arXiv subjects

Quoc-Bao Nguyen

Publications and source records attributed to Quoc-Bao Nguyen.

5 recordsLinked to original sources

arXiv2026

Reciprocity can halve what a mechanical network can learn

Tunable mechanical networks are being built as materials that learn in place. Capacity is estimated by counting parameters against target constraints, ignoring Maxwell-Betti reciprocity, the symmetry every passive linear elastic network obeys. When p degrees of freedom are both driven and read, every reachable response block lies in a subspace of codimension p(p-1)/2, whatever the network's size, topology and stiffnesses; at full overlap the unreachable fraction is (p-1)/2p, tending to one half. When a finite list of demands replaces the block, the charge is a list invariant that on coordinate tasks counts the terminal pairs instrumented in both directions, so a shared terminal alone is free. The consequence for training is a number: any learning rule leaves an error at least the norm of the target's antisymmetric part on the shared block, computable in advance; positive definiteness adds an orthogonal term. A second-order optimiser reaches that floor within 1% in 143 of 144 runs, a contrastive rule with a bond-local update direction within 0.1% in 22 of 24; the forbidden directions are the antisymmetric ones, which no tuning reaches and an odd coupling does: we prove that a rank-revealing choice of p(p-1)/2 odd bonds spans them whenever the passive network's wedges do, and realises any small enough antisymmetric shared block exactly in projection. At a fixed number of accessed degrees of freedom, each free to be both driven and read, where the bond count does not bind, overlapping sensors onto actuators roughly doubles the ceiling on reachable dimension; at a fixed count of sensors plus actuators it does not. Prescribed-displacement drives obey a companion law. The symmetry is classical; its fixed-graph consequence at partial overlap is new. For a published robotic metamaterial, no symmetric positive-definite stiffness matrix meets both targets in the deposited linear model.

cond-mat.soft↗

arXiv2026

Blind directions of physical learning networks: where to measure and what to measure

A physical learning network is read at a few accessible nodes. Every task and learning rule that uses only the steady voltages and currents there, at a fixed operating point, acts through the boundary response map. Changes in the kernel of its Jacobian are blind to first order. A walk along one fiber ran to a 600-step cap, one edge then at 15.2 times its start. A decomposition theorem splits the response Jacobian over the hidden components. The blind dimension adds over components whose surviving slots are disjoint. A boundary-to-boundary edge adds one parameter and deletes one slot. Exposing a hidden node changes only its component. For one hidden node the contribution counts the bipartite components of the non-adjacency graph of its neighbors. For a pocket of h hidden nodes a factor-analysis bound caps what outside electrodes can expose. It is attained when every pocket node meets every neighbor and no edge joins two of those neighbors, so past a threshold further electrodes outside such a pocket expose nothing. An electrode inside such a pocket, when its hidden nodes form a clique, is worth h-1 directions where one outside is worth none. Read as vector displacements rather than potentials, the same nodes left no deficit beyond counting in all 90 spring networks tested, each a pocket fully joined to five or more accessible nodes, and nothing blind in 88. What limits the reading is the quantity measured as much as the number of contacts. Maximum-weight spanning forests give a proved upper bound on the blind dimension. The matching equality is proved for one hidden node and conjectured beyond. It holds in all 5,343 components whose maximum could be attained and certified, and the forest count matched the certified blind dimension in 1,448 of 1,500 held-out networks, where the maximum must be searched. A self-learning circuit shows the split on hardware.

physics.soc-ph↗

arXiv2026

A Revisit to Point Estimation Through the Empirical Bayes Method: The Case of Binomial Distribution with Beta Prior and Extension to Poisson Distribution

Between the classical (frequentist) approach, which is based solely on the data, and a fully Bayesian set-up where one assumes a prior distribution for the model parameters, lies the Empirical Bayes (EB) approach which appears to be a good compromise between the aforementioned two approaches. Even though many researchers have suggested various variants of the EB method, the standard practice is to derive the Bayes estimator under a family of suitable priors indexed by its own parameter(s), called the hyperparameter(s), and then replace the unknown hyperparameter(s) by their estimate(s) obtained from the marginal distribution of the data. But the fundamental question that is being raised here is: does the EB method really work to produce an improved estimator - the so-called Empirical Bayes Estimator (EBE)? In this work we are going to revisit the widely cited simple problem of estimating a Binomial parameter using the regular two-parameter Beta family of priors under the quadratic loss function, and prove that the Type-II maximum likelihood (ML-II) step does not work. If we further restrict our attention to one-parameter symmetric Beta family of priors then still the resultant EBE does not show any remarkable performance compared to the MLE details of which have been provided with extensive computations. The Binomial study has been extended to the Poisson model as well.

stat.ME↗

arXiv2017

Automatic Curation of Golf Highlights using Multimodal Excitement Features

The production of sports highlight packages summarizing a game's most exciting moments is an essential task for broadcast media. Yet, it requires labor-intensive video editing. We propose a novel approach for auto-curating sports highlights, and use it to create a real-world system for the editorial aid of golf highlight reels. Our method fuses information from the players' reactions (action recognition such as high-fives and fist pumps), spectators (crowd cheering), and commentator (tone of the voice and word analysis) to determine the most interesting moments of a game. We accurately identify the start and end frames of key shot highlights with additional metadata, such as the player's name and the hole number, allowing personalized content summarization and retrieval. In addition, we introduce new techniques for learning our classifiers with reduced manual training data annotation by exploiting the correlation of different modalities. Our work has been demonstrated at a major golf tournament, successfully extracting highlights from live video streams over four consecutive days.

cs.CV↗

arXiv2016

Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images

Melanoma is the deadliest form of skin cancer. While curable with early detection, only highly trained specialists are capable of accurately recognizing the disease. As expertise is in limited supply, automated systems capable of identifying disease could save lives, reduce unnecessary biopsies, and reduce costs. Toward this goal, we propose a system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin lesions, as well as analyzing the detected area and surrounding tissue for melanoma detection. The system is evaluated using the largest publicly available benchmark dataset of dermoscopic images, containing 900 training and 379 testing images. New state-of-the-art performance levels are demonstrated, leading to an improvement in the area under receiver operating characteristic curve of 7.5% (0.843 vs. 0.783), in average precision of 4% (0.649 vs. 0.624), and in specificity measured at the clinically relevant 95% sensitivity operating point 2.9 times higher than the previous state-of-the-art (36.8% specificity compared to 12.5%). Compared to the average of 8 expert dermatologists on a subset of 100 test images, the proposed system produces a higher accuracy (76% vs. 70.5%), and specificity (62% vs. 59%) evaluated at an equivalent sensitivity (82%).

cs.CV↗