BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Ning An

Ning An contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine Learningphysics.opticsCryptography and Securitycs.CYDigital LibrariesQuantitative MethodsRobotics

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
6works
0followers
7topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

6 published item(s)

preprint2026arXiv

Let Robots Feel Your Touch: Visuo-Tactile Cortical Alignment for Embodied Mirror Resonance

Observing touch on another's body can elicit corresponding tactile sensations in the observer, a phenomenon termed mirror touch that supports empathy and social perception. This visuo-tactile resonance is thought to rely on structural correspondence between visual and somatosensory cortices, yet robotic systems lack computational frameworks that instantiate this principle. Here we demonstrate that cortical correspondence can be operationalized to endow robots with mirror touch. We introduce Mirror Touch Net, which imposes semantic, distributional and geometric alignment between visual and tactile representations through multi-level constraints, enabling prediction of millimetre-scale tactile signals across 1,140 taxels on a robotic hand from RGB images. Manifold analysis reveals that these constraints reshape visual representations into geometry consistent with the tactile manifold, reducing the complexity of cross-modal mapping. Extending this alignment framework to cross-domain observations of human hands enables tactile prediction and reflexive responses to observed human touch. Our results link a neural principle of visuo-tactile resonance to robotic perception, providing an explainable route towards anticipatory touch and empathic human-robot interaction. Code is available at https://github.com/fun0515/Mirror-Touch-Net.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Sentient: Detecting APTs Via Capturing Indirect Dependencies and Behavioral Logic

Advanced Persistent Threats (APTs) are difficult to detect due to their complexity and stealthiness. To mitigate such attacks, many approaches model entities and their relationship using provenance graphs to detect the stealthy and persistent characteristics of APTs. However, existing detection methods suffer from the flaws of missing indirect dependencies, noisy complex scenarios, and missing behavioral logical associations, which make it difficult to detect complex scenarios and effectively identify stealthy threats. In this paper, we propose Sentient, an APT detection method that combines pre-training and intent analysis. It employs a graph transformer to learn structural and semantic information from provenance graphs to avoid missing indirect dependencies. We mitigate scenario noise by combining global and local information. Additionally, we design an Intent Analysis Module (IAM) to associate logical relationships between behaviors. Sentient is trained solely on easily obtainable benign data to detect malicious behaviors that deviate from benign behavioral patterns. We evaluated Sentient on three widely-used datasets covering real-world attacks and simulated attacks. Notably, compared to six state-of-the-art methods, Sentient achieved an average reduction of 44% in false positive rate(FPR) for detection.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

A Monolithic Graphene-Functionalized Microlaser for Multispecies Gas Detection

Optical microcavity enhanced light-matter interaction offers a powerful tool to develop fast and precise sensing techniques, spurring applications in the detection of biochemical targets ranging from cells, nanoparticles, and large molecules. However, the intrinsic inertness of such pristine microresonators limits their spread in new fields such as gas detection. Here, a functionalized microlaser sensor is realized by depositing graphene in an erbium-doped over-modal microsphere. By using a 980 nm pump, multiple laser lines excited in different mode families of the microresonator are co-generated in a single device. The interference between these splitting mode lasers produce beat notes in the electrical domain (0.2-1.1 MHz) with sub-kHz accuracy, thanks to the graphene-induced intracavity backward scattering. This allows for multispecies gas identification from a mixture, and ultrasensitive gas detection down to individual molecule.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A deep belief network-based method to identify proteomic risk markers for Alzheimer disease

While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Digital personal health libraries: a systematic literature review

Objective: This paper gives context on recent literature regarding the development of digital personal health libraries (PHL) and provides insights into the potential application of consumer health informatics in diverse clinical specialties. Materials and Methods: A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Here, 2,850 records were retrieved from PubMed and EMBASE in March 2020 using search terms: personal, health, and library. Information related to the health topic, target population, study purpose, library function, data source, data science method, evaluation measure, and status were extracted from each eligible study. In addition, knowledge discovery methods, including co-occurrence analysis and multiple correspondence analysis, were used to explore research trends of PHL. Results: After screening, this systematic review focused on a dozen articles related to PHL. These encompassed health topics such as infectious diseases, congestive heart failure, electronic prescribing. Data science methods included relational database, information retrieval technology, ontology construction technology. Evaluation measures were heterogeneous regarding PHL functions and settings. At the time of writing, only one of the PHLs described in these articles is available for the public while the others are either prototypes or in the pilot stage. Discussion: Although PHL researches have used different methods to address problems in diverse health domains, there is a lack of an effective PHL to meet the needs of older adults. Conclusion: The development of PHLs may create an unprecedented opportunity for promoting the health of older consumers by providing diverse health information.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Gain-assisted chiral soliton microcombs

The emerging microresonator-based frequency combs revolutionize a broad range of applications from optical communications to astronomical calibration. Despite of their significant merits, low energy efficiency and the lack of all-optical dynamical control severely hinder the transfer of microcomb system to real-world applications. Here, by introducing active lasing medium into the soliton microcomb, for the first time, we experimentally achieve the chiral soliton with agile on-off switch and tunable dual-comb generation in a packaged microresonator. It is found that such a microresonator enables a soliton slingshot effect, the rapid soliton formation arising from the extra energy accumulation induced by inter-modal couplings. Moreover, tuning the erbium gain can generate versatile multi-soliton states, and extend the soliton operation window to a remarkable range over 18 GHz detuning. Finally, the gain-assisted chirality of counterpropagating soliton is demonstrated, which enables an unprecedented fast on-off switching of soliton microcombs. The non-trivial chiral soliton formation with active controllability inspires new paradigms of miniature optical frequency combs and brings the fast tunable soliton tools within reach.

0 citations0 reviewsNo code linkedOpen access