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The resurgence of interest in Kondo insulators has been driven by two major mysteries: the presence of metallic surface states and the observation of quantum oscillations. To further explore these mysteries, it is crucial to investigate another similar system beyond the two existing ones, SmB₆and YbB₁₂. Here, we address this by reporting on a Kondo insulator, U₃Bi₄Ni₃. Our transport measurements reveal that a surface state emerges below 250 kelvin and dominates transport properties below 150 kelvin, which is well above the temperature scale of SmB₆and YbB₁₂. At low temperatures, the surface conductivity is about one order of magnitude higher than the bulk. The robustness of the surface state indicates that it is inherently protected. The similarities and differences between U₃Bi₄Ni₃and the other two Kondo insulators will provide valuable insights into the nature of metallic surface states in Kondo insulators and their interplay with strong electron correlations. 

This paper investigates the effect of filtering (or modulating) the functional magnetic resonance imaging (fMRI) time-series on intelligence metrics predicted using dynamic functional connectivity (dFC). Thirteen brain regions that have highest correlation with intelligence are selected and their corresponding time-series are filtered. Using filtered time-series, the modified intelligence metrics are predicted. This experiment investigates whether modulating the time-series of one or two regions of the brain will increase or decrease the fluid ability and fluid intelligence among healthy humans. Two sets of experiments are performed. In the first case, each of the thirteen regions is separately filtered using four different digital filters with passbands: i) 0 - 0.25π, ii) 0.25π - 0.5π, iii) 0.5π - 0.75π, and iv) 0.75π – π, respectively. In the second case, two of the thirteen regions are filtered simultaneously using a low-pass filter of passband 0 - 0.25π. In both cases, the predicted intelligence declined for 45-65% of the subjects after filtering in comparison with the ground truths. In the first case, the low-pass filtering process had the highest predicted intelligence among the four filters. In the second case, it was noticed that the filtering of two regions simultaneously resulted in a higher prediction of intelligence for over 80% of the subjects compared to low-pass filtering of a single region. 

We present an automated pipeline capable of distinguishing the phenotypes of myeloid-derived suppressor cells (MDSC) in healthy and tumor-bearing tissues in mice using flow cytometry data. In contrast to earlier work where samples are analyzed individually, we analyze all samples from each tissue collectively using a representative template for it. We demonstrate with 43 flow cytometry samples collected from three tissues, naive bone-marrow, spleens of tumor-bearing mice, and intra-peritoneal tumor, that a set of templates serves as a better classifier than popular machine learning approaches including support vector machines and neural networks. Our "interpretable machine learning" approach goes beyond classification and identifies distinctive phenotypes associated with each tissue, information that is clinically useful. Hence the pipeline presented here leads to better understanding of the maturation and differentiation of MDSCs using high-throughput data.