Clustering of Data with Missing Entries

The analysis of large datasets is often complicated by the presence of missing entries, mainly because most of the current machine learning algorithms are designed to work with full data. The main focus of this work is to introduce a clustering algorithm, that will provide good clustering even in the presence of missing data. The proposed technique solves an $\ell_0$ fusion penalty based optimization problem to recover the clusters. We theoretically analyze the conditions needed for the successful recovery of the clusters. We also propose an algorithm to solve a relaxation of this problem using saturating non-convex fusion penalties. The method is demonstrated on simulated and real datasets, and is observed to perform well in the presence of large fractions of missing entries.Comment: arXiv admin note: substantial text overlap with arXiv:1709.0187

UniCat: Crafting a Stronger Fusion Baseline for Multimodal Re-Identification

Multimodal Re-Identification (ReID) is a popular retrieval task that aims to re-identify objects across diverse data streams, prompting many researchers to integrate multiple modalities into a unified representation. While such fusion promises a holistic view, our investigations shed light on potential pitfalls. We uncover that prevailing late-fusion techniques often produce suboptimal latent representations when compared to methods that train modalities in isolation. We argue that this effect is largely due to the inadvertent relaxation of the training objectives on individual modalities when using fusion, what others have termed modality laziness. We present a nuanced point-of-view that this relaxation can lead to certain modalities failing to fully harness available task-relevant information, and yet, offers a protective veil to noisy modalities, preventing them from overfitting to task-irrelevant data. Our findings also show that unimodal concatenation (UniCat) and other late-fusion ensembling of unimodal backbones, when paired with best-known training techniques, exceed the current state-of-the-art performance across several multimodal ReID benchmarks. By unveiling the double-edged sword of "modality laziness", we motivate future research in balancing local modality strengths with global representations.Comment: Accepted NeurIPS 2023 UniReps, 9 pages, 4 table

Thermodynamics and kinetics of the Mg65Cu25Y10 bulk metallic glass forming liquid

The thermodynamics and kinetics of the bulk metallic glass forming Mg65Cu25Y10 liquid were investigated using differential scanning calorimetry and three-point beam bending. The experiments lead to the determination of the thermodynamic functions as well as the viscosity of the supercooled liquid. The viscosity shows a temperature dependence, which is consistent with that of a strong glass similar to Zr–Ti–Cu–Ni–Be bulk metallic glasses or sodium silicate glasses. This contrasts with more fragile conventional metallic glass formers or pure metals. The relatively weak temperature dependence of the thermodynamic functions of the supercooled liquid is related to these sluggish kinetics in the supercooled liquid. Entropy, viscosity, and kinetic glass transition are compared in the frameworks of the fragility concept and the Adam–Gibbs theory. Strong liquid behavior retards the formation of crystals kinetically and thermodynamically

A Message Passing Algorithm for the Minimum Cost Multicut Problem

We propose a dual decomposition and linear program relaxation of the NP -hard minimum cost multicut problem. Unlike other polyhedral relaxations of the multicut polytope, it is amenable to efficient optimization by message passing. Like other polyhedral elaxations, it can be tightened efficiently by cutting planes. We define an algorithm that alternates between message passing and efficient separation of cycle- and odd-wheel inequalities. This algorithm is more efficient than state-of-the-art algorithms based on linear programming, including algorithms written in the framework of leading commercial software, as we show in experiments with large instances of the problem from applications in computer vision, biomedical image analysis and data mining.Comment: Added acknowledgment

Examining Stress Relaxation in a Dissimilar Metal Weld Subjected to Postweld Heat Treatment

Dissimilar metal welds are often required in nuclear power plants to join components made from austenitic steels to those from ferritic steels, particularly in fast breeder reactor plants, in order to join the intermediate heat exchanger to the steam generator. The process of welding alters the microstructure of the base materials and causes residual stresses to form, both because of the change in the microstructure and the differing thermal histories in various regions. Postweld heat treatment (PWHT) is required to relieve the residual stresses and achieve preferable microstructural gradients across the weld joint. Therefore, in order to arrive at the optimal PWHT process, it is necessary to investigate the effects of heat treatment on the joint integrity, microstructure, and residual stress relaxation in the welds. To investigate the effect of PWHT on the residual stress relaxation and corresponding alteration of microstructure across a welded joint, a dissimilar weld between modified 9Cr-1Mo steel and austenitic stainless steel AISI 316LN was made using autogenous electron beam welding. To achieve this, the welding process was first modeled numerically using finite element analysis, and the residual stress predictions were validated by experimental investigation using neutron diffraction. The validated model was then used to study the residual stress relaxation through the simulation of PWHT. The predicted stress relaxation was compared with contour method measurement of residual stresses in the actual welded plate subjected to PWHT. The results indicate that, although some relaxation of residual stresses occurred during PWHT, there is still a significant portion of highly localized residual stresses left in the specimen