pwc-archive/papers-with-abstracts huggingface dataset:
Minority games played by arbitrageurs on the energy market
Authors: Tim Ritmeester, Hildegard Meyer-Ortmanns
Date: 2020-12-18 00:00:00

Along with the energy transition, the energy markets change their organization toward more decentralized and self-organized structures, striving for locally optimal profits. These tendencies may endanger the physical grid stability. One realistic option is the exhaustion of reserve energy due to an abuse by arbitrageurs. We map the energy market to different versions of a minority game and determine the expected amount of arbitrage as well as its fluctuations as a function of the model parameters. Of particular interest are the impact of heterogeneous contributions of arbitrageurs, the interplay between external stochastic events and nonlinear price functions of reserve power, and the effect of risk aversion due to suspected penalties. The non-monotonic dependence of arbitrage on the control parameters reveals an underlying phase transition that is the counterpart to replica symmetry breaking in spin glasses. As conclusions from our results we propose economic and statutory measures to counteract a detrimental effect of arbitrage.
Decentralized Pure Exchange Processes on Networks
Authors: Daniele Cassese, Paolo Pin
Date: 2018-03-23 00:00:00

We define a class of pure exchange Edgeworth trading processes that under minimal assumptions converge to a stable set in the space of allocations, and characterise the Pareto set of these processes. Choosing a specific process belonging to this class, that we define fair trading, we analyse the trade dynamics between agents located on a weighted network. We determine the conditions under which there always exists a one-to-one map between the set of networks and the set of limit points of the dynamics. This result is used to understand what is the effect of the network topology on the trade dynamics and on the final allocation. We find that the positions in the network affect the distribution of the utility gains, given the initial allocations
Pursuing robust decisions in uncertain traffic equilibrium problems
Authors: Filippo Fabiani
Date: 2021-03-23 00:00:00

We evaluate the robustness of agents' traffic equilibria in randomized routing games characterized by an uncertain network demand with a possibly unknown probability distribution. Specifically, we extend the so-called hose model by considering a traffic equilibrium model where the uncertain network demand configuration belongs to a polyhedral set, whose shape is itself a-priori unknown. By exploiting available data, we apply the scenario approach theory to establish distribution-free feasibility guarantees for agents' traffic equilibria of the uncertain routing game without the need to know an explicit characterization of such set. A numerical example on a traffic network testbed corroborates the proposed theoretical results.
Global Iterative Sliding Mode Control of an Industrial Biaxial Gantry System for Contouring Motion Tasks
Authors: Wenxin Wang, Jun Ma, Zilong Cheng, Xiaocong Li, Clarence W de Silva, Tong Heng Lee
Date: 2021-03-23 00:00:00
Tasks: Friction

This paper proposes a global iterative sliding mode control approach for high-precision contouring tasks of a flexure-linked biaxial gantry system. For such high-precision contouring tasks, it is the typical situation that the involved multi-axis cooperation is one of the most challenging problems. As also would be inevitably encountered, various factors render the multi-axis cooperation rather difficult; such as the strong coupling (which naturally brings nonlinearity) between different axes due to its mechanical structure, the backlash and deadzone caused by the friction, and the difficulties in system identification, etc. To overcome the above-mentioned issues, this work investigates an intelligent model-free contouring control method for such a multi-axis motion stage. Essentially in the methodology developed here, it is firstly ensured that all the coupling, friction, nonlinearity, and disturbance (regarded as uncertain dynamics in each axis) are suitably posed as `uncertainties'. Then, a varying-gain sliding mode control method is proposed to adaptively compensate for the matched unknown dynamics in the time domain, while an iterative learning law is applied to suppress the undesirable effects (arising from the repetitive matched and unmatched uncertainties in the iteration domain). With this approach, the chattering that typically results from the overestimated control gains in the sliding mode control is thus suppressed during the iterations. To analyze the contouring performance and show the improved outcomes, rigorous proof is furnished on both the stability in the time domain and the convergence in the iteration domain; and the real-time experiments also illustrate that the requirements of precision motion control towards high-speed and complex-curvature references can be satisfied using the proposed method, without prior knowledge of the boundary to the unknown dynamics.
Generalized Iterative Super-Twisting Sliding Mode Control: A Case Study on Flexure-Joint Dual-Drive H-Gantry Stage
Authors: Wenxin Wang, Jun Ma, Zilong Cheng, Xiaocong Li, Abdullah Al Mamun, Tong Heng Lee
Date: 2021-03-23 00:00:00

Mechatronic systems are commonly used in the industry, where fast and accurate motion performance is always required to guarantee manufacturing precision and efficiency. Nevertheless, the system model and parameters are difficult to be obtained accurately. Moreover, the high-order modes, strong coupling in the multi-axis systems, or unmodeled frictions will bring uncertain dynamics to the system. To overcome the above-mentioned issues and enhance the motion performance, this paper introduces a novel intelligent and totally model-free control method for mechatronic systems with unknown dynamics. In detail, a 2-degree-of-freedom (DOF) architecture is designed, which organically merges a generalized super-twisting algorithm with a unique iterative learning law. The controller solely utilizes the input-output data collected in iterations such that it works without any knowledge of the system parameters. The rigorous proof of convergence ability is given and a case study on flexture-joint dual-drive H-gantry stage is shown to validate the effectiveness of the proposed method.
On the Value of Preview Information For Safety Control
Authors: Zexiang Liu, Necmiye Ozay
Date: 2021-03-19 00:00:00

Incorporating predictions of external inputs, which can otherwise be treated as disturbances, has been widely studied in control and computer science communities. These predictions are commonly referred to as preview in optimal control and lookahead in temporal logic synthesis. However, little work has been done for analyzing the value of preview information for safety control for systems with continuous state spaces. In this work, we start from showing general properties for discrete-time nonlinear systems with preview and strategies on how to determine a good preview time, and then we study a special class of linear systems, called systems in Brunovsky canonical form, and show special properties for this class of systems. In the end, we provide two numerical examples to further illustrate the value of preview in safety control.
The Adoption of Blockchain-based Decentralized Exchanges
Authors: Agostino Capponi, Ruizhe Jia
Date: 2021-03-16 00:00:00

We investigate the market microstructure of Automated Market Makers (AMMs), the most prominent type of blockchain-based decentralized exchanges. We show that the order execution mechanism yields token value loss for liquidity providers if token exchange rates are volatile. AMMs are adopted only if their token pairs are of high personal use for investors, or the token price movements of the pair are highly correlated. A pricing curve with higher curvature reduces the arbitrage problem but also investors' surplus. Pooling multiple tokens exacerbates the arbitrage problem. We provide statistical support for our main model implications using transaction-level data of AMMs.
The international forward guidance transmission under a global liquidity trap
Authors: Daisuke Ida, Hirokuni Iiboshi
Date: 2021-03-23 00:00:00

This paper quantitatively explores the interaction effect of forward guidance (FG) on international monetary policy transmission using a standard two-country new Keynesian model with a global liquidity trap. First, we show that the magnitude of the constant risk aversion coefficient (CRRA) is important in determining the beggar-thy-neighbor and prosper-thy-neighbor effects in foreign economies when the home country only faces the zero lower bound (ZLB) constraints. Second,we demonstrate that both countries may benefit from adopting only the home country's FG policies if the home central bank only faces the ZLB. Third, we find the potential benefit of the FG interaction effect between two countries. Thus, we document the possibility that home and foreign central banks can benefit from monetary policy coordination by adopting the same duration of FG quarters.
Features of aminopropyl modified mesoporous silica nanoparticles. Implications on the active targeting capability
Authors: M. V. Cabanas, D. Lozano, A. Torres-Pardo, C. Sobrino, J. Gonzalez-Calbet, D. Arcos, M. Vallet-Regi
Date: 2021-03-23 00:00:00

Aminopropyl modified mesoporous SiO2 nanoparticles, MCM-41 type, have been synthesized by the co-condensation method from tetraethylorthosilicate (TEOS) and aminopropyltriethoxysilane (APTES). By means of modifying TEOS/APTES ratio we have carried out an in-depth characterization of the nanoparticles as a function of APTES content. Surface charge and nanoparticles morphology were strongly influenced by the amount of APTES and particles changed from hexagonal to bean-like morphology insofar APTES increased. Besides, the porous structure was also affected, showing a contraction of the lattice parameter and pore size, while increasing the wall thickness. These results bring about new insights about the nanoparticles formation during the co-condensation process. The model proposed herein considers that different interactions stablished between TEOS and APTES with the structure directing agent have consequences on pore size, wall thickness and particle morphology. Finally, APTES is an excellent linker to covalently attach active targeting agents such as folate groups. We have hypothesized that APTES could also play a role in the biological behavior of the nanoparticles. So, the internalization efficiency of the nanoparticles has been tested with cancerous LNCaP and non-cancerous preosteoblast-like MC3T3-E1 cells. The results indicate a cooperative effect between aminopropylsilane presence and folic acid, only for the cancerous LNCaP cell line.
Multifunctional antibiotic- and zinc-containing mesoporous bioactive glass scaffolds to fight bone infection
Authors: C. Heras, J. Jimenez Holguin, A. L. Doadrio, M. Vallet-Regi, S. Sanchez-Salcedo, A. J. Salinas
Date: 2021-03-23 00:00:00

Bone regeneration is a clinical challenge that requires multiple approaches. Sometimes, it also includes the development of new osteogenic and antibacterial biomaterials to treat the occurrence of possible infection processes derived from surgery. This study evaluates the antibacterial properties of meso-macroporous scaffolds coated with gelatin and based on a bioactive glass and after being doped with 4% ZnO (4ZN-GE) and loaded with saturated and minimally inhibitory concentrations of one of the antibiotics levofloxacin (LEVO), vancomycin (VANCO), rifampicin (RIFAM) or gentamicin (GENTA). After the physicochemical characterization of the materials, inorganic ion and antibiotic release studies were performed from the scaffolds. In addition, molecular modeling allowed the determination of electrostatic potential density maps and hydrogen bonds of the antibiotics and the glass matrix. In vitro antibacterial studies (in plankton, inhibition halos and biofilm destruction) with S. aureus and E. coli as model bacteria showed a synergistic effect of zinc ions and antibiotics. The effect was especially noticeable in planktonic cultures of S. aureus with 4ZN-GE scaffolds loaded with VANCO, LEVO or RIFAM and in cultures of E. coli with LEVO or GENTA. Furthermore, S. aureus biofilms were completely destroyed by 4ZN-GE scaffolds loaded with VANCO, LEVO or RIFAM and total destruction of E. coli biofilm was achieved with 4ZN-GE scaffolds loaded with GENTA or LEVO. This approach could be an important step in the fight against microbial resistance and provide much needed options for the treatment of bone infection.
Cryptocurrency Dynamics: Rodeo or Ascot?
Authors: Konstantin Häusler, Wolfgang Karl Härdle
Date: 2021-03-23 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

We model the dynamics of the cryptocurrency (CC) asset class via a stochastic volatility with correlated jumps (SVCJ) model with rolling-window parameter estimates. By analyzing the time-series of parameters, stylized patterns are observable which are robust to changes of the window size and supported by cluster analysis. During bullish periods, volatility stabilizes at low levels and the size and volatility of jumps in mean decreases. In bearish periods though, volatility increases and takes longer to return to its long-run trend. Furthermore, jumps in mean and jumps in volatility are independent. With the rise of the CC market in 2017, a level shift of the volatility of volatility occurred. All codes are available on Quantlet.com.
Strong Detectability and Observers for Linear Time Varying Systems
Authors: Markus Tranninger, Richard Seeber, Juan G. Rueda-Escobedo, Martin Horn
Date: 2021-03-23 00:00:00
Tasks: State Estimation

This work presents a notion of strong detectability for linear time varying systems affected by unknown inputs. It is shown that this notion is equivalent to detectability of an auxiliary system without unknown inputs. This allows a straightforward observer design for dependable state estimation in the presence of unknown inputs. The design reduces to a deterministic Kalman filter design problem, where the observer gains can be obtained from the solution of a differential Riccati equation. The efficacy of the proposed approach is demonstrated by means of a numerical example.
A hybrid approach to Vietnamese word segmentation
Authors: Tuan-Phong Nguyen, Anh-Cuong Le
Date: 2016-12-29 00:00:00
Tasks: Segmentation, Sentence, Vietnamese Word Segmentation

Word segmentation is the very first task for Vietnamese language processing. Word-segmented text is the input of almost other NLP tasks. This task faces some challenges due to specific characteristics of the language. As in many other Asian languages such as Japanese, Korean and Chinese, white spaces in Vietnamese are not always used as word separators and a word may contain one or more syllables. In this paper, we propose an efficient hybrid approach to detect word boundary for Vietnamese texts using logistic regression as a binary classifier combining with longest matching algorithm. First, longest matching algorithm is used to catch words that contain more than two syllables in input sentence. Next, the system utilizes the classifier to determine the boundary of 2-syllable words and proper names. Then, the predictions having low confidence conducted by the classifier are verified by a dictionary to get the final result. Our system can achieve an F-measure of 98.82% which is the most accurate result for Vietnamese word segmentation to the best of our knowledge. Moreover, the system also has a high speed. It can run word segmentation for nearly 34k tokens per second.
Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art
Authors: Patrick Lewis, Myle Ott, Jingfei Du, Veselin Stoyanov
Date: 2020-11-01 00:00:00

A large array of pretrained models are available to the biomedical NLP (BioNLP) community. Finding the best model for a particular task can be difficult and time-consuming. For many applications in the biomedical and clinical domains, it is crucial that models can be built quickly and are highly accurate. We present a large-scale study across 18 established biomedical and clinical NLP tasks to determine which of several popular open-source biomedical and clinical NLP models work well in different settings. Furthermore, we apply recent advances in pretraining to train new biomedical language models, and carefully investigate the effect of various design choices on downstream performance. Our best models perform well in all of our benchmarks, and set new State-of-the-Art in 9 tasks. We release these models in the hope that they can help the community to speed up and increase the accuracy of BioNLP and text mining applications.
A Minimal Training Strategy to Play Flappy Bird Indefinitely with NEAT
Authors: Matheus G. Cordeiro, Paulo Bruno S. Serafim, Yuri Lenon B. Nogueira, Creto A. Vidal, Joaquim B. Cavalcante Neto
Date: 2019-12-09 00:00:00

A large number of algorithms to generate behaviors of game agents have been developed in recent years. Most of them are based on artificial intelligence techniques that need a training stage. In this context, this paper proposes a minimal training strategy to develop autonomous virtual players using the NEAT neuroevolutionary algorithm to evolve an agent capable of playing the Flappy Bird game. NEAT was used to find the simplest neural network architecture that can perfectly play the game. The modeling of the scenarios and the fitness function were set to ensure adequate representation of the problem compared to the real game. The fitness function is a weighted average based on multiple scenarios and scenario-specific components. Coupling the minimal training strategy, a representative fitness and NEAT, the algorithm had a short convergence time (around 20 generations), with a low complexity network and achieved the perfect behavior in the game.
August 1, 2015 Statement from DENG YUHAO： Exit from the Communist Party of China (CCP)
Authors: deng yuhao
Date: 2015-08-01 00:00:00

August 1, 2015 Statement: Exit from the Communist Party of China (CCP) and all organizations and institutions controlled by the Communist Party of China (CPP).
Date of announcement: August 1, 2015
Statement: DENG YUHAO
邓玉豪2015年8月1日声明：退出中国共产党以及中国共产党所控制的所有组织与机构。
Authors: dyhdyh
Date: 2015-08-01 00:00:00

邓玉豪2015年8月1日声明：退出中国共产党以及中国共产党所控制的所有组织与机构。
声明日期：2015年8月1日

声明人: 邓玉豪
2015-08-01 
中国大陆
Efficient Regional Memory Network for Video Object Segmentation
Authors: Haozhe Xie, Hongxun Yao, Shangchen Zhou, Shengping Zhang, Wenxiu Sun
Date: 2021-03-24 00:00:00
Proceeding: CVPR 2021 1
Tasks: Object, One-shot visual object segmentation, Optical Flow Estimation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Recently, several Space-Time Memory based networks have shown that the object cues (e.g. video frames as well as the segmented object masks) from the past frames are useful for segmenting objects in the current frame. However, these methods exploit the information from the memory by global-to-global matching between the current and past frames, which lead to mismatching to similar objects and high computational complexity. To address these problems, we propose a novel local-to-local matching solution for semi-supervised VOS, namely Regional Memory Network (RMNet). In RMNet, the precise regional memory is constructed by memorizing local regions where the target objects appear in the past frames. For the current query frame, the query regions are tracked and predicted based on the optical flow estimated from the previous frame. The proposed local-to-local matching effectively alleviates the ambiguity of similar objects in both memory and query frames, which allows the information to be passed from the regional memory to the query region efficiently and effectively. Experimental results indicate that the proposed RMNet performs favorably against state-of-the-art methods on the DAVIS and YouTube-VOS datasets.
Improved Estimation of Concentration Under $\ell_p$-Norm Distance Metrics Using Half Spaces
Authors: Jack Prescott, Xiao Zhang, David Evans
Date: 2021-03-24 00:00:00
Proceeding: ICLR 2021 1

Concentration of measure has been argued to be the fundamental cause of adversarial vulnerability. Mahloujifar et al. presented an empirical way to measure the concentration of a data distribution using samples, and employed it to find lower bounds on intrinsic robustness for several benchmark datasets. However, it remains unclear whether these lower bounds are tight enough to provide a useful approximation for the intrinsic robustness of a dataset. To gain a deeper understanding of the concentration of measure phenomenon, we first extend the Gaussian Isoperimetric Inequality to non-spherical Gaussian measures and arbitrary $\ell_p$-norms ($p \geq 2$). We leverage these theoretical insights to design a method that uses half-spaces to estimate the concentration of any empirical dataset under $\ell_p$-norm distance metrics. Our proposed algorithm is more efficient than Mahloujifar et al.'s, and our experiments on synthetic datasets and image benchmarks demonstrate that it is able to find much tighter intrinsic robustness bounds. These tighter estimates provide further evidence that rules out intrinsic dataset concentration as a possible explanation for the adversarial vulnerability of state-of-the-art classifiers.
Light Field Reconstruction Using Convolutional Network on EPI and Extended Applications
Authors: Gaochang Wu, Yebin Liu, Lu Fang, Qionghai Dai, Tianyou Chai
Date: 2021-03-24 00:00:00

In this paper, a novel convolutional neural network (CNN)-based framework is developed for light field reconstruction from a sparse set of views. We indicate that the reconstruction can be efficiently modeled as angular restoration on an epipolar plane image (EPI). The main problem in direct reconstruction on the EPI involves an information asymmetry between the spatial and angular dimensions, where the detailed portion in the angular dimensions is damaged by undersampling. Directly upsampling or super-resolving the light field in the angular dimensions causes ghosting effects. To suppress these ghosting effects, we contribute a novel "blur-restoration-deblur" framework. First, the "blur" step is applied to extract the low-frequency components of the light field in the spatial dimensions by convolving each EPI slice with a selected blur kernel. Then, the "restoration" step is implemented by a CNN, which is trained to restore the angular details of the EPI. Finally, we use a non-blind "deblur" operation to recover the spatial high frequencies suppressed by the EPI blur. We evaluate our approach on several datasets, including synthetic scenes, real-world scenes and challenging microscope light field data. We demonstrate the high performance and robustness of the proposed framework compared with state-of-the-art algorithms. We further show extended applications, including depth enhancement and interpolation for unstructured input. More importantly, a novel rendering approach is presented by combining the proposed framework and depth information to handle large disparities.