Probabilistic Label Relation Graphs with Ising Models

We consider classification problems in which the label space has structure. A common example is hierarchical label spaces, corresponding to the case where one label subsumes another (e.g., animal subsumes dog). But labels can also be mutually exclusive (e.g., dog vs cat) or unrelated (e.g., furry, carnivore). To jointly model hierarchy and exclusion relations, the notion of a HEX (hierarchy and exclusion) graph was introduced in [7]. This combined a conditional random field (CRF) with a deep neural network (DNN), resulting in state of the art results when applied to visual object classification problems where the training labels were drawn from different levels of the ImageNet hierarchy (e.g., an image might be labeled with the basic level category "dog", rather than the more specific label "husky"). In this paper, we extend the HEX model to allow for soft or probabilistic relations between labels, which is useful when there is uncertainty about the relationship between two labels (e.g., an antelope is "sort of" furry, but not to the same degree as a grizzly bear). We call our new model pHEX, for probabilistic HEX. We show that the pHEX graph can be converted to an Ising model, which allows us to use existing off-the-shelf inference methods (in contrast to the HEX method, which needed specialized inference algorithms). Experimental results show significant improvements in a number of large-scale visual object classification tasks, outperforming the previous HEX model.Comment: International Conference on Computer Vision (2015

Pairing Correlations Near a Kondo-Destruction Quantum Critical Point

Motivated by the unconventional superconductivity observed in heavy-fermion metals, we investigate pairing susceptibilities near a continuous quantum phase transition of the Kondo-destruction type. We solve two-impurity Bose-Fermi Anderson models with Ising and Heisenberg forms of the interimpurity exchange interaction using continuous-time quantum Monte-Carlo and numerical renormalization-group methods. Each model exhibits a Kondo-destruction quantum critical point separating Kondo-screened and local-moment phases. For antiferromagnetic interimpurity exchange interactions, singlet pairing is found to be enhanced in the vicinity of the transition. Implications of this result for heavy-fermion superconductivity are discussed.Comment: 5 pages, 5 figures + supplementary material 2 page, 2 figures: Replaced with published versio

Optimizing NOTEARS Objectives via Topological Swaps

Recently, an intriguing class of non-convex optimization problems has emerged in the context of learning directed acyclic graphs (DAGs). These problems involve minimizing a given loss or score function, subject to a non-convex continuous constraint that penalizes the presence of cycles in a graph. In this work, we delve into the optimization challenges associated with this class of non-convex programs. To address these challenges, we propose a bi-level algorithm that leverages the non-convex constraint in a novel way. The outer level of the algorithm optimizes over topological orders by iteratively swapping pairs of nodes within the topological order of a DAG. A key innovation of our approach is the development of an effective method for generating a set of candidate swapping pairs for each iteration. At the inner level, given a topological order, we utilize off-the-shelf solvers that can handle linear constraints. The key advantage of our proposed algorithm is that it is guaranteed to find a local minimum or a KKT point under weaker conditions compared to previous work and finds solutions with lower scores. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches in terms of achieving a better score. Additionally, our method can also be used as a post-processing algorithm to significantly improve the score of other algorithms. Code implementing the proposed method is available at https://github.com/duntrain/topo.Comment: 39 pages, 12 figures, ICML 202

Mouse cytoplasmic dynein intermediate chains: identification of new isoforms, alternative splicing and tissue distribution of transcripts

BACKGROUND: Intracellular transport of cargoes including organelles, vesicles, signalling molecules, protein complexes, and RNAs, is essential for normal function of eukaryotic cells. The cytoplasmic dynein complex is an important motor that moves cargos along microtubule tracks within the cell. In mammals this multiprotein complex includes dynein intermediate chains 1 and 2 which are encoded by two genes, Dync1i1 and Dync1i2. These proteins are involved in dynein cargo binding and dynein complexes with different intermediate chains bind to specific cargoes, although the mechanisms to achieve this are not known. The DYNC1I1 and DYNC1I2 proteins are translated from different splice isoforms, and specific forms of each protein are essential for the function of different dynein complexes in neurons. METHODOLOGY/PRINCIPAL FINDINGS: Here we have undertaken a systematic survey of the dynein intermediate chain splice isoforms in mouse, basing our study on mRNA expression patterns in a range of tissues, and on bioinformatics analysis of mouse, rat and human genomic and cDNA sequences. We found a complex pattern of alternative splicing of both dynein intermediate chain genes, with maximum complexity in the embryonic and adult nervous system. We have found novel transcripts, including some with orthologues in human and rat, and a new promoter and alternative non-coding exon 1 for Dync1i2. CONCLUSIONS/SIGNIFICANCE: These data, including the cloned isoforms will be essential for understanding the role of intermediate chains in the cytoplasmic dynein complex, particularly their role in cargo binding within individual tissues including different brain regions

Cross-subject dual-domain fusion network with task-related and task-discriminant component analysis enhancing one-shot SSVEP classification

This study addresses the significant challenge of developing efficient decoding algorithms for classifying steady-state visual evoked potentials (SSVEPs) in scenarios characterized by extreme scarcity of calibration data, where only one calibration is available for each stimulus target. To tackle this problem, we introduce a novel cross-subject dual-domain fusion network (CSDuDoFN) incorporating task-related and task-discriminant component analysis (TRCA and TDCA) for one-shot SSVEP classification. The CSDuDoFN framework is designed to comprehensively transfer information from source subjects, while TRCA and TDCA are employed to exploit the single available calibration of the target subject. Specifically, we develop multi-reference least-squares transformation (MLST) to map data from both source subjects and the target subject into the domain of sine-cosine templates, thereby mitigating inter-individual variability and benefiting transfer learning. Subsequently, the transformed data in the sine-cosine templates domain and the original domain data are separately utilized to train a convolutional neural network (CNN) model, with the adequate fusion of their feature maps occurring at distinct network layers. To further capitalize on the calibration of the target subject, source aliasing matrix estimation (SAME) data augmentation is incorporated into the training process of the ensemble TRCA (eTRCA) and TDCA models. Ultimately, the outputs of the CSDuDoFN, eTRCA, and TDCA are combined for SSVEP classification. The effectiveness of our proposed approach is comprehensively evaluated on three publicly available SSVEP datasets, achieving the best performance on two datasets and competitive performance on one. This underscores the potential for integrating brain-computer interface (BCI) into daily life.Comment: 10 pages,6 figures, and 3 table

The bioinformatics resource for oral pathogens

Complete genomic sequences of several oral pathogens have been deciphered and multiple sources of independently annotated data are available for the same genomes. Different gene identification schemes and functional annotation methods used in these databases present a challenge for cross-referencing and the efficient use of the data. The Bioinformatics Resource for Oral Pathogens (BROP) aims to integrate bioinformatics data from multiple sources for easy comparison, analysis and data-mining through specially designed software interfaces. Currently, databases and tools provided by BROP include: (i) a graphical genome viewer (Genome Viewer) that allows side-by-side visual comparison of independently annotated datasets for the same genome; (ii) a pipeline of automatic data-mining algorithms to keep the genome annotation always up-to-date; (iii) comparative genomic tools such as Genome-wide ORF Alignment (GOAL); and (iv) the Oral Pathogen Microarray Database. BROP can also handle unfinished genomic sequences and provides secure yet flexible control over data access. The concept of providing an integrated source of genomic data, as well as the data-mining model used in BROP can be applied to other organisms. BROP can be publicly accessed at