arXiv subjects

Lu Wang

Publications and source records attributed to Lu Wang.

At least 289 records · Page 16Linked to original sources

arXiv2023

Joint Communication and Sensing in RIS-enabled mmWave Networks

Empowering cellular networks with augmented sensing capabilities is one of the key research areas in 6G communication systems. Recently, we have witnessed a plethora of efforts to devise solutions that integrate sensing capabilities into communication systems, i.e., joint communication and sensing (JCAS). However, most prior works do not consider the impact of reconfigurable intelligent surfaces (RISs) on JCAS systems, especially at millimeter-wave (mmWave) bands. Given that RISs are expected to become an integral part of cellular systems, it is important to investigate their potential in cellular networks beyond communication goals. In this paper, we study mmWave orthogonal frequency-division multiplexing (OFDM) JCAS systems in the presence of RISs. Specifically, we jointly design the hybrid beamforming and RIS phase shifts to guarantee the sensing functionalities via minimizing a chordal-distance metric, subject to signal-to-interference-plus-noise (SINR) and power constraints. The non-convexity of the investigated problem poses a challenge which we address by proposing a solution based on the penalty method and manifold-based alternating direction method of multipliers (ADMM). Simulation results demonstrate that under various settings both sensing and communication experience improved performance when the RIS is adequately designed. In addition, we discuss the tradeoff between sensing and communication.

eess.SP↗

arXiv2023

Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails

Research on email anomaly detection has typically relied on specially prepared datasets that may not adequately reflect the type of data that occurs in industry settings. In our research, at a major financial services company, privacy concerns prevented inspection of the bodies of emails and attachment details (although subject headings and attachment filenames were available). This made labeling possible anomalies in the resulting redacted emails more difficult. Another source of difficulty is the high volume of emails combined with the scarcity of resources making machine learning (ML) a necessity, but also creating a need for more efficient human training of ML models. Active learning (AL) has been proposed as a way to make human training of ML models more efficient. However, the implementation of Active Learning methods is a human-centered AI challenge due to potential human analyst uncertainty, and the labeling task can be further complicated in domains such as the cybersecurity domain (or healthcare, aviation, etc.) where mistakes in labeling can have highly adverse consequences. In this paper we present research results concerning the application of Active Learning to anomaly detection in redacted emails, comparing the utility of different methods for implementing active learning in this context. We evaluate different AL strategies and their impact on resulting model performance. We also examine how ratings of confidence that experts have in their labels can inform AL. The results obtained are discussed in terms of their implications for AL methodology and for the role of experts in model-assisted email anomaly screening.

cs.HC↗

arXiv2023

Maximal Violation of Kirchhoff's Law in Planar Heterostructures

Violating Kirchhoff's law has so far required nonreciprocal materials patterned in microstructures. In these configurations, the excitation of a guided or polaritonic mode that lies outside the light cone, often via gratings, was a requirement. Here, we describe how nonreciprocity manifests itself in pattern-free heterostructures. We demonstrate that a resonant mode in a dielectric spacer separating a nonreciprocal film from a back-reflector suffices to maximally violate Kirchhoff's law, and identify the minimal dielectric requirements for such functionality, which are satisfied by currently available materials.

physics.optics↗

arXiv2023

Non-greedy Tree-based Learning for Estimating Global Optimal Dynamic Treatment Decision Rules with Continuous Treatment Dosage

Dynamic treatment regime (DTR) plays a critical role in precision medicine when assigning patient-specific treatments at multiple stages and optimizing a long term clinical outcome. However, most of existing work about DTRs have been focused on categorical treatment scenarios, instead of continuous treatment options. Also, the performances of regular black-box machine learning methods and regular tree learning methods are lack of interpretability and global optimality respectively. In this paper, we propose a non-greedy global optimization method for dose search, namely Global Optimal Dosage Tree-based learning method (GoDoTree), which combines a robust estimation of the counterfactual outcome mean with an interpretable and non-greedy decision tree for estimating the global optimal dynamic dosage treatment regime in a multiple-stage setting. GoDoTree-Learning recursively estimates how the counterfactual outcome mean depends on a continuous treatment dosage using doubly robust estimators at each stage, and optimizes the stage-specific decision tree in a non-greedy way. We conduct simulation studies to evaluate the finite sample performance of the proposed method and apply it to a real data application for optimal warfarin dose finding.

stat.ME↗

arXiv2023

Iteration Complexity of an Infeasible Interior Point Methods for Seconder-order Cone Programming and its Warmstarting

This paper studies the worst case iteration complexity of an infeasible interior point method (IPM) for seconder order cone programming (SOCP), which is more convenient for warmstarting compared with feasible IPMs. The method studied bases on the homogeneous and self-dual model and the Monteiro-Zhang family of searching directions. Its worst case iteration complexity is $O\left(k^{1/2}\log\left(ε^{-1}\right)\right)$, to reduce the primal residual, dual residual, and complementarity gap by a factor of $ε$, where $k$ is the number of cone constraints. The result is the same as the best known result for feasible IPMs. The condition under which warmstarting improves the complexity bound is also studied.

math.OC↗

arXiv2023

The diagnostic utility of endocytoscopy for the detection of esophageal lesions: a systematic review and meta-analysis

Objective: To systematically evaluate the value of endocytoscopy (ECS) in the diagnosis of early esophageal cancer (EC). Methods: Pubmed, Ovid and EMbase databases were searched to collect diagnostic tests of ECS assisted diagnosis of early EC. The retrieval time was from the establishment of the database to August 2022. Review manager 5.4, Stata 16.0 and Meta-Disc 1.4 were used for meta-analysis after two researchers independently screened literature, extracted data and evaluated the bias risk of included studies. Results: A total of 7 studies were included, including 520 lesions. Meta-analysis results showed that the combined sensitivity(SE), specificity(SP), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and positive posterior probability (PPP) of ECS screening for early EC were 0.95[95%CI: 0.84, 0.98], 0.92 [95%CI: 0.83, 0.96], 11.8 [95%CI: 5.3, 26.1], 0.06 [95%CI: 0.02, 0.18], 203 [95%CI: 50, 816], and 75%, respectively. The area (AUC) under the receiver Operating Characteristic curve (SROC) was 0.98[95%CI: 0.96, 0.99]. Conclusions: Current evidence suggests that ECS can be used as an effective screening tool for early EC. Due to the limited number and quality of included studies, it is imperative to conduct more high-quality studies to verify the above conclusions.

q-bio.TO↗

arXiv2022

RMove: Recommending Move Method Refactoring Opportunities using Structural and Semantic Representations of Code

Incorrect placement of methods within classes is a typical code smell called Feature Envy, which causes additional maintenance and cost during evolution. To remove this design flaw, several Move Method refactoring tools have been proposed. To the best of our knowledge, state-of-the-art related techniques can be broadly divided into two categories: the first line is non-machine-learning-based approaches built on software measurement, while the selection and thresholds of software metrics heavily rely on expert knowledge. The second line is machine learning-based approaches, which suggest Move Method refactoring by learning to extract features from code information. However, most approaches in this line treat different forms of code information identically, disregarding their significant variation on data analysis. In this paper, we propose an approach to recommend Move Method refactoring named RMove by automatically learning structural and semantic representation from code fragment respectively. We concatenate these representations together and further train the machine learning classifiers to guide the movement of method to suitable classes. We evaluate our approach on two publicly available datasets. The results show that our approach outperforms three state-of-the-art refactoring tools including PathMove, JDeodorant, and JMove in effectiveness and usefulness. The results also unveil useful findings and provide new insights that benefit other types of feature envy refactoring techniques.

cs.SE↗

arXiv2022

Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning

Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not all users would fully utilize the resources simultaneously. However, how to design an oversubscription policy that improves utilization while satisfying the some safety constraints remains an open problem. Existing methods and industrial practices are over-conservative, ignoring the coordination of diverse resource usage patterns and probabilistic constraints. To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem. Specifically, C2MARL reduces the number of constraints by considering their upper bounds and leverages a multi-agent reinforcement learning paradigm to learn a safe and optimal coordination policy. We evaluate our C2MARL on an internal cloud platform and public cloud datasets. Experiments show that our C2MARL outperforms existing methods in improving utilization ($20\%\sim 86\%$) under different levels of safety constraints.

cs.LG↗

arXiv2022

Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive Structure

Generative models have demonstrated impressive results on Aspect-based Sentiment Analysis (ABSA) tasks, particularly for the emerging task of extracting Aspect-Category-Opinion-Sentiment (ACOS) quadruples. However, these models struggle with implicit sentiment expressions, which are commonly observed in opinionated content such as online reviews. In this work, we introduce GEN-SCL-NAT, which consists of two techniques for improved structured generation for ACOS quadruple extraction. First, we propose GEN-SCL, a supervised contrastive learning objective that aids quadruple prediction by encouraging the model to produce input representations that are discriminable across key input attributes, such as sentiment polarity and the existence of implicit opinions and aspects. Second, we introduce GEN-NAT, a new structured generation format that better adapts autoregressive encoder-decoder models to extract quadruples in a generative fashion. Experimental results show that GEN-SCL-NAT achieves top performance across three ACOS datasets, averaging 1.48% F1 improvement, with a maximum 1.73% increase on the LAPTOP-L1 dataset. Additionally, we see significant gains on implicit aspect and opinion splits that have been shown as challenging for existing ACOS approaches.

cs.CL↗

arXiv2022

Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis

Prior work on ideology prediction has largely focused on single modalities, i.e., text or images. In this work, we introduce the task of multimodal ideology prediction, where a model predicts binary or five-point scale ideological leanings, given a text-image pair with political content. We first collect five new large-scale datasets with English documents and images along with their ideological leanings, covering news articles from a wide range of US mainstream media and social media posts from Reddit and Twitter. We conduct in-depth analyses of news articles and reveal differences in image content and usage across the political spectrum. Furthermore, we perform extensive experiments and ablation studies, demonstrating the effectiveness of targeted pretraining objectives on different model components. Our best-performing model, a late-fusion architecture pretrained with a triplet objective over multimodal content, outperforms the state-of-the-art text-only model by almost 4% and a strong multimodal baseline with no pretraining by over 3%.

cs.CL↗

arXiv2022

Time-aware Prompting for Text Generation

In this paper, we study the effects of incorporating timestamps, such as document creation dates, into generation systems. Two types of time-aware prompts are investigated: (1) textual prompts that encode document timestamps in natural language sentences; and (2) linear prompts that convert timestamps into continuous vectors. To explore extrapolation to future data points, we further introduce a new data-to-text generation dataset, TempWikiBio, containing more than 4 millions of chronologically ordered revisions of biographical articles from English Wikipedia, each paired with structured personal profiles. Through data-to-text generation on TempWikiBio, text-to-text generation on the content transfer dataset, and summarization on XSum, we show that linear prompts on encoder and textual prompts improve the generation quality on all datasets. Despite having less performance drop when testing on data drawn from a later time, linear prompts focus more on non-temporal information and are less sensitive to the given timestamps, according to human evaluations and sensitivity analyses. Meanwhile, textual prompts establish the association between the given timestamps and the output dates, yielding more factual temporal information in the output.

cs.CL↗

arXiv2022

Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation

Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the need for studying interactions among entities when inferring stances. We first introduce a new task, entity-to-entity (E2E) stance detection, which primes models to identify entities in their canonical names and discern stances jointly. To support this study, we curate a new dataset with 10,619 annotations labeled at the sentence-level from news articles of different ideological leanings. We present a novel generative framework to allow the generation of canonical names for entities as well as stances among them. We further enhance the model with a graph encoder to summarize entity activities and external knowledge surrounding the entities. Experiments show that our model outperforms strong comparisons by large margins. Further analyses demonstrate the usefulness of E2E stance detection for understanding media quotation and stance landscape, as well as inferring entity ideology.

cs.CL↗

arXiv2022

Influence of Pre-bottleneck Diversion Devices on Pedestrian Flow

The existence of bottlenecks often leads to the stagnation of pedestrian gatherings, which seriously affects the efficiency of traffic and reduces the flow of pedestrians. Some studies have shown that setting devices in front of bottlenecks can promote pedestrian evacuation under certain conditions. In this paper, the effect of setting diversion devices in front of the exit on pedestrian flow is studied. From our observation, these diversion devices can form a buffer zone before the exit and affect pedestrian behaviors. The evacuation times are found to decrease as the devices became farther away from the exit. In our experiments, it is found that the effect of shunt piles on evacuation is better than in the case of safety barriers and without device conditions. Under the condition of setting up safety barriers approximately 1m and 3m in front of the exit, the evacuation times are extended by 0.88% and 2.67%. For shunt piles, the evacuation times are 11.53% and 14.96% shorter than that of those without a device regarding the different distances to exit (1m and 3m, respectively). In addition, setting up shunt piles reduces the time interval between two consecutive pedestrians. To sum up, in our experimental settings, the diversion devices can effectively improve the average speed ahead of the exit and promote evacuation to become more orderly, which reduces the congestion in the later period of evacuation. In other words, this study demonstrates that a reasonable layout of facilities can not only meet the daily functional requirements but also improve the efficient use of space in emergencies, reducing the probability of crowd conventions and jams.

physics.soc-ph↗

arXiv2022

Simulating nuclear and electronic quantum effects in enzymes

An accurate treatment of the structures and dynamics that lead to enhanced chemical reactivity in enzymes requires explicit treatment of both electronic and nuclear quantum effects. The former can be captured in ab initio molecular dynamics (AIMD) simulations while the latter can be included by performing ab initio path integral molecular dynamics (AI-PIMD) simulations. Both AIMD and AI-PIMD simulations have traditionally been computationally prohibitive for large enzymatic systems. Recent developments in streaming computer architectures and new algorithms to accelerate path integral simulations now make these simulations practical for biological systems, allowing elucidation of enzymatic reactions in unprecedented detail. In this chapter, we summarize these recent developments and discuss practical considerations for applying AIMD and AI-PIMD simulations to enzymes.

physics.chem-ph↗

arXiv2022

Extensive Study of Multiple Deep Neural Networks for Complex Random Telegraph Signals

Time-fluctuating signals are ubiquitous and diverse in many physical, chemical, and biological systems, among which random telegraph signals (RTSs) refer to a series of instantaneous switching events between two discrete levels from single-particle movements. Reliable RTS analyses are crucial prerequisite to identify underlying mechanisms related to performance sensitivity. When numerous levels partake, complex patterns of multilevel RTSs occur, making their quantitative analysis exponentially difficult, hereby systematic approaches are found elusive. Here, we present a three-step analysis protocol via progressive knowledge-transfer, where the outputs of early step are passed onto a subsequent step. Especially, to quantify complex RTSs, we build three deep neural network architectures that can process temporal data well and demonstrate the model accuracy extensively with a large dataset of different RTS types affected by controlling background noise size. Our protocol offers structured schemes to quantify complex RTSs from which meaningful interpretation and inference can ensue.

physics.app-ph↗

arXiv2022

A Flow-based Distributed Trading Mechanism in Regional Electricity Market with Energy Hub

The concept of Energy Hub (EH) has been emerged to accommodate renewable energy sources in a multi-energy system to deploy the synergies between electricity and other energy sources. However, the market mechanisms for the integration of the EHs into the energy markets are not sufficiently elaborated. This paper proposes a flow-based two-level distributed trading mechanism in the regional electricity market with EH. At the lower level, the regional system operator coordinates the regional grids transactions in two markets, the local energy market with EH and the wholesale market of the upstream grid. Every nodal agent as an independent stakeholder leverages price discrepancy to cross arbitrage from different markets. At the upper level, the EH is a third player intending to maximize profit from trading in the regional electricity market and gas market. The regional electricity market clearing problem is formulated as a mathematical program with equilibrium constraints, for which we develop an ADMM-based distributed algorithm to obtain the equilibrium solution. The DC power flow is decomposed into optimization problems for the regional system operator and agents at different nodes, which can be solved in a distributed manner to achieve global optimality without violating the privacy of players. Case studies based on a realistic regional grid verify the effectiveness of the proposed algorithm and show that the mechanism is effective in decomposing power flow and increasing energy efficiency.

math.OC↗

arXiv2022

Discrimination-Based Double Auction for Maximizing Social Welfare in the Electricity and Heating Market Considering Privacy Preservation

This paper proposes a doubled-sided auction mechanism with price discrimination for social welfare (SW) maximization in the electricity and heating market. In this mechanism, energy service providers (ESPs) submit offers and load aggregators (LAs) submit bids to an energy trading center (ETC) to maximize their utility; in turn, the selfless ETC as an auctioneer leverages dis-criminatory price weights to regulate the behaviors of ESPs and LAs, which combines the individual benefits of each stakeholder with the overall social welfare to achieve the global optimum. Nash games are employed to describe the interactions between players with the same market role. Theoretically, we first prove the existence and uniqueness of the Nash equilibrium; then, considering the requirement of game players to preserve privacy, a distributed algorithm based on the alternating direction method of multipliers is developed to implement distributed bidding and analytical target cascading algorithm is applied to reach the balance of demand and supply. We validated the proposed mechanism using case studies on a city-level distribution system. The results indicated that the achieved SW improved by 4%-15% compared with other mechanisms, and also verified the effectiveness of the distributed algorithm.

cs.GT↗

arXiv2022

POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection

Ideology is at the core of political science research. Yet, there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. To this end, we study Pretrained Language Models using novel ideology-driven pretraining objectives that rely on the comparison of articles on the same story written by media of different ideologies. We further collect a large-scale dataset, consisting of more than 3.6M political news articles, for pretraining. Our model POLITICS outperforms strong baselines and the previous state-of-the-art models on ideology prediction and stance detection tasks. Further analyses show that POLITICS is especially good at understanding long or formally written texts, and is also robust in few-shot learning scenarios.

cs.CL↗