then we exhibit classes of high scale models that can or cannot provide large non-SM CP effects, thus demonstrating that data on $B_{d}\to ϕK_{s}$ can probe both supersymmetry breaking and the underlying high scale theory and even make relatively direct contact with string-motivated models. We provide a novel and systematic technique to understand the relations between high and low scale parameters from RGE running. We include all constraints from other data, particularly $b\to sγ$ and EDMs.

hep-ph↗

arXiv2003

B --> Phi K_S and Supersymmetry

The rare decay B --> Phi K_S is a well-known probe of physics beyond the Standard Model because it arises only through loop effects yet has the same time-dependent CP asymmetry as B --> Psi K_S. Motivated by recent data suggesting new physics in B --> Phi K_S, we look to supersymmetry for possible explanations, including contributions mediated by gluino loops and by Higgs bosons. Chirality-preserving LL and RR gluino contributions are generically small, unless gluinos and squarks masses are close to the current lower bounds. Higgs contributions are also too small to explain a large asymmetry if we impose the current upper limit on B(B_s --> mu mu). On the other hand, chirality-flipping LR and RL gluino contributions can provide sizable effects and while remaining consistent with related results in B --> Psi K_S, Delta M_s, B --> X_s gamma and other processes. We discuss how the LR and RL insertions can be distinguished using other observables, and we provide a string-based model and other estimates to show that the needed sizes of mass insertions are reasonable.

hep-ph↗

arXiv2003

B(d) --> phi K(S) CP asymmetries as an important probe of supersymmetry

The decay $B_d \to ϕK_S$ is a special probe of physics beyond the Standard Model (SM), since it has no SM tree level contribution. Motivated by recent data suggesting a deviation from the SM for its time-dependent CP asymmetry, we examine supersymmetric explanations. Chirality preserving contributions are generically small, unless gluino is relatively light. Higgs contributions are also too small to explain a large asymmetry. Chirality flipping $LR$ and $RL$ gluino contributions actually can provide sizable effects without conflict with all related results. We discuss how various insertions can be distinguished, and argue the needed sizes of mass insertions are reasonable.

hep-ph↗

arXiv1999

Running Coupling Constants of Fermions with Masses in Quantum Electro Dynamics and Quantum Chromo Dynamics

Based on a simple but effective regularization-renormalization method (RRM), the running coupling constants (RCC) of fermions with masses in quantum electrodynamics (QED) and quantum chromodynamics (QCD) are calculated by renormalization group equation (RGE). Starting at Q=0 ($Q$ being the momentum transfer), the RCC in QED increases with the increase of $Q$ whereas the RCCs for different flavors of quarks with masses in QCD are different and they increase with the decrease of $Q$ to reach a maximum at low $Q$ for each flavor of quark and then decreases to zero at $Q\longrightarrow 0$. The physical explanation is given.

hep-ph↗