arXiv subjects

Ke Zhou

Publications and source records attributed to Ke Zhou.

At least 73 records · Page 4Linked to original sources

arXiv2023

Mean-variance hybrid portfolio optimization with quantile-based risk measure

This paper addresses the importance of incorporating various risk measures in portfolio management and proposes a dynamic hybrid portfolio optimization model that combines the spectral risk measure and the Value-at-Risk in the mean-variance formulation. By utilizing the quantile optimization technique and martingale representation, we offer a solution framework for these issues and also develop a closed-form portfolio policy when all market parameters are deterministic. Our hybrid model outperforms the classical continuous-time mean-variance portfolio policy by allocating a higher position of the risky asset in favorable market states and a less risky asset in unfavorable market states. This desirable property leads to promising numerical experiment results, including improved Sortino ratio and reduced downside risk compared to the benchmark models.

q-fin.PM↗

arXiv2022

Hyperbolic Hierarchical Contrastive Hashing

Hierarchical semantic structures, naturally existing in real-world datasets, can assist in capturing the latent distribution of data to learn robust hash codes for retrieval systems. Although hierarchical semantic structures can be simply expressed by integrating semantically relevant data into a high-level taxon with coarser-grained semantics, the construction, embedding, and exploitation of the structures remain tricky for unsupervised hash learning. To tackle these problems, we propose a novel unsupervised hashing method named Hyperbolic Hierarchical Contrastive Hashing (HHCH). We propose to embed continuous hash codes into hyperbolic space for accurate semantic expression since embedding hierarchies in hyperbolic space generates less distortion than in hyper-sphere space and Euclidean space. In addition, we extend the K-Means algorithm to hyperbolic space and perform the proposed hierarchical hyperbolic K-Means algorithm to construct hierarchical semantic structures adaptively. To exploit the hierarchical semantic structures in hyperbolic space, we designed the hierarchical contrastive learning algorithm, including hierarchical instance-wise and hierarchical prototype-wise contrastive learning. Extensive experiments on four benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art unsupervised hashing methods. Codes will be released.

cs.CV↗

arXiv2022

Foreground Object Structure Transfer for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to train a classification model from the labeled source domain for the unlabeled target domain. Since the data distributions of the two domains are different, the model often performs poorly on the target domain. Existing methods align the feature distributions of the source and target domains and learn domain-invariant features to improve the performance of the model. However, the features are usually aligned as a whole, and the domain adaptation task fails to serve the classification, which will ignore the class information and lead to misalignment.In this paper, we investigate those features that should be used for domain alignment, introduce prior knowledge to extract foreground features to guide the domain adaptation task for classification tasks, and perform alignment in the local structure of objects. We propose a method called Foreground Object Structure Transfer(FOST). The key to FOST is the new clustering based condition, which combines the relative position relationship of foreground objects. Based on this conditions, FOST makes the data distribution of the same class more compact in geometry. In practice, since the label of the target domain is not available, we use the clustering information of the source domain to assign pseudo labels to the target domain samples, and then according to the source domain data prior knowledge guides those positive features to maximum the inter-class distance between different classes and mimimum the intra-class distance. Extensive experimental results on various benchmarks ($i.e.$ ImageCLEF-DA, Office-31, Office-Home, Visda-2017) under different domain adaptation settings prove that our FOST compares favorably against the existing state-of-the-art domain adaptation methods.

cs.CV↗

arXiv2021

POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling

Conversational search systems, such as Google Assistant and Microsoft Cortana, provide a new search paradigm where users are allowed, via natural language dialogues, to communicate with search systems. Evaluating such systems is very challenging since search results are presented in the format of natural language sentences. Given the unlimited number of possible responses, collecting relevance assessments for all the possible responses is infeasible. In this paper, we propose POSSCORE, a simple yet effective automatic evaluation method for conversational search. The proposed embedding-based metric takes the influence of part of speech (POS) of the terms in the response into account. To the best knowledge, our work is the first to systematically demonstrate the importance of incorporating syntactic information, such as POS labels, for conversational search evaluation. Experimental results demonstrate that our metrics can correlate with human preference, achieving significant improvements over state-of-the-art baseline metrics.

cs.IR↗

arXiv2021

How Epidemic Psychology Works on Twitter: Evolution of responses to the COVID-19 pandemic in the U.S

Disruptions resulting from an epidemic might often appear to amount to chaos but, in reality, can be understood in a systematic way through the lens of "epidemic psychology". According to Philip Strong, the founder of the sociological study of epidemic infectious diseases, not only is an epidemic biological; there is also the potential for three psycho-social epidemics: of fear, moralization, and action. This work empirically tests Strong's model at scale by studying the use of language of 122M tweets related to the COVID-19 pandemic posted in the U.S. during the whole year of 2020. On Twitter, we identified three distinct phases. Each of them is characterized by different regimes of the three psycho-social epidemics. In the refusal phase, users refused to accept reality despite the increasing number of deaths in other countries. In the anger phase (started after the announcement of the first death in the country), users' fear translated into anger about the looming feeling that things were about to change. Finally, in the acceptance phase, which began after the authorities imposed physical-distancing measures, users settled into a "new normal" for their daily activities. Overall, refusal of accepting reality gradually died off as the year went on, while acceptance increasingly took hold. During 2020, as cases surged in waves, so did anger, re-emerging cyclically at each wave. Our real-time operationalization of Strong's model is designed in a way that makes it possible to embed epidemic psychology into real-time models (e.g., epidemiological and mobility models).

cs.CY↗

arXiv2021

What and How long: Prediction of Mobile App Engagement

User engagement is crucial to the long-term success of a mobile app. Several metrics, such as dwell time, have been used for measuring user engagement. However, how to effectively predict user engagement in the context of mobile apps is still an open research question. For example, do the mobile usage contexts (e.g.,~time of day) in which users access mobile apps impact their dwell time? Answers to such questions could help mobile operating system and publishers to optimize advertising and service placement. In this paper, we first conduct an empirical study for assessing how user characteristics, temporal features, and the short/long-term contexts contribute to gains in predicting users' app dwell time on the population level. The comprehensive analysis is conducted on large app usage logs collected through a mobile advertising company. The dataset covers more than 12K anonymous users and 1.3 million log events. Based on the analysis, we further investigate a novel mobile app engagement prediction problem -- can we predict simultaneously what app the user will use next and how long he/she will stay on that app? We propose several strategies for this joint prediction problem and demonstrate that our model can improve the performance significantly when compared with the state-of-the-art baselines. Our work can help mobile system developers in designing a better and more engagement-aware mobile app user experience.

cs.IR↗

arXiv2021

Meta-evaluation of Conversational Search Evaluation Metrics

Conversational search systems, such as Google Assistant and Microsoft Cortana, enable users to interact with search systems in multiple rounds through natural language dialogues. Evaluating such systems is very challenging given that any natural language responses could be generated, and users commonly interact for multiple semantically coherent rounds to accomplish a search task. Although prior studies proposed many evaluation metrics, the extent of how those measures effectively capture user preference remains to be investigated. In this paper, we systematically meta-evaluate a variety of conversational search metrics. We specifically study three perspectives on those metrics: (1) reliability: the ability to detect "actual" performance differences as opposed to those observed by chance; (2) fidelity: the ability to agree with ultimate user preference; and (3) intuitiveness: the ability to capture any property deemed important: adequacy, informativeness, and fluency in the context of conversational search. By conducting experiments on two test collections, we find that the performance of different metrics varies significantly across different scenarios whereas consistent with prior studies, existing metrics only achieve a weak correlation with ultimate user preference and satisfaction. METEOR is, comparatively speaking, the best existing single-turn metric considering all three perspectives. We also demonstrate that adapted session-based evaluation metrics can be used to measure multi-turn conversational search, achieving moderate concordance with user satisfaction. To our knowledge, our work establishes the most comprehensive meta-evaluation for conversational search to date.

cs.IR↗

arXiv2021

The Healthy States of America: Creating a Health Taxonomy with Social Media

Since the uptake of social media, researchers have mined online discussions to track the outbreak and evolution of specific diseases or chronic conditions such as influenza or depression. To broaden the set of diseases under study, we developed a Deep Learning tool for Natural Language Processing that extracts mentions of virtually any medical condition or disease from unstructured social media text. With that tool at hand, we processed Reddit and Twitter posts, analyzed the clusters of the two resulting co-occurrence networks of conditions, and discovered that they correspond to well-defined categories of medical conditions. This resulted in the creation of the first comprehensive taxonomy of medical conditions automatically derived from online discussions. We validated the structure of our taxonomy against the official International Statistical Classification of Diseases and Related Health Problems (ICD-11), finding matches of our clusters with 20 official categories, out of 22. Based on the mentions of our taxonomy's sub-categories on Reddit posts geo-referenced in the U.S., we were then able to compute disease-specific health scores. As opposed to counts of disease mentions or counts with no knowledge of our taxonomy's structure, we found that our disease-specific health scores are causally linked with the officially reported prevalence of 18 conditions.

cs.CY↗

arXiv2020

Humane Visual AI: Telling the Stories Behind a Medical Condition

A biological understanding is key for managing medical conditions, yet psychological and social aspects matter too. The main problem is that these two aspects are hard to quantify and inherently difficult to communicate. To quantify psychological aspects, this work mined around half a million Reddit posts in the sub-communities specialised in 14 medical conditions, and it did so with a new deep-learning framework. In so doing, it was able to associate mentions of medical conditions with those of emotions. To then quantify social aspects, this work designed a probabilistic approach that mines open prescription data from the National Health Service in England to compute the prevalence of drug prescriptions, and to relate such a prevalence to census data. To finally visually communicate each medical condition's biological, psychological, and social aspects through storytelling, we designed a narrative-style layered Martini Glass visualization. In a user study involving 52 participants, after interacting with our visualization, a considerable number of them changed their mind on previously held opinions: 10% gave more importance to the psychological aspects of medical conditions, and 27% were more favourable to the use of social media data in healthcare, suggesting the importance of persuasive elements in interactive visualizations.

cs.HC↗

arXiv2020

MeetCues: Supporting Online Meetings Experience

The remote work ecosystem is transforming patterns of communication between teams and individuals located at distance. Particularly, the absence of certain subtle cues in current communication tools may hinder an online's meeting outcome by negatively impacting attendees' overall experience and, often, make them feeling disconnected. The problem here might be due to the fact that current tools fall short in capturing it. To partly address this, we developed an online platform-MeetCues-with the aim of supporting online communication during meetings. MeetCues is a companion platform for a commercial communication tool with interactive and visual UI features that support back-channels of communications. It allows attendees to be more engaged during a meeting, and reflect in real-time or post-meeting. We evaluated our platform in a diverse set of five, real-world corporate meetings, and we found that, not only people were more engaged and aware during their meetings, but they also felt more connected. These findings suggest promise in the design of new communications tools, and reinforce the role of InfoVis in augmenting and enriching online meetings.

cs.HC↗

arXiv2019

Voltage gated inter-cation selective ion channels from graphene nanopores

With the ability to selectively control ionic flux, biological protein ion channels perform a fundamental role in many physiological processes. For practical applications that require the functionality of a biological ion channel, graphene provides a promising solid-state alternative, due to its atomic thinness and mechanical strength. Here, we demonstrate that nanopores introduced into graphene membranes, as large as 50 nm in diameter, exhibit inter-cation selectivity with a ~20x preference for K+ over divalent cations and can be modulated by an applied gate voltage. Liquid atomic force microscopy of the graphene devices reveals surface nanobubbles near the pore to be responsible for the observed selective behavior. Molecular dynamics simulations indicate that translocation of ions across the pore likely occurs via a thin water layer at the edge of the pore and the nanobubble. Our results demonstrate a significant improvement in the inter-cation selectivity displayed by a solid-state nanopore device and by utilizing the pores in a de-wetted state, offers an approach to fabricating selective graphene membranes that does not rely on the fabrication of sub-nm pores.

cond-mat.mes-hall↗

arXiv2018

Data Acquisition System for CSNS Neutron Beam Monitor

In Chinese Spallation Neutron Source (CSNS), proton beam is used to hit metal tungsten target, and then high flux neutrons are generated for experiments on instruments. For neutron flux spectrum correction, boron-coated GEM, lithium glass and 3He are used as neutron beam monitor in instruments. To be integrated into neutron instrument control, a new DAQ software for neutron beam monitor is developed, called NEROS (Neutron Event Readout System). NEROS is based on EPICS V4 and a unified data format for CSNS neutron beam monitor is defined. The framework and software design of NEROS is introduced in this paper, including real-time data readout, data processing, data visualization and data storage in Nexus format. The performance is evaluated through offline test, X-ray test and 252Cf test. The deployment of NEROS and its running result in CSNS instrument commissioning are also introduced in this paper.

physics.ins-det↗

arXiv2018

The Proton Beam Real-time Monitor System in CSNS

In Chinese Spallation Neutron Source (CSNS), proton beam is used to hit metal tungsten target, and then high flux neutron are generated for experiments on instruments. For neutron flux spectrum correction, the current of proton beam is for each instrument. A real time monitor system is developed in CSNS, to monitor, broadcast and record each pulse of proton. Each proton pulse charge is measured and marked with high-precision timestamp. Then, the result of measurement will be broadcasted to control room and each neutron instrument. In control room, the proton charge of each pulse is listened and stored in database by agent program for offline use. The high-precision timestamp can be used to proton charge and neutron data alignment in time scale. The architecture of proton beam monitor system is introduced in this paper. And the performance of this system is evaluated in this paper.

physics.ins-det↗

arXiv2014

A note on the passage time of finite state Markov chains

Consider a Markov chain with finite state $\{0, 1, ..., d\}$. We give the generation functions (or Laplace transforms) of absorbing (passage) time in the following two situations : (1) the absorbing time of state $d$ when the chain starts from any state $i$ and absorbing at state $d$; (2) the passage time of any state $i$ when the chain starts from the stationary distribution supposed the chain is time reversible and ergodic. Example shows that it is more convenient compared with the existing methods, especially we can calculate the expectation of the absorbing time directly.

math.PR↗

arXiv2014

Scaling limit of the local time of the Sinai's random walk

We prove that the local times of a sequence of Sinai's random walks convergence to those of Brox's diffusion by proper scaling, which is accord with the result of Seignourel (2000). Our proof is based on the convergence of the branching processes in random environment by Kurtz (1979).

math.PR↗

arXiv2014

Dynamic Mean-LPM and Mean-CVaR Portfolio Optimization in Continuous-time

Instead of controlling "symmetric" risks measured by central moments of investment return or terminal wealth, more and more portfolio models have shifted their focus to manage "asymmetric" downside risks that the investment return is below certain threshold. Among the existing downside risk measures, the lower-partial moments (LPM) and conditional value-at-risk (CVaR) are probably most promising. In this paper we investigate the dynamic mean-LPM and mean-CVaR portfolio optimization problems in continuous-time, while the current literature has only witnessed their static versions. Our contributions are two-fold, in both building up tractable formulations and deriving corresponding analytical solutions. By imposing a limit funding level on the terminal wealth, we conquer the ill-posedness exhibited in the class of mean-downside risk portfolio models. The limit funding level not only enables us to solve both dynamic mean-LPM and mean-CVaR portfolio optimization problems, but also offers a flexibility to tame the aggressiveness of the portfolio policies generated from such mean - downside risk models. More specifically, for a general market setting, we prove the existence and uniqueness of the Lagrangian multiplies, which is a key step in applying the martingale approach, and establish a theoretical foundation for developing efficient numerical solution approaches. Moreover, for situations where the opportunity set of the market setting is deterministic, we derive analytical portfolio policies for both dynamic mean-LPM and mean-CVaR formulations.

q-fin.PM↗

arXiv2013

Tail asymptotic of the stationary distribution for the state dependent (1,R)-reflecting random walk: near critical

In this paper, we consider the $(1,R)$ state-dependent reflecting random walk (RW) on the half line, allowing the size of jumps to the right at maximal $R$ and to the left only 1. We provide an explicit criterion for positive recurrence and the explicit expression of the stationary distribution based on the intrinsic branching structure within the walk. As an application, we obtain the tail asymptotic of the stationary distribution in the "near critical" situation.

math.PR↗

arXiv2013

Hitting Time Distribution for Skip-Free Markov Chains: A Simple Proof

A well-known theorem for an irreducible skip-free chain with absorbing state $d$, under some conditions, is that the hitting (absorbing) time of state $d$ starting from state 0 is distributed as the sum of $d$ independent geometric (or exponential) random variables. The purpose of this paper is to present a direct and simple proof of the theorem in the cases of both discrete and continuous time skip-free Markov chains. Our proof is to calculate directly the generation functions (or Laplace transforms) of hitting times in terms of the iteration method.

math.PR↗