arXiv subjects

Joon Lee

Publications and source records attributed to Joon Lee.

16 recordsLinked to original sources

arXiv2026

Optimal Fusion Strategies for Quantum Computation

Logical fusions are important for a number of tasks in quantum information, such as quantum error correction and quantum repeaters. In the photonic setting one must contend with the fact that physical fusions are probabilistic (i.e.~the associated qubits are measured in product bases), which---depending on the failures and the associated bases---can lead to a failure on the logical level. The choice of failure basis of each qubit is known as a fusion strategy, and finding good fusion strategies is important for optimizing performance of fusion-based quantum computation. Here we provide a complete characterization when $k=1$ qubits are encoded, and in particular characterize those codes and fusion strategies such that all but one physical fusion can fail, i.e.~\emph{perfect fusion strategies}. In doing so, we recover previously known perfect fusion strategies, and find perfect fusion strategies for quantum parity-check codes, answering an open question. We furthermore show that perfect fusion strategies are generic: random $[[n, 1, d]]$ graph codes admit a perfect fusion strategy with probability exponentially close to $1$. Additionally we motivate the study of a new graph parameter, namely the maximum degree of a graph at a given vertex taken over all LC-equivalent graphs, by giving a new operationally meaningful interpretation of it.

quant-ph↗

arXiv2026

Ultra-Low-Impedance Robotic Gripper for High-Bandwidth and Transparent Physical Interaction

Conventional robotic grippers often use high-ratio transmissions to generate grasping torque and external force sensors to measure physical interaction. High-ratio transmissions increase friction, reflected inertia, and mechanical impedance, while external sensors add hardware complexity. To address these trade-offs, this study proposes a novel 9-DOF, three-fingered Differential Direct-Drive (DDD) gripper that combines DD motors with a low-ratio (1:2) differential transmission. The mechanism centralizes actuator mass at the base to minimize moving-link inertia, while the differential architecture couples two motors in parallel to amplify torque during flexion. Experiments show that the prototype delivers a nominal grasping force of approximately 18 N and a fingertip force of 4.7 N, while maintaining a low motor contribution to system inertia (0.236%) and low passive mechanical impedance, with a maximum measured value of 50.1 N/m when the motors are unpowered. The proposed hardware addresses the trade-offs among torque, physical transparency, and kinematic dexterity, providing a foundation for high-bandwidth interaction and sensorless proprioceptive force estimation.

cs.RO↗

arXiv2026

RIM Hand : A Robotic Hand with an Accurate Carpometacarpal Joint and Nitinol-Supported Skeletal Structure

This paper presents the flexible RIM Hand, a biomimetic robotic hand that precisely replicates the carpometacarpal (CMC) joints and employs superelastic Nitinol wires throughout its skeletal framework. By modeling the full carpal-to-metacarpal anatomy, the design enables realistic palm deformation through tendon-driven fingers while enhancing joint restoration and supports skeletal structure with Nitinol-based dorsal extensors. A flexible silicone skin further increases contact friction and contact area, enabling stable grasps for diverse objects. Experiments show that the palm can deform up to 28%, matching human hand flexibility, while achieving more than twice the payload capacity and three times the contact area compared to a rigid palm design. The RIM Hand thus offers improved dexterity, compliance, and anthropomorphism, making it promising for prosthetic and service-robot applications.

cs.RO↗

arXiv2024

The PRODSAT phase of random quantum satisfiability

The $k$-QSAT problem is a quantum analog of the famous $k$-SAT constraint satisfaction problem. We must determine the zero energy ground states of a Hamiltonian of $N$ qubits consisting of a sum of $M$ random $k$-local rank-one projectors. It is known that product states of zero energy exist with high probability if and only if the underlying factor graph has a clause-covering dimer configuration. This means that the threshold of the PRODSAT phase is a purely geometric quantity equal to the dimer covering threshold. We revisit and fully prove this result through a combination of complex analysis and algebraic methods based on Buchberger's algorithm for complex polynomial equations with random coefficients. We also discuss numerical experiments investigating the presence of entanglement in the PRODSAT phase in the sense that product states do not span the whole zero energy ground state space.

cs.IT↗

arXiv2023

Unsupervised Feature Selection to Identify Important ICD-10 Codes for Machine Learning: A Case Study on a Coronary Artery Disease Patient Cohort

The use of International Classification of Diseases (ICD) codes in healthcare presents a challenge in selecting relevant codes as features for machine learning models due to this system's large number of codes. In this study, we compared several unsupervised feature selection methods for an ICD code database of 49,075 coronary artery disease patients in Alberta, Canada. Specifically, we employed Laplacian Score, Unsupervised Feature Selection for Multi-Cluster Data, Autoencoder Inspired Unsupervised Feature Selection, Principal Feature Analysis, and Concrete Autoencoders with and without ICD tree weight adjustment to select the 100 best features from over 9,000 codes. We assessed the selected features based on their ability to reconstruct the initial feature space and predict 90-day mortality following discharge. Our findings revealed that the Concrete Autoencoder methods outperformed all other methods in both tasks. Furthermore, the weight adjustment in the Concrete Autoencoder method decreased the complexity of features.

cs.LG↗

arXiv2023

Machine Learning Capability: A standardized metric using case difficulty with applications to individualized deployment of supervised machine learning

Model evaluation is a critical component in supervised machine learning classification analyses. Traditional metrics do not currently incorporate case difficulty. This renders the classification results unbenchmarked for generalization. Item Response Theory (IRT) and Computer Adaptive Testing (CAT) with machine learning can benchmark datasets independent of the end-classification results. This provides high levels of case-level information regarding evaluation utility. To showcase, two datasets were used: 1) health-related and 2) physical science. For the health dataset a two-parameter IRT model, and for the physical science dataset a polytonomous IRT model, was used to analyze predictive features and place each case on a difficulty continuum. A CAT approach was used to ascertain the algorithms' performance and applicability to new data. This method provides an efficient way to benchmark data, using only a fraction of the dataset (less than 1%) and 22-60x more computationally efficient than traditional metrics. This novel metric, termed Machine Learning Capability (MLC) has additional benefits as it is unbiased to outcome classification and a standardized way to make model comparisons within and across datasets. MLC provides a metric on the limitation of supervised machine learning algorithms. In situations where the algorithm falls short, other input(s) are required for decision-making.

cs.LG↗

arXiv2022

Prostate Cancer Malignancy Detection and localization from mpMRI using auto-Deep Learning: One Step Closer to Clinical Utilization

Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work where we trained a customized convolutional neural network on a public cohort with 201 patients and the cropped 2D patches around the region of interest were used as the input, the cropped 2.5D slices of the prostate glands were used as the input, and the optimal model were searched in the model space using autoKeras. Something different was peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effectively in highlighting the most suspicious slices out of a sequence, hopefully to greatly ease the workload for the physicians.

eess.IV↗

arXiv2021

The full rank condition for sparse random matrices

We derive a sufficient condition for a sparse random matrix with given numbers of non-zero entries in the rows and columns having full row rank. The result covers both matrices over finite fields with independent non-zero entries and $\{0,1\}$-matrices over the rationals. The sufficient condition is generally necessary as well.

math.CO↗

arXiv2021

Warning Propagation: stability and subcriticality

Warning Propagation is a combinatorial message passing algorithm that unifies and generalises a wide variety of recursive combinatorial procedures. Special cases include the Unit Clause Propagation and Pure Literal algorithms for satisfiability as well as the peeling process for identifying the $k$-core of a random graph. Here we analyse Warning Propagation in full generality on a very general class of multi-type random graphs. We prove that under mild assumptions on the random graph model and the stability of the the message limit, Warning Propagation converges rapidly. In effect, the analysis of the fixed point of the message passing process on a random graph reduces to analysing the process on a multi-type Galton-Watson tree. This result corroborates and generalises a heuristic first put forward by Pittel, Spencer and Wormald in their seminal $k$-core paper (JCTB 1996).

math.CO↗

arXiv2021

The sparse parity matrix

Let $\mathbf{A}$ be an $n\times n$-matrix over $\mathbb{F}_2$ whose every entry equals $1$ with probability $d/n$ independently for a fixed $d>0$. Draw a vector $\mathbf{y}$ randomly from the column space of $\mathbf{A}$. It is a simple observation that the entries of a random solution $\mathbf{x}$ to $\mathbf{A} x=\mathbf{y}$ are asymptotically pairwise independent, i.e., $\sum_{i \mathrm{e}$ the overlap concentrates on a single value once we condition on the matrix $\mathbf{A}$, while over the probability space of $\mathbf{A}$ its conditional expectation vacillates between two different values $\alpha_*(d)<\alpha^*(d)$, either of which occurs with probability $1/2+o(1)$. This bifurcated non-concentration result provides an instructive contribution to both the theory of random constraint satisfaction problems and of inference problems on random structures.

math.CO↗

arXiv2021

Warning Propagation on random graphs

Warning Propagation is a combinatorial message passing algorithm that unifies and generalises a wide variety of recursive combinatorial procedures. Special cases include the Unit Clause Propagation and Pure Literal algorithms for satisfiability as well as the peeling process for identifying the $k$-core of a random graph. Here we analyse Warning Propagation in full generality on the binomial random graph. We prove that under a mild stability assumption Warning Propagation converges rapidly. In effect, the analysis of the fixed point of the message passing process on a random graph reduces to analysing the process on a Galton-Watson tree. This result corroborates and generalises a heuristic first put forward by Pittel, Spencer and Wormald in their seminal $k$-core paper (JCTB 1996).

math.CO↗

arXiv2020

A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network

Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification.

cs.CV↗

arXiv2020

The random 2-SAT partition function

We show that throughout the satisfiable phase the normalised number of satisfying assignments of a random $2$-SAT formula converges in probability to an expression predicted by the cavity method from statistical physics. The proof is based on showing that the Belief Propagation algorithm renders the correct marginal probability that a variable is set to `true' under a uniformly random satisfying assignment.

math.PR↗

arXiv2019

Similarity-based Random Survival Forest

Predicting time-to-event outcomes in large databases can be a challenging but important task. One example of this is in predicting the time to a clinical outcome for patients in intensive care units (ICUs), which helps to support critical medical treatment decisions. In this context, the time to an event of interest could be, for example, survival time or time to recovery from a disease/ailment observed within the ICU. The massive health datasets generated from the uptake of Electronic Health Records (EHRs) are quite heterogeneous as patients can be quite dissimilar in their relationship between the feature vector and the outcome, adding more noise than information to prediction. In this paper, we propose a modified random forest method for survival data that identifies similar cases in an attempt to improve accuracy for predicting time-to-event outcomes; this methodology can be applied in various settings, including with ICU databases. We also introduce an adaptation of our methodology in the case of dependent censoring. Our proposed method is demonstrated in the Medical Information Mart for Intensive Care (MIMIC-III) database, and, in addition, we present properties of our methodology through a comprehensive simulation study. Introducing similarity to the random survival forest method indeed provides improved predictive accuracy compared to random survival forest alone across the various analyses we undertook.

stat.CO↗

arXiv2019

Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic MRI Using Mask-RCNN

Prostate cancer (PCa) is the most common cancer in men in the United States. Multiparametic magnetic resonance imaging (mp-MRI) has been explored by many researchers to targeted prostate biopsies and radiation therapy. However, assessment on mp-MRI can be subjective, development of computer-aided diagnosis systems to automatically delineate the prostate gland and the intraprostratic lesions (ILs) becomes important to facilitate with radiologists in clinical practice. In this paper, we first study the implementation of the Mask-RCNN model to segment the prostate and ILs. We trained and evaluated models on 120 patients from two different cohorts of patients. We also used 2D U-Net and 3D U-Net as benchmarks to segment the prostate and compared the model's performance. The contour variability of ILs using the algorithm was also benchmarked against the interobserver variability between two different radiation oncologists on 19 patients. Our results indicate that the Mask-RCNN model is able to reach state-of-art performance in the prostate segmentation and outperforms several competitive baselines in ILs segmentation.

cs.CV↗

arXiv2017

Video Highlight Prediction Using Audience Chat Reactions

Sports channel video portals offer an exciting domain for research on multimodal, multilingual analysis. We present methods addressing the problem of automatic video highlight prediction based on joint visual features and textual analysis of the real-world audience discourse with complex slang, in both English and traditional Chinese. We present a novel dataset based on League of Legends championships recorded from North American and Taiwanese Twitch.tv channels (will be released for further research), and demonstrate strong results on these using multimodal, character-level CNN-RNN model architectures.

cs.CL↗