arXiv subjects

Yu Yang

Publications and source records attributed to Yu Yang.

At least 253 records · Page 14Linked to original sources

arXiv2021

Parity measurement in the strong dispersive regime of circuit quantum acoustodynamics

Mechanical resonators are emerging as an important new platform for quantum science and technologies. A large number of proposals for using them to store, process, and transduce quantum information motivates the development of increasingly sophisticated techniques for controlling mechanical motion in the quantum regime. By interfacing mechanical resonators with superconducting circuits, circuit quantum acoustodynamics (cQAD) can make a variety of important tools available for manipulating and measuring motional quantum states. Here we demonstrate direct measurements of the phonon number distribution and parity of nonclassical mechanical states. We do this by operating our system in the strong dispersive regime, where a superconducting qubit can be used to spectroscopically resolve phonon Fock states. These measurements are some of the basic building blocks for constructing acoustic quantum memories and processors. Furthermore, our results open the door to performing even more complex quantum algorithms using mechanical systems, such as quantum error correction and multi-mode operations.

quant-ph↗

arXiv2021

Influence of database noises to machine learning for spatiotemporal chaos

A new strategy, namely the "clean numerical simulation" (CNS), was proposed (J. Computational Physics, 418:109629, 2020) to gain reliable/convergent simulations (with negligible numerical noises) of spatiotemporal chaotic systems in a long enough interval of time, which provide us benchmark solution for comparison. Here we illustrate that machine learning (ML) can always give good enough fitting predictions of a spatiotemporal chaos by using, separately, two quite different training sets: one is the "clean database" given by the CNS with negligible numerical noises, the other is the "polluted database" given by the traditional algorithms in single/double precision with considerably large numerical noises. However, even in statistics, the ML predictions based on the "polluted database" are quite different from those based on the "clean database". It illustrates that the database noises have huge influences on ML predictions of some spatiotemporal chaos, even in statistics. Thus, we must use a "clean" database for machine learning of some spatiotemporal chaos. This surprising result might open a new door and possibility to study machine learning.

physics.comp-ph↗

arXiv2021

Language Scaling for Universal Suggested Replies Model

We consider the problem of scaling automated suggested replies for Outlook email system to multiple languages. Faced with increased compute requirements and low resources for language expansion, we build a single universal model for improving the quality and reducing run-time costs of our production system. However, restricted data movement across regional centers prevents joint training across languages. To this end, we propose a multi-task continual learning framework, with auxiliary tasks and language adapters to learn universal language representation across regions. The experimental results show positive cross-lingual transfer across languages while reducing catastrophic forgetting across regions. Our online results on real user traffic show significant gains in CTR and characters saved, as well as 65% training cost reduction compared with per-language models. As a consequence, we have scaled the feature in multiple languages including low-resource markets.

cs.CL↗

arXiv2021

Greenplum: A Hybrid Database for Transactional and Analytical Workloads

Demand for enterprise data warehouse solutions to support real-time Online Transaction Processing (OLTP) queries as well as long-running Online Analytical Processing (OLAP) workloads is growing. Greenplum database is traditionally known as an OLAP data warehouse system with limited ability to process OLTP workloads. In this paper, we augment Greenplum into a hybrid system to serve both OLTP and OLAP workloads. The challenge we address here is to achieve this goal while maintaining the ACID properties with minimal performance overhead. In this effort, we identify the engineering and performance bottlenecks such as the under-performing restrictive locking and the two-phase commit protocol. Next we solve the resource contention issues between transactional and analytical queries. We propose a global deadlock detector to increase the concurrency of query processing. When transactions that update data are guaranteed to reside on exactly one segment we introduce one-phase commit to speed up query processing. Our resource group model introduces the capability to separate OLAP and OLTP workloads into more suitable query processing mode. Our experimental evaluation on the TPC-B and CH-benCHmark benchmarks demonstrates the effectiveness of our approach in boosting the OLTP performance without sacrificing the OLAP performance.

cs.DB↗

arXiv2021

Quantum state transfer between two photons with polarization and orbital angular momentum via quantum teleportation technology

Quantum teleportation is a useful quantum information technology to transmit quantum states between different degrees of freedom. We here report a quantum state transfer experiment in the linear optical system, transferring a single photon state in the polarization degree of freedom (DoF) to another photon in the orbital angular momentum (OAM) quantum state via a biphoton OAM entangled channel. Our experimental method is based on quantum teleportation technology. The differences between ours and the original teleportation scheme is that the transfer state is known in ours, and our method is for different particles with different DoFs while the original one is for different particles with same DoF. Besides, our present experiment is implemented with a high Bell-efficiency since each of the four hybrid-entangled Bell states can be discriminated. We use six states of poles of the Bloch sphere to test our experiment, and the fidelity of the quantum state transfer is $91.8\pm1.3\%$.

quant-ph↗

arXiv2021

Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test

The Kolmogorov-Smirnov (KS) test is popularly used in many applications, such as anomaly detection, astronomy, database security and AI systems. One challenge remained untouched is how we can obtain an explanation on why a test set fails the KS test. In this paper, we tackle the problem of producing counterfactual explanations for test data failing the KS test. Concept-wise, we propose the notion of most comprehensible counterfactual explanations, which accommodates both the KS test data and the user domain knowledge in producing explanations. Computation-wise, we develop an efficient algorithm MOCHE (for MOst CompreHensible Explanation) that avoids enumerating and checking an exponential number of subsets of the test set failing the KS test. MOCHE not only guarantees to produce the most comprehensible counterfactual explanations, but also is orders of magnitudes faster than the baselines. Experiment-wise, we present a systematic empirical study on a series of benchmark real datasets to verify the effectiveness, efficiency and scalability of most comprehensible counterfactual explanations and MOCHE.

cs.LG↗

arXiv2021

Optimal Sharing and Fair Cost Allocation of Community Energy Storage

This paper studies an energy storage (ES) sharing model which is cooperatively invested by multiple buildings for harnessing on-site renewable utilization and grid price arbitrage. To maximize the economic benefits, we jointly consider the ES sizing, operation, and cost allocation via a coalition game formulation. Particularly, we study a fair ex-post cost allocation based on nucleolus which addresses fairness by minimizing the minimal dissatisfaction of all the players. To overcome the exponential computation burden caused by the implicit characteristic function, we employ a constraint generation technique to gradually approach the unique nucleolus by leveraging the sparse problem structure. We demonstrate both the fairness and computational efficiency of the method through case studies, which are not provided by the existing Shapley approach or proportional method. Particularly, only a small fraction of characteristic function (less than 1% for 20 buildings) is required to achieve the cost allocation versus the exponential information required by Shapley approach. Though there exists a minor increase of computation over the proportional method, the proposed method can ensure fairness while the latter fails in some cases. Further, we demonstrate both the building-wise and community-wise economic benefits are enhanced with the ES sharing model over the individual ES (IES) model. Accordingly, the overall value of ES is considerably improved (about 1.83 times).

cs.GT↗

arXiv2021

Maximums of generalized Hasse-Witt invariants and their applications to anabelian geometry

Let $(X, D_{X})$ be an arbitrary pointed stable curve of topological type $(g_{X}, n_{X})$ over an algebraically closed field of characteristic $p>0$. We prove that the generalized Hasse-Witt invariants of prime-to-$p$ cyclic admissible coverings of $(X, D_{X})$ attain maximum. As applications, we obtain an anabelian formula for $(g_{X}, n_{X})$, and prove that the field structures associated to inertia subgroups of marked points can be reconstructed group-theoretically from open continuous homomorphisms of admissible fundamental groups. Moreover, the formula for maximum generalized Hasse-Witt invariants and the result concerning reconstructions of field structures play important roles in the theory of moduli spaces of fundamental groups developed by the author of the present paper.

math.AG↗

arXiv2021

Stochastic Optimal Control of HVAC system for Energy-efficient Buildings

The heating, ventilation and air-conditioning (HVAC) system accounts for substantial energy use in buildings, whereas a large group of occupants are still not actually feeling comfortable staying inside. This poses the issue of developing energy-efficient HVAC control, i.e., reduce energy use (cost) while simultaneously enhancing human comfort. This paper pursues the objective and studies the stochastic optimal HVAC control subject to uncertain thermal demand (i.e., the weather and occupancy etc). Particularly, we involve the elaborate predicted mean vote (PMV) thermal comfort model in the optimization. The problem is computationally challenging due to the non-linear and non-analytical constraints imposed by the system dynamics and PMV model. We make the following contributions to address it. First, we formulate the problem as a Markov decision process (MDP) which is a desirable modeling technique capable of handling the complexities. Second, we propose a gradient-based learning (GB-L) method for progressively learning a stochastic control policy off-line and store it for on-line execution. Third, we prove the learning method converge to the optimal policies theoretically, and its performance (i.e., energy cost, thermal comfort and on-line computation) for HVAC control via simulations. The comparisons with the existing model predictive control based relaxation (MPC-R) method which is assumed with accurate future information and supposed to provide the near-optimal bounds, show that though there exists some performance loss in energy cost reduction (i.e., 6.5%), the proposed method can enable efficient on-line implementation (less than 1 second) and provide high probability of thermal comfort under uncertainties.

eess.SY↗

arXiv2021

Selling Renewable Utilization Service to Consumers via Cloud Energy Storage

This paper proposes a cloud energy storage (CES) model for enabling local renewable integration of building consumers (BCs). Different from most existing third-party based ES sharing models that the energy storage operator (ESO) gains profit by leasing energy or power capacity, our CES model allows the ESO to sell renewable utilization service (RUS) to its consumers, i.e., the total amount of local renewable generation shifted to supply their demand. Notably, we propose a quadratic price model for the ESO charging its consumers by their requested RUS and formulate their interactions as a Stackelberg game, which admits an equilibrium. We prove the CES model outperforms individual ES (IES) model in social welfare. Besides, we study the performance of the CES model compared with the IES model and an existing ES sharing model (referring to VES model) via case studies. We demonstrate the CES model can provide 2-4 times profit to the ESO than the VES model. Meanwhile, higher cost reduction for the BCs are secured by the CES model. Moreover, we show the CES model can achieve near social optima and high ES efficiency (i.e., utilization) which are not provided by the other ES models.

cs.GT↗

arXiv2021

Distributed Control of Multi-zone HVAC Systems Considering Indoor Air Quality

This paper studies a scalable control method for multi-zone heating, ventilation and air-conditioning (HVAC) systems to optimize the energy cost for maintaining thermal comfort and indoor air quality (IAQ) (represented by CO2) simultaneously. This problem is computationally challenging due to the complex system dynamics, various spatial and temporal couplings as well as multiple control variables to be coordinated. To address the challenges, we propose a two-level distributed method (TLDM) with a upper level and lower level control integrated. The upper level computes zone mass flow rates for maintaining zone thermal comfort with minimal energy cost, and then the lower level strategically regulates zone mass flow rates and the ventilation rate to achieve IAQ while preserving the near energy saving performance of upper level. As both the upper and lower level computation are deployed in a distributed manner, the proposed method is scalable and computationally efficient. The near-optimal performance of the method in energy cost saving is demonstrated through comparison with the centralized method. In addition, the comparisons with the existing distributed method show that our method can provide IAQ with only little increase of energy cost while the latter fails. Moreover, we demonstrate our method outperforms the demand controlled ventilation strategies (DCVs) for IAQ management with about 8-10% energy cost reduction.

eess.SY↗

arXiv2020

The interplay between symmetry-breaking and symmetry-preserving bifurcations in soft dielectric films and the emergence of giant electro-actuation

Soft elastomers that can exhibit extremely large deformations under the action of an electric field are essential for applications such as soft robotics, stretchable and flexible electronics, energy harvesting among others. The critical limiting factor in conventional electro-actuation of such materials is the occurrence of the so-called pull-in instability. In this work, we demonstrate an extraordinarily simple way to coax a dielectric thin film towards a symmetry-breaking pitchfork bifurcation state while avoiding pull-in instability. Through the nonlinear interplay between the two bifurcation modes, we predict electro-actuation strains that exceed what is conventionally possible by 200$\%$, and at significantly lower applied electric fields.

cond-mat.soft↗

arXiv2020

Optimizing Demand-Responsive Paratransit Operations: A Mixed Integer Programming Approach

The traditional demand-responsive paratransit system plays an important role in connecting people to health care, particularly those who are carless, low-income, senior, underinsured/uninsured, or who have a disability. However, the existing paratransit system usually has low service quality due to long waiting times, low operation frequencies, and high costs. In order to improve the service quality, we propose to design a new demand-responsive paratransit system that offers public, Uber-like options for non-emergency medical transportation. Mixed integer programming models are thus developed to optimize the system operations with the objectives of minimizing user waiting times for riders as well as operating costs for operators. The results produced in this paper will assist local departments of transportation and transit agencies as they consider operational strategies to meet non-emergency medical transport needs.

math.OC↗

arXiv2020

The soft-mode phonons mediated unconventional superconductivity in monolayer 1T'-WTe2

Recent experiments have tuned the monolayer 1T'-WTe2 to be superconducting by electrostatic gating. Here, we theoretically study the phonon-mediated superconductivity in monolayer 1T'-WTe2 via charge doping. We reveal that the emergence of soft-mode phonons with specific momentum is crucial to give rise to the superconductivity in electron-doping regime, whereas no such soft-mode phonons and no superconductivity emerge in hole-doping regime. We also find a superconducting dome, which can be attributed to the change of Fermi surface nesting condition as electron doping. By taking into account the experimentally established strong anisotropy of temperature-dependent upper critical field H_{c2} between the in-plane and out-of-plane directions, we show that the superconducting state probably has the unconventional equal-spin-triplet pairing in A_{u} channel of C_{2h} point group. Our studies provide a promising understanding to the doping dependent superconductivity and strong anisotropy of H_{c2} in monolayer 1T'-WTe2.

cond-mat.supr-con↗

arXiv2020

Moduli spaces of fundamental groups of curves in positive characteristic I

In this series of papers, we investigate a new anabelian phenomenon of curves over algebraically closed fields of positive characteristic. Let $\overline M_{g, n}$ be the moduli space of curves of type $(g, n)$ over $\overline {\mathbb{F}}_p$. We introduce a topological space $\overline Π_{g, n}$ which can be determined group-theoretically from admissible fundamental groups of pointed stable curves of type $(g, n)$. By introducing a certain equivalence relation $\sim_{fe}$ on the underlying topological space $|\overline M_{g, n}|$ of $\overline M_{g, n}$, we obtain a topological space $\overline {\mathfrak{M}_{g, n}}:= |\overline {M_{g, n}}|/\sim_{fe}$. Moreover, there is a natural continuous map $π_{g,n}^{\rm adm}: \overline {\mathfrak{M}}_{g, n} \rightarrow \overline Π_{g, n}.$ Furthermore, we pose a conjecture (=the Homeomorphism Conjecture) which says that $π_{g,n}^{\rm adm}$ is a homeomorphism. The Homeomorphism Conjecture generalizes all the conjectures in the theory of anableian geometry of curves over algebraically closed fields of characteristic $p$. One of main results of the present series of papers says that the Homeomorphism Conjecture holds when $\text{dim}(\overline M_{g, n})=1$ (i.e., $(g, n)=(0,4)$ or $(g, n)=(1,1)$). In the present paper, we establish two fundamental tools to analyze the geometric behavior of curves from open continuous homomorphisms of admissible fundamental groups, which play central roles in the theory developed in the series of papers. Moreover, we prove that $π_{0,n}^{\rm adm}([q])$ is a closed point of $\overline Π_{0,n}$ when $[q]$ is a closed point of $\overline {\mathfrak{M}}_{0, n}$. In particular, we obtain that the Homeomorphism Conjecture holds when $(g, n)=(0, 4)$.

math.AG↗

arXiv2020

An Overview of Researches on Laser Ion Acceleration Using Mixed Solid Target and Single Ion Target

The essay gives an overview on researches in the field of laser ion acceleration, focusing on two types of targets. There are many types of targets while they can all be divided into targets that apply single ion or multiple ions. Mixed solid targets are proven efficient in accelerating heavy ions and generate high-quality ion beams with energy divergence lower than 5%. Traditional methods like TNSA are mainly used to accelerate protons or heavy ions and there are still many spaces for modification and improvement. Applications of laser-driven ion beams are wide in fields like detector technology, cancer therapy and so on, which is promising and necessary.

physics.plasm-ph↗

arXiv2020

The expected subtree number index in random polyphenylene and spiro chains

Subtree number index $\emph{STN}(G)$ of a graph $G$ is the number of nonempty subtrees of $G$. It is a structural and counting based topological index that has received more and more attention in recent years. In this paper we first obtain exact formulas for the expected values of subtree number index of random polyphenylene and spiro chains, which are molecular graphs of a class of unbranched multispiro molecules and polycyclic aromatic hydrocarbons. Moreover, we establish a relation between the expected values of the subtree number indices of a random polyphenylene and its corresponding hexagonal squeeze. We also present the average values for subtree number indices with respect to the set of all polyphenylene and spiro chains with $n$ hexagons.

math.CO↗

arXiv2020

EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors

Early prediction of students at risk (STAR) is an effective and significant means to provide timely intervention for dropout and suicide. Existing works mostly rely on either online or offline learning behaviors which are not comprehensive enough to capture the whole learning processes and lead to unsatisfying prediction performance. We propose a novel algorithm (EPARS) that could early predict STAR in a semester by modeling online and offline learning behaviors. The online behaviors come from the log of activities when students use the online learning management system. The offline behaviors derive from the check-in records of the library. Our main observations are two folds. Significantly different from good students, STAR barely have regular and clear study routines. We devised a multi-scale bag-of-regularity method to extract the regularity of learning behaviors that is robust to sparse data. Second, friends of STAR are more likely to be at risk. We constructed a co-occurrence network to approximate the underlying social network and encode the social homophily as features through network embedding. To validate the proposed algorithm, extensive experiments have been conducted among an Asian university with 15,503 undergraduate students. The results indicate EPARS outperforms baselines by 14.62% ~ 38.22% in predicting STAR.

cs.AI↗