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1. Minimum detectable levels of biologically relevant elements in P8 filter paper standard using PIXE spectroscopy

   https://doi.org/10.1016/j.nimb.2024.165544  

   Bowen, Charles T; Byers, Todd A; Nook, Cory; Kaur_Saini, Darshpreet; Rout, Bibhudutta; Glass, Gary A (December 2024, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms)

   Bogdanović; Iva; Lorenz, Katharina (Ed.)

   PIXE analysis was conducted on p8 fisher brand filter paper samples soaked in elemental standard solutions to determine the minimum detectable levels of Al, Si, P, S, Cl, K, Ca, Cr, Fe, Ni, Cu, and Se. All samples were analyzed with beam parameters of 2 µC incident charge, and beam current of less than 2 nA at 2 MeV beam energy. Minimum detectable levels were obtained by analyzing the x-ray spectrum in the GeoPIXE analysis package, and the data for each element would be averaged over all collected spectra. The minimum detectable level in parts per million was found to be on average 9.59 for Al, 4.6 for Si, 3.23 for P, 2.27 for S, 1.82 for Cl, 1.15 for K, 0.88 for Ca, 0.51 for Cr, 0.07 for Mn, 0.54 for Fe, 1.59 for Ni, 2.0 for Zn, 1.55 for Cu, and 6.5 for Se. Minimal deviation from the averaged values was observed, except in cases where samples contained high concentrations of elements with overlapping X-ray energies. 

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2. CpHMD Input Files and Supplementary Movies Showing Self-assembly and Bilayer CpHMD Trajectories of Different LNP Formulations at Varying Solution pH

   https://doi.org/10.5281/zenodo.15035911  

   Schneebeli, Severin T; Colston, Kyle J; Monsalve, Santiago C (January 2025, Zenodo)

   Abstract: General note: a/b/c represent replicas 1, 2, and 3, respectively Movie S1a/b/c. Self-assembly of the DLin-KC2-DMA LNP formulation for 100 ns at pH = 3. Movie S2a/b/c. Self-assembly of the DLin-KC2-DMA LNP formulation for 100 ns at pH = 4. Movie S3a/b/c. Self-assembly of the DLin-KC2-DMA LNP formulation for 100 ns at pH = 5. Movie S4a/b/c. Self-assembly of the DLin-KC2-DMA LNP formulation for 100 ns at pH = 6. Movie S5a/b/c. Self-assembly of the DLin-KC2-DMA LNP formulation for 100 ns at pH = 7. Movie S6a/b/c. Self-assembly of the DLin-KC2-DMA LNP formulation for 100 ns at pH = 8. Movie S7a/b/c. Self-assembly of the DLin-KC2-DMA LNP formulation for 100 ns at pH = 9. Movie S8a/b/c. Self-assembly of the DLin-KC2-DMA LNP formulation for 100 ns at pH = 10. Movie S9a/b/c. Self-assembly of the DODAP LNP formulation for 100 ns at pH = 3. Movie S10a/b/c. Self-assembly of the DODAP LNP formulation for 100 ns at pH = 4. Movie S11a/b/c. Self-assembly of the DODAP LNP formulation for 100 ns at pH = 5. Movie S12a/b/c. Self-assembly of the DODAP LNP formulation for 100 ns at pH = 6. Movie S13a/b/c. Self-assembly of the DODAP LNP formulation for 100 ns at pH = 7. Movie S14a/b/c. Self-assembly of the DODAP LNP formulation for 100 ns at pH = 8. Movie S15a/b/c. Self-assembly of the DODAP LNP formulation for 100 ns at pH = 9. Movie S16a/b/c. Self-assembly of the DODAP LNP formulation for 100 ns at pH = 10. Movie S17a/b/c. Self-assembly of the DODMA LNP formulation for 100 ns at pH = 3. Movie S18a/b/c. Self-assembly of the DODMA LNP formulation for 100 ns at pH = 4. Movie S19a/b/c. Self-assembly of the DODMA LNP formulation for 100 ns at pH = 5. Movie S20a/b/c. Self-assembly of the DODMA LNP formulation for 100 ns at pH = 6. Movie S21a/b/c. Self-assembly of the DODMA LNP formulation for 100 ns at pH = 7. Movie S22a/b/c. Self-assembly of the DODMA LNP formulation for 100 ns at pH = 8. Movie S23a/b/c. Self-assembly of the DODMA LNP formulation for 100 ns at pH = 9. Movie S24a/b/c. Self-assembly of the DODDMA LNP formulation for 100 ns at pH = 10. Movie S25a/b/c. Self-assembly of the DLin-DMA LNP formulation for 100 ns at pH = 3. Movie S26a/b/c. Self-assembly of the DLin-DMA LNP formulation for 100 ns at pH = 4. Movie S27a/b/c. Self-assembly of the DLin-DMA LNP formulation for 100 ns at pH = 5. Movie S28a/b/c. Self-assembly of the DLin-DMA LNP formulation for 100 ns at pH = 6. Movie S29a/b/c. Self-assembly of the DLin-DMA LNP formulation for 100 ns at pH = 7. Movie S30a/b/c. Self-assembly of the DLin-DMA LNP formulation for 100 ns at pH = 8. Movie S31a/b/c. Self-assembly of the DLin-DMA LNP formulation for 100 ns at pH = 9. Movie S32a/b/c. Self-assembly of the DLin-DMA LNP formulation for 100 ns at pH = 10. Movie S33a/b/c. Self-assembly of the DLin-MC3-DMA LNP formulation for 100 ns at pH = 3. Movie S34a/b/c. Self-assembly of the DLin-MC3-DMA LNP formulation for 100 ns at pH = 4. Movie S35a/b/c. Self-assembly of the DLin-MC3-DMA LNP formulation for 100 ns at pH = 5. Movie S36a/b/c. Self-assembly of the DLin-MC3-DMA LNP formulation for 100 ns at pH = 6. Movie S37a/b/c. Self-assembly of the DLin-MC3-DMA LNP formulation for 100 ns at pH = 7. Movie S38a/b/c. Self-assembly of the DLin-MC3-DMA LNP formulation for 100 ns at pH = 8. Movie S39a/b/c. Self-assembly of the DLin-MC3-DMA LNP formulation for 100 ns at pH = 9. Movie S40a/b/c. Self-assembly of the DLin-MC3-DMA LNP formulation for 100 ns at pH = 10. Movie S41a/b/c. Simulated pH change of the acidic DODAP structure to pH = 7.4. Movie S42a/b/c. Mixed self-assembly/bilayer simulations of DLin-KC2-DMA for 50 ns at pH = 7, started from the corresponding pH 10 structures, which were equilibrated for 100 ns. Movie S43a/b/c. Mixed self-assembly/bilayer simulation of DLin-KC2-DMA for 50 ns at pH = 8, started from the corresponding pH 10 structure, which was equilibrated for 100 ns. Movie S44a/b/c. Mixed self-assembly/bilayer simulation of DLin-KC2-DMA for 50 ns at pH = 9, started from the corresponding pH 10 structure, which was equilibrated for 100 ns. Movie S45a/b/c. Mixed self-assembly/bilayer simulations of DLin-MC3-DMA for 50 ns at pH = 7, started from the corresponding pH 10 structures, which were equilibrated for 100 ns. Movie S46a/b/c. Mixed self-assembly/bilayer simulation of DLin-MC3-DMA for 50 ns at pH = 8, started from the corresponding pH 10 structure, which was equilibrated for 100 ns. Movie S47a/b/c. Mixed self-assembly/bilayer simulation of DLin-MC3-DMA for 50 ns at pH = 9, started from the corresponding pH 10 structure, which was equilibrated for 100 ns. Movie S48a/b/c. Mixed self-assembly/bilayer simulations of DODMA for 50 ns at pH = 7, started from the corresponding pH 10 structures, which were equilibrated for 100 ns. Movie S49a/b/c. Mixed self-assembly/bilayer simulation of DODMA for 50 ns at pH = 8, started from the corresponding pH 10 structure, which was equilibrated for 100 ns. Movie S50a/b/c. Mixed self-assembly/bilayer simulation of DODMA for 50 ns at pH = 9, started from the corresponding pH 10 structure, which was equilibrated for 100 ns. Movie S51a/b/c. Mixed self-assembly/bilayer simulations of DLin-DMA for 50 ns at pH = 7, started from the corresponding pH 10 structures, which were equilibrated for 100 ns. Movie S52a/b/c. Mixed self-assembly/bilayer simulation of DLin-DMA for 50 ns at pH = 8, started from the corresponding pH 10 structure, which was equilibrated for 100 ns. Movie S53a/b/c. Mixed self-assembly/bilayer simulation of DLin-DMA for 50 ns at pH = 9, started from the corresponding pH 10 structure, which was equilibrated for 100 ns. Movie S54a/b/c. Mixed self-assembly/bilayer simulation of DODAP for 50 ns at pH = 7, started from the corresponding pH 10 structure, which was equilibrated for 100 ns. Movie S55a/b/c. Mixed self-assembly/bilayer simulation of DODAP for 50 ns at pH = 8, started from the corresponding pH 10 structure, which was equilibrated for 100 ns. Movie S56a/b/c. Mixed self-assembly/bilayer simulations of DODAP for 50 ns at pH = 9, started from the corresponding pH 10 structures, which were equilibrated for 100 ns. 

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3. Role of Regularization in the Prediction of Valence from Speech

   https://doi.org/10.21437/Interspeech.2018-2508  

   Sridhar, Kusha; Parthasarathy, Srinivas; Busso, Carlos (September 2018, Interspeech 2018)

   Regularization plays a key role in improving the prediction of emotions using attributes such as arousal, valence and dominance. Regularization is particularly important with deep neural networks (DNNs), which have millions of parameters. While previous studies have reported competitive performance for arousal and dominance, the prediction results for valence using acoustic features are significantly lower. We hypothesize that higher regularization can lead to better results for valence. This study focuses on exploring the role of dropout as a form of regularization for valence, suggesting the need for higher regularization. We analyze the performance of regression models for valence, arousal and dominance as a function of the dropout probability. We observe that the optimum dropout rates are consistent for arousal and dominance. However, the optimum dropout rate for valence is higher. To understand the need for higher regularization for valence, we perform an empirical analysis to explore the nature of emotional cues conveyed in speech. We compare regression models with speakerdependent and speaker-independent partitions for training and testing. The experimental evaluation suggests stronger speaker dependent traits for valence. We conclude that higher regularization is needed for valence to force the network to learn global patterns that generalize across speakers. 

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4. Effects of ferromagnetic branch structures on coercivity of alnico permanent magnets

   https://doi.org/10.1063/9.0000298  

   Won, Hoyun; Hong, Yang-Ki; Choi, Minyeong; Mankey, Gary J.; Lee, Jongkook; Lee, Taegyu; Lim, Tae Won (March 2022, AIP Advances)

   The effects of various branches geometry and dimensions such as length, thickness, and width for H-, U-, O-, YH- and YU-shaped alnico structures on coercivity ( H ci ) using micromagnetic simulation for coherent rotation and curling modes are investigated. The simulation results suggest that the H-shaped structure needs long and short branch length for the coherent rotation and curling, respectively, regardless of branch thickness and width to realize high H ci . Short branch length with thin thickness and short width are recommended for both rotations for the U- and O-shaped structures. Lastly, both Y-shaped structures need branch with long length, thin thickness, and mid-long width for the coherent rotation, but short width for the YH-shaped and mid-long width for the YU-shaped regardless of length and thickness for curling are desired. Furthermore, among the five studied structures, H- or YH-shaped structure for coherent rotation and O-shaped structure for curling are highly recommended for fabrication to realize a high H ci . 

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5. Faster Linear Algebra for Distance Matrices

   Indyk, Piotr; Silwal, Sandeep (January 2022, Conference on Neural Information Processing Systems)

   The distance matrix of a dataset X of n points with respect to a distance function f represents all pairwise distances between points in X induced by f. Due to their wide applicability, distance matrices and related families of matrices have been the focus of many recent algorithmic works. We continue this line of research and take a broad view of algorithm design for distance matrices with the goal of designing fast algorithms, which are specifically tailored for distance matrices, for fundamental linear algebraic primitives. Our results include efficient algorithms for computing matrix-vector products for a wide class of distance matrices, such as the l1 metric for which we get a linear runtime, as well as a quadratic lower bound for any algorithm which computes a matrix-vector product for the l_infty case. Our upper bound results have many further downstream applications, including the fastest algorithm for computing a relative error low-rank approximation for the distance matrix induced by l1 and l2 functions and the fastest algorithm for computing an additive error lowrank approximation for the l2 metric, in addition to applications for fast matrix multiplication among others. We also give algorithms for constructing distance matrices and show that one can construct an approximate l2 distance matrix in time faster than the bound implied by the Johnson-Lindenstrauss lemma. 

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