arXiv subjects

Jing He

Publications and source records attributed to Jing He.

At least 73 records · Page 4Linked to original sources

arXiv2022

Localization of n-exangulated categories

Nakaoka-Ogawa-Sakai considered the localization of an extriangulated category. This construction unified the Serre quotient of abelian categories and the Verdier quotient of triangulated categories. Recently, Herschend-Liu-Nakaoka defined $n$-exangulated categories as a higher dimensional analogue of extriangulated categories. Let $\mathcal C$ be an $n$-exangulated category and $\mathcal{F}$ be a multiplicative system satisfying mild assumption. In this article, we give a necessary and sufficient condition for the localization of $\mathcal C$ be an $n$-exangulated category. This way gives a new class of $n$-exangulated categories which are neither $n$-exact nor $(n+2)$-angulated in general. Moreover, our result also generalizes work by Nakaoka-Ogawa-Sakai.

math.RT↗

arXiv2022

Reinforcement learning for multi-item retrieval in the puzzle-based storage system

Nowadays, fast delivery services have created the need for high-density warehouses. The puzzle-based storage system is a practical way to enhance the storage density, however, facing difficulties in the retrieval process. In this work, a deep reinforcement learning algorithm, specifically the Double&Dueling Deep Q Network, is developed to solve the multi-item retrieval problem in the system with general settings, where multiple desired items, escorts, and I/O points are placed randomly. Additionally, we propose a general compact integer programming model to evaluate the solution quality. Extensive numerical experiments demonstrate that the reinforcement learning approach can yield high-quality solutions and outperforms three related state-of-the-art heuristic algorithms. Furthermore, a conversion algorithm and a decomposition framework are proposed to handle simultaneous movement and large-scale instances respectively, thus improving the applicability of the PBS system.

cs.LG↗

arXiv2021

Auslander-Reiten-Serre duality for n-exangulated categories

Let $(\mathscr{C},\mathbb{E},\mathfrak{s})$ be an Ext-finite, Krull-Schmidt and $k$-linear $n$-exangulated category with $k$ a commutative artinian ring. In this note, we prove that $\mathscr{C}$ has Auslander-Reiten-Serre duality if and only if $\mathscr{C}$ has Auslander-Reiten $n$-exangles. Moreover, we also give an equivalent condition for the existence of Serre duality (which is a special type of Auslander-Reiten-Serre duality). Finally, assume further that $\mathscr{C}$ has Auslander-Reiten-Serre duality. We exploit a bijection triangle, which involves the restricted Auslander bijection and the Auslander-Reiten-Serre duality.

math.RT↗

arXiv2021

Higher Auslander-Reiten sequences via morphisms determined by objects

Let $(\mathscr{C},\mathbb{E},\mathfrak{s})$ be an ${\rm Ext}$-finite, Krull-Schmidt and $k$-linear $n$-exangulated category with $k$ a commutative artinian ring. In this note, we define two additive subcategories $\mathscr{C}_r$ and $\mathscr{C}_l$ of $\mathscr{C}$ in terms of the representable functors from the stable category of $\mathscr{C}$ to the category of finitely generated $k$-modules. Moreover, we show that there exists an equivalence between the stable categories of these two full subcategories. Finally, we give some equivalent characterizations on the existence of Auslander-Reiten $n$-exangles via determined morphisms. These results unify and extend their works by Jiao-Le for exact categories, Zhao-Tan-Huang for extriangulated categories, Xie-Liu-Yang for $n$-abelian categories.

math.RT↗

arXiv2021

One-sided n-suspended categories

Let $n$ be an integer greater or equal than $3$. We give a simultaneous generalization of $(n-2)$-exact categories and $n$-angulated categories, and we call it one-sided $n$-suspended categories. One-sided $n$-angulated categories are also examples of one-sided $n$-suspended categories. We provide a general framework for passing from one-sided $n$-suspended categories to one-sided $n$-angulated categories. Besides, we give a method to construct $n$-angulated quotient categories from Frobenius $n$-prile categories. These results generalize their works by Jasso for $n$-exact categories, Lin for $(n+2)$-angulated categories and Li for one-sided suspended categories.

math.RT↗

arXiv2021

Linear Adjusting Programming in Factor Space

The definition of factor space and a unified optimization based classification model were developed for linear programming. Intelligent behaviour appeared in a decision process can be treated as a point y, the dynamic state observed and controlled by the agent, moving in a factor space impelled by the goal factor and blocked by the constraint factors. Suppose that the feasible region is cut by a group of hyperplanes, when point y reaches the region's wall, a hyperplane will block the moving and the agent needs to adjust the moving direction such that the target is pursued as faithful as possible. Since the wall is not able to be represented to a differentiable function, the gradient method cannot be applied to describe the adjusting process. We, therefore, suggest a new model, named linear adjusting programming (LAP) in this paper. LAP is similar as a kind of relaxed linear programming (LP), and the difference between LP and LAP is: the former aims to find out the ultimate optimal point, while the latter just does a direct action in short period. You may ask: Where will a blocker encounter? How can the moving direction be adjusted? Where further blockers may be encountered next, and how should the direction be adjusted again? We request at least an adjusting should be achieved at the first time. If a hyperplane blocks y going ahead along with the direction d, then we must adjust the new direction d' as the projection of g in the blocking plane. If there is only one blocker at a time, it is straightforward to calculate the projection, but how to calculate the projection when there are more than one blocker encountered simultaneously? We suggest a projection calculation by means of the Hat matrix in this paper. Linear adjusting programming will attract interest in many fields. It might bring a new light to solve the linear programming problem with a strong polynomial solution.

math.OC↗

arXiv2020

How to Eliminate Detour Behaviors in E-hailing? Real-time Detecting and Time-dependent Pricing

With the rapid development of information and communication technology (ICT), taxi business becomes a typical electronic commerce mode. However, one traditional problem still exists in taxi service, that greedy taxi drivers may deliberately take unnecessary detours to overcharge passengers. The detection of these fraudulent behaviors is essential to ensure high-quality taxi service. In this paper, we propose a novel framework for detecting and analyzing the detour behaviors both in off-line database and among on-line trips. Applying our framework to real-world taxi data-set, a remarkable performance (AUC surpasses 0.98) has been achieved in off-line classification. Meanwhile, we further extend the off-line methods to on-line detection, a warning mechanism is introduced to remind drivers and an excellent precision (AUC surpasses 0.90) also has arrived in this phases. After conducting extensive experiments to verify the relationships between pricing regulations and detour behaviors, some quantitative pricing suggestions, including rising base fare and reducing distance-based fare rate, are provided to eliminate detour behaviors from the long term.

cs.CY↗

arXiv2019

Viscoelastic properties and flow instabilities of aqueous suspensions of cellulosic fibers

Processing of concentrated lignocellulosic biomass suspensions typically involves the conversion of the cellulose into sugars and sugars into ethanol. Biomass is usually pre-processed (i.e. via comminution, steam explosion, etc.) to form fine cellulosic fibers to be dispersed into an aqueous phase for further treatment. The resulting cellulose suspensions need to be pressurized and pumped into and out of various processing vessels without allowing the development of flow instabilities that are typically associated with the demixing, i.e., the segregation of the cellulosic biomass from the aqueous phase. Here, it is demonstrated that the use of a gelation agent, hydroxypropyl guar gum (HPG) at the relatively low concentration of 0.5 wt% significantly affects the development of viscoelastic material functions of cellulosic suspensions, and improves the dispersive mixing of the cellulose fibers within the aqueous phase. This results in the reduction of the flow instabilities and associated demixing effects that are ubiquitously observed during the pressurization of cellulosic suspensions in Poiseuille and compressive squeeze flows.

cond-mat.soft↗

arXiv2018

Crystallization-Arrested Viscoelastic Phase Separation in Semiconducting Polymer Gels

Through a combination of rheological characterization and temperature-variable imaging methods, a novel gelation pathway in dilute solutions of a semiconducting polymer to achieve interconnected, crystalline networks with hierarchical porosity is reported. Upon rapid cooling, solutions of regioregular poly(3-hexylthiophene) (RR-P3HT) in ortho-dichlorobenzene formed thermoreversible gels. Temperature-variable confocal microscopy revealed cooling-induced structural rearrangement to progress through viscoelastic phase separation. The phase separation process arrested prematurely during the formation of micron-sized solvent-rich "holes" within the RR-P3HT matrix due to intrachain crystallization. Cryogen-based scanning electron microscopy of RR P3HT gels revealed the existence of an interfibrillar network exhibiting nano-sized pores. Remarkably, these networks formed to equal gel strengths when a third component, either small molecule phenyl C61 butyric acid methyl ester (PCBM) or non-crystallizing regiorandom (Rra)-P3HT, was added to the solution. Organic solar cells in which the active layers were deposited from phase-separated solutions displayed 45% higher efficiency compared to reference cells.

physics.app-ph↗

arXiv2018

Shear viscosity and wall slip behavior of dense suspensions of polydisperse particles

Shear viscosity and wall slip of dense suspensions of a silicone polymer incorporated with polydisperse particles were investigated. Three types of particles with low aspect ratios were used to achieve a relatively high maximum packing fraction of 0.86. Such a high maximum packing fraction allowed the preparation of suspensions with a wide range of solid volume fractions in the range of 0.62 to 0.82. The wall slip velocities of the suspensions in steady torsional and capillary flows were characterized and determined to be fully consistent with the mechanism of apparent slip layer formation at the wall. Upon wall slip corrections it was found that at shear stresses which are significantly above the yield stress the relative shear viscosity of the suspensions obeys well the Krieger-Dougherty relationship that links the relative shear viscosity behavior of dense suspensions solely to the ratio of the volume fraction over the maximum packing fraction of solids. However, at lower shear stresses that are in the vicinity of the yield stresses the relative shear viscosity becomes functions of both the ratio of the volume fraction over the maximum packing fraction and the shear stress. It is clearly demonstrated that without wall slip analysis the accurate characterization of the relative shear viscosity of dense suspensions is not possible.

cond-mat.soft↗

arXiv2018

Personalized Next Point-of-Interest Recommendation via Latent Behavior Patterns Inference

In this paper, we address the problem of personalized next Point-of-interest (POI) recommendation which has become an important and very challenging task for location-based social networks (LBSNs), but not well studied yet. With the conjecture that, under different contextual scenarios, human exhibits distinct mobility pattern, we attempt here to jointly model the next POI recommendation under the influence of user's latent behavior pattern. We propose to adopt a third-rank tensor to model the successive check-in behaviors. By integrating categorical influence into mobility patterns and aggregating user's spatial preference on a POI, the proposed model deal with the next new POI recommendation problem by nature. By incorporating softmax function to fuse the personalized Markov chain with latent pattern, we furnish a Bayesian Personalized Ranking (BPR) approach and derive the optimization criterion accordingly. Expectation Maximization (EM) is then used to estimate the model parameters. We further develop a personalized model by taking into account personalized mobility patterns under the contextual scenario to improve the recommendation performance. Extensive experiments on two large-scale LBSNs datasets demonstrate the significant improvements of our model over several state-of-the-art methods.

cs.SI↗

arXiv2017

Type II Nodal line Semimetal

Recently, topological semimetals become hot topic in condensed matter physics, including Dirac semimetal, Weyl semimetal, and nodal line semimetal (NLSM). In this paper, a new type of node- line semimetal - type-II NLSM is proposed based on a two-band cubic lattice model. For type-II NLSM, the zero energy bulk states have a closed loop in momentum space but the (local) Weyl cones on nodal line become tilted. The effect of magnetic field and that of correlation on type-II NLSM are studied. In particular, after considering repulsive interaction and additional spin degrees of freedom, different types of long range magnetic orders appear in bulk states. In addition, the interaction-induced ferromagnetic order of surface states may exist. At critical point between type-I NLSM and type-II NLSM, arbitrary tiny interaction induces ferromagnetic order due to a flat band at Fermi surface.

cond-mat.str-el↗

arXiv2017

Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data

Natural language generation plays a critical role in spoken dialogue systems. We present a new approach to natural language generation for task-oriented dialogue using recurrent neural networks in an encoder-decoder framework. In contrast to previous work, our model uses both lexicalized and delexicalized components i.e. slot-value pairs for dialogue acts, with slots and corresponding values aligned together. This allows our model to learn from all available data including the slot-value pairing, rather than being restricted to delexicalized slots. We show that this helps our model generate more natural sentences with better grammar. We further improve our model's performance by transferring weights learnt from a pretrained sentence auto-encoder. Human evaluation of our best-performing model indicates that it generates sentences which users find more appealing.

cs.CL↗

arXiv2016

Policy Networks with Two-Stage Training for Dialogue Systems

In this paper, we propose to use deep policy networks which are trained with an advantage actor-critic method for statistically optimised dialogue systems. First, we show that, on summary state and action spaces, deep Reinforcement Learning (RL) outperforms Gaussian Processes methods. Summary state and action spaces lead to good performance but require pre-engineering effort, RL knowledge, and domain expertise. In order to remove the need to define such summary spaces, we show that deep RL can also be trained efficiently on the original state and action spaces. Dialogue systems based on partially observable Markov decision processes are known to require many dialogues to train, which makes them unappealing for practical deployment. We show that a deep RL method based on an actor-critic architecture can exploit a small amount of data very efficiently. Indeed, with only a few hundred dialogues collected with a handcrafted policy, the actor-critic deep learner is considerably bootstrapped from a combination of supervised and batch RL. In addition, convergence to an optimal policy is significantly sped up compared to other deep RL methods initialized on the data with batch RL. All experiments are performed on a restaurant domain derived from the Dialogue State Tracking Challenge 2 (DSTC2) dataset.

cs.CL↗

arXiv2016

A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems

User simulation is essential for generating enough data to train a statistical spoken dialogue system. Previous models for user simulation suffer from several drawbacks, such as the inability to take dialogue history into account, the need of rigid structure to ensure coherent user behaviour, heavy dependence on a specific domain, the inability to output several user intentions during one dialogue turn, or the requirement of a summarized action space for tractability. This paper introduces a data-driven user simulator based on an encoder-decoder recurrent neural network. The model takes as input a sequence of dialogue contexts and outputs a sequence of dialogue acts corresponding to user intentions. The dialogue contexts include information about the machine acts and the status of the user goal. We show on the Dialogue State Tracking Challenge 2 (DSTC2) dataset that the sequence-to-sequence model outperforms an agenda-based simulator and an n-gram simulator, according to F-score. Furthermore, we show how this model can be used on the original action space and thereby models user behaviour with finer granularity.

cs.CL↗

arXiv2016

The Simulation of Non-Abelian Statistics of Majorana Fermions in Ising Chain with Z2 Symmetry

In this paper, we numerically study the non-Abelian statistics of the zero-energy Majorana fermions on the end of Majorana chain and show its application to quantum computing by mapping it to a spin model with special symmetry. In particular, by using transverse-field Ising model with Z2 symmetry, we verify the nontrivial non-Abelian statistics of Majorana fermions. Numerical evidence and comparison in both Majorana-representation and spin-representation are presented. The degenerate ground states of a symmetry protected spin chain therefore previde a promising platform for topological quantum computation.

cond-mat.mes-hall↗

arXiv2016

A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data

Understanding unstructured text is a major goal within natural language processing. Comprehension tests pose questions based on short text passages to evaluate such understanding. In this work, we investigate machine comprehension on the challenging {\it MCTest} benchmark. Partly because of its limited size, prior work on {\it MCTest} has focused mainly on engineering better features. We tackle the dataset with a neural approach, harnessing simple neural networks arranged in a parallel hierarchy. The parallel hierarchy enables our model to compare the passage, question, and answer from a variety of trainable perspectives, as opposed to using a manually designed, rigid feature set. Perspectives range from the word level to sentence fragments to sequences of sentences; the networks operate only on word-embedding representations of text. When trained with a methodology designed to help cope with limited training data, our Parallel-Hierarchical model sets a new state of the art for {\it MCTest}, outperforming previous feature-engineered approaches slightly and previous neural approaches by a significant margin (over 15\% absolute).

cs.CL↗

arXiv2015

Topological Hierarchy Insulators and Topological Fractal Insulators

Topological insulators are new states of quantum matter with metallic edge/surface states. In this paper, we pointed out that there exists a new type of particle-hole symmetry-protected topological insulator - topological hierarchy insulator (THI), a composite topological state of a (parent) topological insulator and its defect-induced topological mid-gap states. A particular type of THI is topological fractal insulator, that is a THI with self-similar topological structure. In the end, we discuss the possible experimental realizations of THIs.

cond-mat.str-el↗