In today's online advertising markets, a crucial requirement for an advertiser is to control her total expenditure within a time horizon under some budget. Among various budget control methods, throttling has emerged as a popular choice, managing an advertiser's total expenditure by selecting only a subset of auctions to participate in. This paper provides a theoretical panorama of a single advertiser's dynamic budget throttling process in repeated second-price auctions. We first establish a lower bound on the regret and an upper bound on the asymptotic competitive ratio for any throttling algorithm, respectively, when the advertiser's values are stochastic and adversarial. Regarding the algorithmic side, we propose the OGD-CB algorithm, which guarantees a near-optimal expected regret with stochastic values. On the other hand, when values are adversarial, we prove that this algorithm also reaches the upper bound on the asymptotic competitive ratio. We further compare throttling with pacing, another widely adopted budget control method, in repeated second-price auctions. In the stochastic case, we demonstrate that pacing is generally superior to throttling for the advertiser, supporting the well-known result that pacing is asymptotically optimal in this scenario. However, in the adversarial case, we give an exciting result indicating that throttling is also an asymptotically optimal dynamic bidding strategy. Our results bridge the gaps in theoretical research of throttling in repeated auctions and comprehensively reveal the ability of this popular budget-smoothing strategy.

In today's online advertising markets, it is common for advertisers to set long-term budgets. Correspondingly, advertising platforms adopt budget control methods to ensure that advertisers' payments lie within their budgets. Most budget control methods rely on the value distributions of advertisers. However, due to the complex advertising landscape and potential privacy concerns, the platform hardly learns advertisers' true priors. Thus, it is crucial to understand how budget control auction mechanisms perform under unassured priors.

The demand for permanent magnet resolvers is rising significantly with the increase of operation speed in the electric propulsion system. In this paper, the rotor-pole number (RPN) cooperation strategy is proposed from the perspective of phase shift on the modular flux switching permanent magnet (FSPM) resolver with yokeless stator. The RPN cooperation strategy is deduced analytically by adjusting the angle fitting ratio under the fixed 90-degree sine and cosine winding phase shift electric angle. Moreover, its restrictions are also derived by the flux leakage and permeance analysis in certain cases. Therefore, the proposed cooperation strategy and its limitations enrich the field modulation principle in the PM resolver application. Based on the proposed cooperation strategy and its limitation, a series of proper rotor and stator combinations are proposed in the FSPM resolver with no more than 1.5% total harmonic distortion (THD) evaluated in finite element analysis (FEA).

A/B testing, or controlled experiments, is the gold standard approach to causally compare the performance of algorithms on online platforms. However, conventional Bernoulli randomization in A/B testing faces many challenges such as spillover and carryover effects. Our study focuses on another challenge, especially for A/B testing on two-sided platforms – budget constraints. Buyers on two-sided platforms often have limited budgets, where the conventional A/B testing may be infeasible to be applied, partly because two variants of allocation algorithms may conflict and lead some buyers to exceed their budgets if they are implemented simultaneously. We develop a model to describe two-sided platforms where buyers have limited budgets. We then provide an optimal experimental design that guarantees small bias and minimum variance. Bias is lower when there is more budget and a higher supply-demand rate. We test our experimental design on both synthetic data and real-world data, which verifies the theoretical results and shows our advantage compared to Bernoulli randomization.

Abstract This paper proposes an enhancement to the Harris’ Hawks Optimisation (HHO) algorithm. Firstly, an enhanced HHO (EHHO) model is developed to solve single-objective optimisation problems (SOPs). EHHO is then further extended to a multi-objective EHHO (MO-EHHO) model to solve multi-objective optimisation problems (MOPs). In EHHO, a nonlinear exploration factor is formulated to replace the original linear exploration method, which improves the exploration capability and facilitate the transition from exploration to exploitation. In addition, the Differential Evolution (DE) scheme is incorporated into EHHO to generate diverse individuals. To replace the DE mutation factor, a chaos strategy that increases randomness to cover wider search areas is adopted. The non-dominated sorting method with the crowding distance is leveraged in MO-EHHO, while a mutation mechanism is employed to increase the diversity of individuals in the external archive for addressing MOPs. Benchmark SOPs and MOPs are used to evaluate EHHO and MO-EHHO models, respectively. The sign test is employed to ascertain the performance of EHHO and MO-EHHO from the statistical perspective. Based on the average ranking method, EHHO and MO-EHHO indicate their efficacy in tackling SOPs and MOPs, as compared with those from the original HHO algorithm, its variants, and many other established evolutionary algorithms.

This paper proposes a new multi-port flux-modulated permanent-magnet (FMPM) composite machine. The combination of the multi-port and flux-modulated configurations leads to the large torque capacity, high efficiency and fault-tolerant capability. Firstly, the composite flux modulation principle is specifically illustrated, as a general design principle of FMPM composite machines. Specific performance analysis is carried out to demonstrate the electromagnetic characteristics of the proposed machine, with auxiliary of the harmonic analysis. The composite flux modulation effect is proven for effective contribution to the higher torque density of the proposed FMPM machine. As a result, the proposed machine serves as a promising candidate in electric transportation applications.

This paper presents an expansion to the Abstract Meaning Representation (AMR) annotation schema that captures fine-grained semantically and pragmatically derived spatial information in grounded corpora. We describe a new lexical category conceptualization and set of spatial annotation tools built in the context of a multimodal corpus consisting of 170 3D structure-building dialogues between a human architect and human builder in Minecraft. Minecraft provides a particularly beneficial spatial relation-elicitation environment because it automatically tracks locations and orientations of objects and avatars in the space according to an absolute Cartesian coordinate system. Through a two-step process of sentence-level and document-level annotation designed to capture implicit information, we leverage these coordinates and bearings in the AMRs in combination with spatial framework annotation to ground the spatial language in the dialogues to absolute space.

Spatial Reasoning from language is essential for natural language understanding. Supporting it requires a representation scheme that can capture spatial phenomena encountered in language as well as in images and videos. Existing spatial representations are not sufficient for describing spatial configurations used in complex tasks. This paper extends the capabilities of existing spatial representation languages and increases coverage of the semantic aspects that are needed to ground spatial meaning of natural language text in the world. Our spatial relation language is able to represent a large, comprehensive set of spatial concepts crucial for reasoning and is designed to support composition of static and dynamic spatial configurations. We integrate this language with the Abstract Meaning Representation (AMR) annotation schema and present a corpus annotated by this extended AMR. To exhibit the applicability of our representation scheme, we annotate text taken from diverse datasets and show how we extend the capabilities of existing spatial representation languages with fine-grained decomposition of semantics and blend it seamlessly with AMRs of sentences and discourse representations as a whole.

We show that the imperceptibility of several existing linguistic steganographic systems (Fang et al., 2017;Yang et al., 2018) relies on implicit assumptions on statistical behaviors of fluent text.We formally analyze them and empirically evaluate these assumptions.Furthermore, based on these observations, we propose an encoding algorithm called patient-Huffman with improved near-imperceptible guarantees.

Vivian Lai, Zheng Cai, Chenhao Tan. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Causal networks are used to describe and to discover causal relationships among variables and data generating mechanisms. There have been many approaches for learning a global causal network of all observed variables. In many applications, we may be interested in finding what are the effects of a specified cause variable and what are the causal paths from the cause variable to its effects. Instead of learning a global causal network, we propose several local learning approaches for finding all effects (or descendants) of the specified cause variable and the causal paths from the cause variable to some effect variable of interest. We discuss the identifiability of the effects and the causal paths from observed data and prior knowledge. For the case that the causal paths are not identifiable, our approaches try to find a path set that contains the causal paths of interest.

With the progress of social medias, an increasing number of dynamic multimedia, such as video clips (GIF), were used in Internet. Many of them show the subjective sentiment of users. GIF sentiment analysis is important for political election predication and economic indicator evaluation. Unfortunately, such task is quite challenging because the users' sentiment hinges on spatial-temporal visual concepts. And the relationship between such concepts and overall sentiment polarity remains unknown. In this paper, dedicated to build a bridge to exploring such relationship, we proposed a SentiPair Sequence based spatial-temporal visual sentiment ontology. The ontology serves as a mid-level representation of GIF sentiment analysis. In order to analyze sentiment polarity, we first constructed a Synset Forest to define the semantic tree structure of visual sentiment concepts. Then, through the Synset Forest, we organically select and combine sentiment label elements to form a mid-level visual sentiment representation. Our experiments indicate that SentiPair outperforms Adjective Noun Pairs which is a state-of-art visual sentiment ontology for image. We also released our dataset (GSO-2015) to the research community. GSO-2015 contains more than 6,000 manually labeled GIFs and 40,000 unlabeled GIFs. Each is labeled with both sentiment and SentiPair Sequence.

This paper presents the CONTRACT framework to address a fundamental deficiency of the IP network control plane, namely the lack of coordination between an IGP and other control functions involved in achieving a high level objective. For example, an IGP's default automatic reaction to a network failure may result in an SLA violation, even if the IGP link weights have been carefully chosen. This is because an IGP blindly routes traffic along the shortest paths based on link weights, and it is completely oblivious to the interactions between SLA compliance, load balancing and traffic policing objectives in a network. The CONTRACT framework makes it possible to coordinate these objectives. Under this framework, routers continue to operate autonomously, but they also coordinate their actions with a centralized network controller, which evaluates the impact of routing changes, decides whether the changes are SLA compliant, and performs load rebalancing and/or packet filter reconfiguration as necessary. The key contribution of CONTRACT is a set of coordination algorithms. We show that CONTRACT can effectively coordinate the actions of routing, load balancing and traffic policing to improve a network's SLA compliance.

A class of similar composite systems is researched. The interconnections of systems are not symmetric and do not satisfy the matching condition. By means of the advantage of interconnections of systems, a two-step method is proposed to study the decentralized stabilization problem for the similar composite systems in this paper. First, the interconnections between subsystems are changed into a new form to satisfy the matching condition; second, based on the new form of the systems, the decentralized robust controllers are designed. In this paper, the controllers guarantee the similar composite systems quadratic stability. At last, the simulation of example is given.