Joint Cuts and Matching of Partitions in One Graph

As two fundamental problems, graph cuts and graph matching have been investigated over decades, resulting in vast literature in these two topics respectively. However the way of jointly applying and solving graph cuts and matching receives few attention. In this paper, we first formalize the problem of simultaneously cutting a graph into two partitions i.e. graph cuts and establishing their correspondence i.e. graph matching. Then we develop an optimization algorithm by updating matching and cutting alternatively, provided with theoretical analysis. The efficacy of our algorithm is verified on both synthetic dataset and real-world images containing similar regions or structures

Molecular Conformation Generation via Shifting Scores

Molecular conformation generation, a critical aspect of computational chemistry, involves producing the three-dimensional conformer geometry for a given molecule. Generating molecular conformation via diffusion requires learning to reverse a noising process. Diffusion on inter-atomic distances instead of conformation preserves SE(3)-equivalence and shows superior performance compared to alternative techniques, whereas related generative modelings are predominantly based upon heuristical assumptions. In response to this, we propose a novel molecular conformation generation approach driven by the observation that the disintegration of a molecule can be viewed as casting increasing force fields to its composing atoms, such that the distribution of the change of inter-atomic distance shifts from Gaussian to Maxwell-Boltzmann distribution. The corresponding generative modeling ensures a feasible inter-atomic distance geometry and exhibits time reversibility. Experimental results on molecular datasets demonstrate the advantages of the proposed shifting distribution compared to the state-of-the-art.Comment: 18 pages, 7 figure

Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms

This paper studies a new task of federated learning (FL) for semantic parsing, where multiple clients collaboratively train one global model without sharing their semantic parsing data. By leveraging data from multiple clients, the FL paradigm can be especially beneficial for clients that have little training data to develop a data-hungry neural semantic parser on their own. We propose an evaluation setup to study this task, where we re-purpose widely-used single-domain text-to-SQL datasets as clients to form a realistic heterogeneous FL setting and collaboratively train a global model. As standard FL algorithms suffer from the high client heterogeneity in our realistic setup, we further propose a novel LOss Reduction Adjusted Re-weighting (Lorar) mechanism to mitigate the performance degradation, which adjusts each client's contribution to the global model update based on its training loss reduction during each round. Our intuition is that the larger the loss reduction, the further away the current global model is from the client's local optimum, and the larger weight the client should get. By applying Lorar to three widely adopted FL algorithms (FedAvg, FedOPT and FedProx), we observe that their performance can be improved substantially on average (4%-20% absolute gain under MacroAvg) and that clients with smaller datasets enjoy larger performance gains. In addition, the global model converges faster for almost all the clients.Comment: ACL 2023 long pape

Variational Wasserstein Barycenters for Geometric Clustering

We propose to compute Wasserstein barycenters (WBs) by solving for Monge maps with variational principle. We discuss the metric properties of WBs and explore their connections, especially the connections of Monge WBs, to K-means clustering and co-clustering. We also discuss the feasibility of Monge WBs on unbalanced measures and spherical domains. We propose two new problems -- regularized K-means and Wasserstein barycenter compression. We demonstrate the use of VWBs in solving these clustering-related problems

Cu-In Halide Perovskite solar absorbers

The long-term chemical instability and the presence of toxic Pb in otherwise stellar solar absorber APbX$_{3}$ have hindered their large-scale commercialization. Previously explored ways to achieve Pb-free halide perovskites involved replacing Pb$^{2+}$ with other similar M$^{2+}$ cations in ns$^2$ electron configuration, e.g., Sn$^{2+}$ or by Bi$^{3+}$ (plus Ag$^+$), but unfortunately this showed either poor stability (M = Sn) or weakly absorbing oversized indirect gaps (M = Bi), prompting concerns that perhaps stability and good optoelectronic properties might be contraindicated. Herein, we exploit the electronic structure underpinning of classic Cu[In,Ga]Se$_{2}$ (CIGS) chalcopyrite solar absorbers to design Pb-free halide perovskites by transmuting 2Pb to the pair [B$^{IB}$ + C$^{III}$]. The resulting group of double perovskites with formula A$_2$BCX$_6$ (A = K, Rb, Cs; B = Cu, Ag; C = Ga, In; X = Cl, Br, I) benefits from the ionic, yet narrow-gap character of halide perovskites, and at the same time borrows the advantage of the strong and rapidly rising Cu(d)/Se(p) $\rightarrow$ Ga/In(s/p) valence-to-conduction-band absorption spectra known from CIGS. This constitutes a new group of CuIn-based Halide Perovskite (CIHP). Our first-principles calculations guided by such design principles indicate that the CIHPs class has members with clear thermodynamic stability, showing rather strong direct-gap optical transitions, and manifesting a wide-range of tunable gap values (from zero to about 2.5 eV) and combination of light electron and heavy-light hole effective masses. Materials screening of candidate CHIPs then identifies the best-of-class Rb$_2$[CuIn]Cl$_6$, Rb$_2$[AgIn]Br$_6$ and Cs$_2$[AgIn]Br$_6$, having direct band gaps of 1.36, 1.46 and 1.50 eV, and a theoretical spectroscopic limited maximal efficiency comparable to chalcopyrites and CH$_3$NH$_3$PbI$_3$.Comment: 25 pages, 6 figure