pwc-archive/papers-with-abstracts huggingface dataset:
Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences
Authors: Quan Yuan, Gao Cong, Aixin Sun
Date: 2014-11-01 00:00:00
Tasks: Recommendation Systems

The availability of user check-in data in large volume from the rapid growing location-based social networks (LBSNs) enables a number of important location-aware services. Point-of-interest (POI) recommendation is one of such services, which is to recommend POIs that users have not visited before. It has been observed that: (i) users tend to visit nearby places, and (ii) users tend to visit different places in different time slots, and in the same time slot, users tend to periodically visit the same places. For example, users usually visit a restaurant during lunch hours, and visit a pub at night. In this paper, we focus on the problem of time-aware POI recommendation, which aims at recommending a list of POIs for a user to visit at a given time. To exploit both geographical and temporal influences in time aware POI recommendation, we propose the Geographical-Temporal influences Aware Graph (GTAG) to model check-in records, geographical influence and temporal influence. For effective and efficient recommendation based on GTAG, we develop a preference propagation algorithm named Breadth first Preference Propagation (BPP). The algorithm follows a relaxed breath-first search strategy, and returns recommendation results within at most 6 propagation steps. Our experimental results on two real-world datasets show that the proposed graph-based approach outperforms state-of-the-art POI recommendation methods substantially.
Asymptotics of Proximity Operator for Squared Loss and Performance Prediction of Nonconvex Sparse Signal Recovery
Authors: Ryo Hayakawa
Date: 2021-03-18 00:00:00
Tasks: compressed sensing

Proximal splitting-based convex optimization is a promising approach to linear inverse problems because we can use some prior knowledge of the unknown variables explicitly. An understanding of the behavior of the optimization algorithms would be important for the tuning of the parameters and the development of new algorithms. In this paper, we first analyze the asymptotic property of the proximity operator for the squared loss function, which appears in the update equations of some proximal splitting methods for linear inverse problems. Our analysis shows that the output of the proximity operator can be characterized with a scalar random variable in the large system limit. Moreover, we apply the asymptotic result to the prediction of optimization algorithms for compressed sensing. Simulation results demonstrate that the MSE performance of the Douglas-Rachford algorithm can be well predicted in compressed sensing with the $\ell_{1}$ optimization. We also examine the behavior of the prediction for the case with nonconvex smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP) regularization.
Formation Control for Connected and Automated Vehicles on Multi-lane Roads: Relative Motion Planning and Conflict Resolution
Authors: Mengchi Cai, Qing Xu, Chaoyi Chen, Jiawei Wang, Keqiang Li, Jianqiang Wang, Qianying Zhu
Date: 2021-03-18 00:00:00
Tasks: Decision Making, Motion Planning, Trajectory Planning

Multi-vehicle coordinated decision making and control can improve traffic efficiency while guaranteeing driving safety. Formation control is a typical multi-vehicle coordination method in the multi-lane scenario. Among the existing formation control methods, the formation switching process is predefined and the collision-free behavior of vehicles has not been considered and explained in detail. This paper proposes a formation control method for multiple Connected and Automated Vehicles (CAVs) on multi-lane roads. Firstly, a bi-level planning framework is proposed to switch the structure of the formation in different scenarios smoothly and effectively. Secondly, the relative coordinate system is established and the conflict-free relative paths are planned in the upper level. Then, multi-stage trajectory planning and tracking are modeled and solved as an optimal control problem with path constraints in the lower level. Next, case study is conducted to verify the function of the proposed method in different scenarios. Finally, simulation in the lane-drop bottleneck scenario is carried out under different traffic volume and numerical results indicate that the proposed method can improve both traffic efficiency and fuel economy at high traffic volume.
Distributed Clustering for User Devices Under Unmanned Aerial Vehicle Coverage Area during Disaster Recovery
Authors: Abdu Saif, Kaharudin Bin Dimyati, Kamarul Ariffin Bin Noordin, Nor Shahida Mohd. Shah, S. H. Alsamhi, Qazwan Abdullah, Nabil Farah
Date: 2021-03-14 00:00:00
Tasks: Clustering

An Unmanned Aerial Vehicle (UAV) is a promising technology for providing wireless coverage to ground user devices. For all the infrastructure communication networks destroyed in disasters, UAVs battery life is challenging during service delivery in a post-disaster scenario. Therefore, selecting cluster heads among user devices plays a vital role in detecting UAV signals and processing data for improving UAV energy efficacy and reliable Connectivity. This paper focuses on the performance evaluation of the clustering approach performance in detecting wireless coverage services with improving energy efficiency. The evaluation performance is a realistic simulation for the ground to air channel Line of Sight (LoS). The results show that the cluster head can effectively link the UAVs and cluster members at minimal energy expenditure. The UAVs altitudes and path loss exponent affected user devices for detecting wireless coverage. Moreover, the bit error rate in the cluster heads is considered for reliable Connectivity in post disaster. Clustering stabilizes the clusters linking the uncovered nodes to the UAV, and its effectiveness in doing so resulted in its ubiquity in emergency communication systems.
Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series
Authors: Ginno Millán, Román Osorio-Comparán, Gastón Lefranc
Date: 2021-03-02 00:00:00
Tasks: Time Series, Time Series Analysis

This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent. The methodology addresses the exhaustive analysis of the Hurst exponent considering bias behaviour, standard deviation, and Mean Squared Error using fractional Gaussian noise signals with stationary increases. Our results show that the Whittle estimator successfully estimates the Hurst exponent in series with few points. Based on the results obtained, a minimum length for the time series is empirically proposed. Finally, to validate the results, the methodology is applied to real traffic captures in a high-speed computer network.
Video-Capable Ultrasonic Wireless Communications through Biological Tissues
Authors: Gizem Tabak, Sijung Yang, Rita Miller, Michael Oelze, Andrew Singer
Date: 2019-09-29 00:00:00

Wireless implanted medical devices (IMDs) provide ease and comfort to an increasing number of patients and physicians. Currently, radiofrequency electromagnetic waves are the most commonly used method for communicating wirelessly with IMDs. However, due to the restrictions on the available bandwidth and the employable power, data rates of RF-based IMDs are limited to 267 kbps. Considering standard definition video streaming requiring 1.2 mbps and high definition requiring 3 mbps bitrates, it is not possible to use such devices for high data rate communication applications such as video streaming. In this work, an alternative method that utilizes ultrasonic waves for IMDs to relay information at high data rates is introduced. Advanced signal processing and communication techniques are tailored to realize the full potential of the ultrasonic channel through biological tissues. Consequently, the experiments demonstrate that video-capable data rates can be achieved with mm-sized transducers communicating through water, ex vivo beef liver, ex vivo pork chop, and in situ through the rabbit abdomen.
SEPTIC (Self Protecting Database): Detecting Injection Attacks and Vulnerabilities Inside the DBMS
Authors: Iberia Medeiros, Miguel Beatriz, Nuno Neves, Miguel Correia
Date: 2019-09-03 00:00:00

Databases continue to be the most commonly used
backend storage in enterprises, but they are often integrated with
vulnerable applications, such as web frontends, which allow injection attacks to be performed. The effectiveness of such attacks
stems from a semantic mismatch between how SQL queries are
believed to be executed and the actual way in which databases
process them. This leads to subtle vulnerabilities in the way input
validation is done in applications. In this paper, we propose SEPTIC, a mechanism for DBMS attack prevention, which can also
assist on the identification of the vulnerabilities in the applications.
The mechanism was implemented in MySQL and evaluated experimentally with various applications and alternative protection
approaches. Our results show no false negatives and no false positives with SEPTIC, on the contrary to other solutions. They also
show that SEPTIC introduces a low performance overhead, in the
order of 2.2%.
Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation
Authors: Carl Yang, Lanxiao Bai, Chao Zhang, Quan Yuan, J. Han profile
Date: 2017-08-01 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recommender system is one of the most popular data mining topics that keep drawing extensive attention from both academia and industry. Among them, POI (point of interest) recommendation is extremely practical but challenging: it greatly benefits both users and businesses in real-world life, but it is hard due to data scarcity and various context. While a number of algorithms attempt to tackle the problem w.r.t. specific data and problem settings, they often fail when the scenarios change. In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs. To enable such a framework, we develop PACE (Preference And Context Embedding), a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and various context associated with users and POIs. We show that PACE successfully bridges CF (collaborative filtering) and SSL by generalizing the de facto methods matrix factorization of CF and graph Laplacian regularization of SSL. Extensive experiments on two real location-based social network datasets demonstrate the effectiveness of PACE.
6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities
Authors: Harsh Tataria, Mansoor Shafi, Andreas F. Molisch, Mischa Dohler, Henrik Sjöland, Fredrik Tufvesson
Date: 2020-08-07 00:00:00

Mobile communications have been undergoing a generational change every ten years or so. However, the time difference between the so-called "G's" is also decreasing. While fifth-generation (5G) systems are becoming a commercial reality, there is already significant interest in systems beyond 5G, which we refer to as the sixth-generation (6G) of wireless systems. In contrast to the already published papers on the topic, we take a top-down approach to 6G. We present a holistic discussion of 6G systems beginning with lifestyle and societal changes driving the need for next generation networks. This is followed by a discussion into the technical requirements needed to enable 6G applications, based on which we dissect key challenges, as well as possibilities for practically realizable system solutions across all layers of the Open Systems Interconnection stack. Since many of the 6G applications will need access to an order-of-magnitude more spectrum, utilization of frequencies between 100 GHz and 1 THz becomes of paramount importance. As such, the 6G eco-system will feature a diverse range of frequency bands, ranging from below 6 GHz up to 1 THz. We comprehensively characterize the limitations that must be overcome to realize working systems in these bands; and provide a unique perspective on the physical, as well as higher layer challenges relating to the design of next generation core networks, new modulation and coding methods, novel multiple access techniques, antenna arrays, wave propagation, radio-frequency transceiver design, as well as real-time signal processing. We rigorously discuss the fundamental changes required in the core networks of the future that serves as a major source of latency for time-sensitive applications. While evaluating the strengths and weaknesses of key 6G technologies, we differentiate what may be achievable over the next decade, relative to what is possible.
Lectin-conjugated pH-responsive mesoporous silica nanoparticles for targeted bone cancer treatment
Authors: M. Martinez-Carmona, D. Lozano, M. Colilla, M. Vallet-Regi
Date: 2021-03-18 00:00:00

A novel multifunctional nanodevice based in doxorubicin (DOX)- loaded mesoporous silica nanoparticles (MSNs) as nanoplatforms for the assembly of different building blocks has been developed for bone cancer treatment. These building blocks consists of: i) a polyacrylic acid (PAA) capping layer grafted to MSNs via an acid-cleavable acetal linker, to minimize premature cargo release and provide the nanosystem of pHresponsive drug delivery ability; and ii) a targeting ligand, the plant lectin concanavalin A (ConA), able to selectively recognize, bind and internalize owing to certain cell-surface glycans, such as sialic acids (SA), overexpressed in given tumor cells. This multifunctional nanosystem exhibits a noticeable higher internalization degree into human osteosarcoma cells (HOS), overexpressing SA, compared to healthy preosteoblast cells (MC3T3-E1). Moreover, the results indicate that small DOX loading leads to almost 100% of osteosarcoma cell death in comparison with healthy bone cells, which significantly preserve their viability. Besides, this nanodevice has a cytotoxicity on tumor cells 8- fold higher than that caused by the free drug. These findings demonstrate that the synergistic combination of different building blocks into a unique nanoplatform increases antitumor effectiveness and decreases toxicity towards normal cells. This line of attack opens up new insights in targeted bone cancer therapy.
Local Electricity Market Design Utilizing Dynamic Network Usage Tariff
Authors: Bence Suto, Daniel Divenyi
Date: 2021-03-18 00:00:00

The new technologies emerging in the energy sector pose new requirements for both the regulation and operation of the electricity grid. Revised tariff structures and the introduction of local markets are two approaches that could tackle the issues resulting from the increasing number of active end-users. However, a smooth transition from the traditional schemes is critical, thus creating the need for architecture that can be implemented in the current circumstances. This paper proposes a local market concept and a corresponding dynamic tariff system, which can be operated parallel to the current retail market. The participants of the market can trade energy peer-to-peer via a platform that allocates proper network charges to all transactions. The calculated tariffs consider the physical effect of the transactions on the grid in terms of nodal voltage deviations, branch current flows, and overall system losses. The proposed method is tested on the IEEE European LV test feeder through market simulations. The results imply that with the proper tuning of DNUT (Dynamic Network Usage Tariff) components, the end-users can realize surplus, while the security of network operation is also ensured.
PySTACHIO: Python Single-molecule TrAcking stoiCHiometry Intensity and simulatiOn, a flexible, extensible, beginner-friendly and optimized program for analysis of single-molecule microscopy
Authors: Jack W Shepherd, Ed J Higgins, Adam J M Wollman, Mark C Leake
Date: 2021-03-18 00:00:00
Tasks: Art Analysis, Benchmarking

As camera pixel arrays have grown larger and faster, and optical microscopy techniques ever more refined, there has been an explosion in the quantity of data acquired during routine light microcopy. At the single-molecule level, analysis involves multiple steps and can rapidly become computationally expensive, in some cases intractable on office workstations. Complex bespoke software can present high activation barriers to entry for new users. Here, we redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with GUI and command-line implementations to facilitate use on local machines and remote clusters, by beginners and advanced users alike. We demonstrate that its performance is on par with previous MATLAB implementations but runs an order of magnitude faster. We tested it against challenge data and demonstrate its performance is comparable to state-of-the-art analysis platforms. We show the code can extract fluorescence intensity values for single reporter dye molecules and, using these, estimate molecular stoichiometries and cellular copy numbers of fluorescently-labeled biomolecules. It can evaluate 2D diffusion coefficients for the characteristically short single-particle tracking data. To facilitate benchmarking we include data simulation routines to compare different analysis programs. Finally, we show that it works with 2-color data and enables colocalization analysis based on overlap integration, to infer interactions between differently labelled biomolecules. By making this freely available we aim to make complex light microscopy single-molecule analysis more democratized.
Leveraged ETF Investing
Authors: Tal Miller
Date: 2021-03-18 00:00:00

It is common knowledge that leverage can increase the potential returns of an investment, at the expense of increased risk. For a passive investor in the stock market, leverage can be achieved using margin debt or leveraged-ETFs. We perform bootstrapped Monte-Carlo simulations of leveraged (and unleveraged) mixed portfolios of stocks and bonds, based on past stock market data, and show that leverage can amplify the potential returns, without significantly increasing the risk for long-term investors.
A Physics-based and Data-driven Linear Three-Phase Power Flow Model for Distribution Power Systems
Authors: Yitong Liu, Zhengshuo Li, Yu Zhou
Date: 2021-03-18 00:00:00

Distribution power systems (DPSs) are mostly unbalanced, and their loads may have notable static voltage characteristics (ZIP loads). Hence, despite abundant papers on linear single-phase power flow models, it is still necessary to study linear three-phase distribution power flow models. To this end, this paper proposes a physics-based and data-driven linear three-phase power flow model for DPSs. We first formulate how to amalgamate data-driven techniques into a physics-based power flow model to obtain our linear model. This amalgamation makes our linear model independent of the assumptions commonly used in the literature (e.g., nodal voltages are nearly 1.0 p.u.) and thus have a relatively high accuracy generally - even when those assumptions become invalid. We then reveal how to apply our model to the DPSs with ZIP loads. We also show that with the Huber penalty function employed, the adverse impact of bad data on our model's accuracy is significantly reduced, rendering our model robust against poor data quality. Case studies have demonstrated that our model generally has 2 to over 10-fold smaller average errors than other linear power flow models, enjoys a satisfying accuracy against bad data, and facilitates a faster solution to DPS analysis and optimization problems.
Finite-word-length FPGA implementation of model predictive control for ITER resistive wall mode control
Authors: Samo Gerkšič, Boštjan Pregelj
Date: 2021-03-18 00:00:00
Tasks: Model Predictive Control

In advanced tokamak scenarios, active feedback control of unstable resistive wall modes (RWM) may be required. A RWM is an instability due to plasma kink at higher plasma pressure, moderated by the presence of a resistive wall surrounding the plasma. We address the dominant kink instability associated with the main nonaxisymmetric (n = 1) RWM, described by the CarMa model. Model predictive control (MPC) is used, with the aim of enlarging the domain of attraction of the unstable RWM modes subject to power-supply voltage constraints. The implementation of MPC is challenging, because the related quadratic programming (QP) on-line optimization problems must be solved at a sub-ms sampling rate. Using complexity-reduction pre-processing techniques and a primal fast gradient method (FGM) QP solver, sufficiently short computation times for ITER are reachable using a standard personal computer (PC). In this work we explore even faster finite-word-length (FWL) implementation using field-programmable gate arrays (FPGA), which would facilitate experimental testing of such control algorithms on dynamically faster medium-sized tokamaks, and compare the computational accuracy and time with the PC implementation.
CDFI: Compression-Driven Network Design for Frame Interpolation
Authors: Tianyu Ding, Luming Liang, Zhihui Zhu, Ilya Zharkov
Date: 2021-03-18 00:00:00
Proceeding: CVPR 2021 1
Tasks: Video Frame Interpolation

DNN-based frame interpolation--that generates the intermediate frames given two consecutive frames--typically relies on heavy model architectures with a huge number of features, preventing them from being deployed on systems with limited resources, e.g., mobile devices. We propose a compression-driven network design for frame interpolation (CDFI), that leverages model pruning through sparsity-inducing optimization to significantly reduce the model size while achieving superior performance. Concretely, we first compress the recently proposed AdaCoF model and show that a 10X compressed AdaCoF performs similarly as its original counterpart; then we further improve this compressed model by introducing a multi-resolution warping module, which boosts visual consistencies with multi-level details. As a consequence, we achieve a significant performance gain with only a quarter in size compared with the original AdaCoF. Moreover, our model performs favorably against other state-of-the-arts in a broad range of datasets. Finally, the proposed compression-driven framework is generic and can be easily transferred to other DNN-based frame interpolation algorithm. Our source code is available at https://github.com/tding1/CDFI.
On the LRD of the Aggregated Traffic Flows in High-Speed Computer Networks
Authors: G. Millán
Date: 2021-03-05 00:00:00
Tasks: Time Series, Time Series Analysis

This paper studies and analyses the behavior of the Long-Range Dependence in network traffic after classifying traffic flows in aggregated time series. Following Differentiated Services architecture principles, the generic Quality of Service applications that requirements and use the transport control protocol, a basic classification criterion of time series is established. Using the fractal theory, the resulting time series are analyzed. The Hurst exponent is estimated and used as a measure of traffic burstiness and Long-Range Dependency in each traffic class. The traffic volume per class is also measured. The study uses traffic traces collected at the core switch at the Electric Engineering Department at Universidad de Santiago de Chile in different periods of network activity.