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Researcher profile

Jialin Li

Jialin Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial Intelligenceastro-ph.IMMachine LearningComputer VisionDistributed, Parallel, and Cluster ComputingSoftware Engineeringcond-mat.mtrl-sciCryptography and SecurityHuman-Computer InteractionNetworking and Internet ArchitectureOperating Systemsphysics.chem-phphysics.comp-ph

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Trust 21 - EmergingVerification L1Unclaimed author
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Published work

11 published item(s)

preprint2026arXiv

Beyond One-Size-Fits-All: A Survey of Personalized Affective Computing in Human-Agent Interaction

In personalized machine learning, the aim of personalization is to train a model that caters to a specific individual or group of individuals by optimizing one or more performance metrics and adhering to specific constraints. In this paper, we discuss the need for personalization in affective computing and present the first survey of existing approaches for personalization in affective computing. Our review spans training techniques and objectives towards the personalization of affective computing models across various interaction modes and contexts. We develop a taxonomy that clusters existing approaches into Data-level and Model-level approaches. Across the Data-Level and Model-Level broad categories, we group existing approaches into seven sub-categories: (1) User-Specific Models, (2) Group-Specific Models, (3) Weighting-Based Approaches, (4) Feature Augmentation, (5) Generative-Based Models which fall into the Data-Level approaches, (6) Fine-Tuning Approaches, and (7) Multitask Learning Approaches falling under the model-level approaches. We provide a problem formulation for personalized affective computing, and to each of the identified sub-categories. Additionally, we provide a statistical analysis of the surveyed literature, analyzing the prevalence of different affective computing tasks, interaction modes (i.e. Human-Computer Interaction (HCI), Human-Human interaction (HHI), Human-Robot Interaction (HRI)), interaction contexts (e.g. educative, social, gaming, etc.), and the level of personalization among the surveyed works. Based on our analysis, we provide a road-map for researchers interested in exploring this direction.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations

In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations. Despite numerous pioneering studies, the field remains in its early stages; existing software implementations are often fragmented, lacking unified frameworks and standardized interfaces required for broad community adoption. Here we present DeepH-pack, a comprehensive and unified software package that integrates first-principles calculations with deep learning. By incorporating fundamental physical principles into neural-network design, such as the nearsightedness principle and the equivariance principle, DeepH-pack achieves robust cross-scale and cross-material generalizability. This allows models trained on small-scale structures to generalize to large-scale and previously unseen materials. The toolkit preserves first-principles accuracy while accelerating electronic structure calculations by several orders of magnitude, establishing an efficient and intelligent computational paradigm for large-scale materials simulation, high-throughput materials database construction, and AI-driven materials discovery.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Geometrically Constrained Stenosis Editing in Coronary Angiography via Entropic Optimal Transport

The scarcity of high-quality imaging data for coronary angiography (CAG) stenosis limits the clinical translation of automated stenosis detection. Synthetic stenosis data provides a practical avenue to augment training sets, improving data quality, diversity, and distributional coverage, and enhancing detection precision and generalization. However, diffusion-based editing commonly relies on soft guidance in a noise-initialized reverse process, offering limited pixel-level precision and structure preservation. We propose the OT-Bridge Editor, which reframes localized editing as a constrained entropic optimal transport (OT) problem and leverages geometric information to steer the generation path, enabling stronger geometric control. Extensive experiments show that our synthesized angiograms consistently improve downstream stenosis detection, yielding substantial relative gains of 27.8% on the public ARCADE benchmark and 23.0% on our multi-center dataset, supported by consistent qualitative results.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces

Recently, skills have been widely adopted in large language model (LLM)-based agent systems across various domains. In existing frameworks, skills are typically injected into the agent reasoning loop as contextual guidance once matched to a runtime task, enabling specialized task-solving capabilities. We find that this execution paradigm introduces two major sources of redundancy: irrelevant context injection and repeated skill-specific reasoning and planning. To this end, we propose SkillSmith, a boundary-first compiler-runtime framework that compiles skill packages offline into minimal executable interfaces. By extracting fine-grained operational boundaries from skills, SkillSmith enables agents to dynamically access and execute only the relevant components at runtime, thereby minimizing unnecessary context injection and redundant reasoning overhead. In the evaluation on SkillsBench benchmark, SkillSmith reduces solve-stage token usage by 57.44%, thinking iterations by 42.99%, solve time by 50.57% (2.02x faster), and token-proportional monetary cost by 57.44% compared with using raw-skills. Moreover, compiled artifacts produced by a stronger model can be reused by a smaller or more efficient runtime model, improving task accuracy in cases where raw skill interpretation fails. The source code and data are available at https://github.com/AetherHeart-AI/Aeloon.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Design of SCALES: A 2-5 Micron Coronagraphic Integral Field Spectrograph for Keck Observatory

We present the design of SCALES (Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy) a new 2-5 micron coronagraphic integral field spectrograph under construction for Keck Observatory. SCALES enables low-resolution (R~50) spectroscopy, as well as medium-resolution (R~4,000) spectroscopy with the goal of discovering and characterizing cold exoplanets that are brightest in the thermal infrared. Additionally, SCALES has a 12x12" field-of-view imager that will be used for general adaptive optics science at Keck. We present SCALES's specifications, its science case, its overall design, and simulations of its expected performance. Additionally, we present progress on procuring, fabricating and testing long lead-time components.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Fabrication of Pupil Masks for a New Infrared Exoplanet Imager at Keck Observatory

The Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy (SCALES) is an instrument being designed to perform direct imaging of exoplanets in the mid-infrared (2-5 μm) with the Adaptive Optics System of W.M. Keck Observatory. To eliminate unwanted thermal infrared radiation, SCALES utilizes both a cold stop for excluding background radiation and a vector vortex coronagraph with Lyot stops for starlight suppression. Optimal geometric masks have been designed. We simulate the propagation of light through the Lyot plane and analyze the on-axis images of stars in the K, L, and M band for the performance of the Lyot stops. Additionally, finalized cold stop and Lyot stop designs are presented along with evaluations on the effects of manufacturing tolerances and tilt in pupil planes caused by off-axis parabolic mirror relays.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Linear-time Temporal Logic guided Greybox Fuzzing

Software model checking is a verification technique which is widely used for checking temporal properties of software systems. Even though it is a property verification technique, its common usage in practice is in "bug finding", that is, finding violations of temporal properties. Motivated by this observation and leveraging the recent progress in fuzzing, we build a greybox fuzzing framework to find violations of Linear-time Temporal Logic (LTL) properties. Our framework takes as input a sequential program written in C/C++, and an LTL property. It finds violations, or counterexample traces, of the LTL property in stateful software systems; however, it does not achieve verification. Our work substantially extends directed greybox fuzzing to witness arbitrarily complex event orderings. We note that existing directed greybox fuzzing approaches are limited to witnessing reaching a location or witnessing simple event orderings like use-after-free. At the same time, compared to model checkers, our approach finds the counterexamples faster, thereby finding more counterexamples within a given time budget. Our LTL-Fuzzer tool, built on top of the AFL fuzzer, is shown to be effective in detecting bugs in well-known protocol implementations, such as OpenSSL and Telnet. We use LTL-Fuzzer to reproduce known vulnerabilities (CVEs), to find 15 zero-day bugs by checking properties extracted from RFCs (for which 12 CVEs have been assigned), and to find violations of both safety as well as liveness properties in real-world protocol implementations. Our work represents a practical advance over software model checkers -- while simultaneously representing a conceptual advance over existing greybox fuzzers. Our work thus provides a starting point for understanding the unexplored synergies between software model checking and greybox fuzzing.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Mixed Fault Tolerance Protocols with Trusted Execution Environment

Blockchain systems are designed, built and operated in the presence of failures. There are two dominant failure models, namely crash fault and Byzantine fault. Byzantine fault tolerance (BFT) protocols offer stronger security guarantees, and thus are widely used in blockchain systems. However, their security guarantees come at a dear cost to their performance and scalability. Several works have improved BFT protocols, and Trusted Execution Environment (TEE) has been shown to be an effective solution. However, existing such works typically assume that each participating node is equipped with TEE. For blockchain systems wherein participants typically have different hardware configurations, i.e., some nodes feature TEE while others do not, existing TEE-based BFT protocols are not applicable. This work studies the setting wherein not all participating nodes feature TEE, under which we propose a new fault model called mixed fault. We explore a new approach to designing efficient distributed fault-tolerant protocols under the mixed fault model. In general, mixed fault tolerance (MFT) protocols assume a network of $n$ nodes, among which up to $f = \frac{n-2}{3}$ can be subject to mixed faults. We identify two key principles for designing efficient MFT protocols, namely, (i) prioritizing non-equivocating nodes in leading the protocol, and (ii) advocating the use of public-key cryptographic primitives that allow authenticated messages to be aggregated. We showcase these design principles by prescribing an MFT protocol, namely MRaft. We implemented a prototype of MRaft using Intel SGX, integrated it into the CCF blockchain framework, conducted experiments, and showed that MFT protocols can obtain the same security guarantees as their BFT counterparts while still providing better performance (both transaction throughput and latency) and scalability.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SCALES for Keck: Optical Design

SCALES is a high-contrast, infrared coronagraphic imager and integral field spectrograph (IFS) to be deployed behind the W.M. Keck Observatory adaptive optics system. A reflective optical design allows diffraction-limited imaging over a large wavelength range (1.0 - 5.0 microns). A microlens array-based IFS coupled with a lenslet reformatter ("slenslit") allow spectroscopy at both low (R = 35 - 250) and moderate (R = 2000 - 6500) spectral resolutions. The large wavelength range, diffraction-limited performance, high contrast coronagraphy and cryogenic operation present a unique optical design challenge. We present the full SCALES optical design, including performance modeling and analysis and manufacturing.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SimBricks: End-to-End Network System Evaluation with Modular Simulation

Full system "end-to-end" measurements in physical testbeds are the gold standard for network systems evaluation but are often not feasible. When physical testbeds are not available we frequently turn to simulation for evaluation. Unfortunately, existing simulators are insufficient for end-to-end evaluation, as they either cannot simulate all components, or simulate them with inadequate detail. We address this through modular simulation, flexibly combining and connecting multiple existing simulators for different components, including processor and memory, devices, and network, into virtual end-to-end testbeds tuned for each use-case. Our architecture, SimBricks, combines well-defined component interfaces for extensibility and modularity, efficient communication channels for local and distributed simulation, and a co-designed efficient synchronization mechanism for accurate timing across simulators. We demonstrate SimBricks scales to 1000 simulated hosts, each running a full software stack including Linux, and that it can simulate testbeds with existing NIC and switch RTL implementations. We also reproduce key findings from prior work in congestion control, NIC architecture, and in-network computing in SimBricks.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Guaranteed Simultaneous Asymmetric Tensor Decomposition via Orthogonalized Alternating Least Squares

Tensor CANDECOMP/PARAFAC (CP) decomposition is an important tool that solves a wide class of machine learning problems. Existing popular approaches recover components one by one, not necessarily in the order of larger components first. Recently developed simultaneous power method obtains only a high probability recovery of top $r$ components even when the observed tensor is noiseless. We propose a Slicing Initialized Alternating Subspace Iteration (s-ASI) method that is guaranteed to recover top $r$ components ($ε$-close) simultaneously for (a)symmetric tensors almost surely under the noiseless case (with high probability for a bounded noise) using $O(\log(\log \frac{1}ε))$ steps of tensor subspace iterations. Our s-ASI method introduces a Slice-Based Initialization that runs $O(1/\log(\frac{λ_r}{λ_{r+1}}))$ steps of matrix subspace iterations, where $λ_r$ denotes the r-th top singular value of the tensor. We are the first to provide a theoretical guarantee on simultaneous orthogonal asymmetric tensor decomposition. Under the noiseless case, we are the first to provide an \emph{almost sure} theoretical guarantee on simultaneous orthogonal tensor decomposition. When tensor is noisy, our algorithm for asymmetric tensor is robust to noise smaller than $\min\{O(\frac{(λ_r - λ_{r+1})ε}{\sqrt{r}}), O(δ_0\frac{λ_r -λ_{r+1}}{\sqrt{d}})\}$, where $δ_0$ is a small constant proportional to the probability of bad initializations in the noisy setting.

0 citations0 reviewsNo code linkedOpen access