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Abstract Motivated by applications in text mining and discrete distribution inference, we test for equality of probability mass functions of K groups of high-dimensional multinomial distributions. Special cases of this problem include global testing for topic models, two-sample testing in authorship attribution, and closeness testing for discrete distributions. A test statistic, which is shown to have an asymptotic standard normal distribution under the null hypothesis, is proposed. This parameter-free limiting null distribution holds true without requiring identical multinomial parameters within each group or equal group sizes. The optimal detection boundary for this testing problem is established, and the proposed test is shown to achieve this optimal detection boundary across the entire parameter space of interest. The proposed method is demonstrated in simulation studies and applied to analyse two real-world datasets to examine, respectively, variation among customer reviews of Amazon movies and the diversity of statistical paper abstracts. 

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society. 

A polylithic integration technology is demonstrated for seamless stitching of RF and digital chiplets. In this technology, stitch-chips with compressible microinterconnects (CMIs) are used for low-loss and dense interconnection between chiplets. A testbed using fused-silica stitch-chips with integrated CMIs is demonstrated including modeling, fabrication, assembly, and characterization. A 500 µm-long stitch-chip signal link is measured to have less than 0.4 dB insertion loss up to 30 GHz. A simulated eye diagram for 1000 µm-long stitch-chip signal link has a clear opening at 50 Gbps data rate. Moreover, the S-parameters of the CMIs are extracted from this testbed and show less than 0.17 dB insertion loss up to 30 GHz. Benchmarking to silicon interposer based interconnection is also reported. 

A degenerate band edge is a resonant dispersive behavior of coupled transmission lines arising from a fourth-order degeneracy due to the coalescence of two propagating and two evanescent modes. It leads to a so-called giant resonance resulting in field enhancement inside the transmission line. In this paper, we propose a SIW periodic line supporting a degenerate band edge and we study the impact of losses. Conductor and dielectric losses are analyzed in the full-wave simulations of the unit cell and of truncated structures.