Data-driven integration of norm-penalized mean-variance portfolios

Mean-variance optimization (MVO) is known to be sensitive to estimation error in its inputs. Norm penalization of MVO programs is a regularization technique that can mitigate the adverse effects of estimation error. We augment the standard MVO program with a convex combination of parameterized $L_1$ and $L_2$-norm penalty functions. The resulting program is a parameterized quadratic program (QP) whose dual is a box-constrained QP. We make use of recent advances in neural network architecture for differentiable QPs and present a data-driven framework for optimizing parameterized norm-penalties to minimize the downstream MVO objective. We present a novel technique for computing the derivative of the optimal primal solution with respect to the parameterized $L_1$-norm penalty by implicit differentiation of the dual program. The primal solution is then recovered from the optimal dual variables. Historical simulations using US stocks and global futures data demonstrate the benefit of the data-driven optimization approach

Distance of Interference of Red Rice (Orya sativa) in Rice (O. sativa)

Three rice cultivars were grown to determine the distance at which red rice affects growth and grain yield. Red rice reduced grain yield of Lemont when rice plants grew within 71 and 53 cm of red rice in 1986 and 1988, respectively. Grain yield of Newbonnet was reduced when grown within 53 cm of red rice in both years. Grain yield of Tebonnet was reduced when grown within 53 and 36 cm of red rice in 1986 and 1988, respectively. Grain yield reduction in influenced areas averaged 35, 26 and 21% for Lemont, Newbonnet, and Tebonnet, respectively. As the distance increased at 10-cm increments from the red rice row, Lemont, Newbonnet, and Teboment grain yields increased 49 to 85, 32 to 40, and 24 to 33 g/m2, respectively. Rice straw dry weight was reduced when Lemont and Tebonnet were grown within71 and 36 cm of red rice in 1986 and 1988, respectively. Straw dry weight of Newbonnet was reduced when grown within 36 cm of red rice in both years. As the distance increased at 10-cm increments from the red rice row, Lemont, Newbonnet and Tebonnet straw biomass increased 22 to 46, 10 to 18, and 12 to 20 g/m2,respectively. Rice panicles/ m2 were reduced when Lemont, Newbonnet, and Tebonnet were grown within 36, 18, and 18 cm of red rice, respectively. Rice grains/panicle were reduced when rice was grown within 71, 71, and 36 cm of red rice for Lemont, Newbonnet, and Tebonnet, respectively

Income Inequality, Household Income, and Mass Shooting in the United States

Mass shootings are becoming a more common occurrence in the United States. Data show that mass shootings increased steadily over the past nearly 50 years. Crucial is that the wide-ranging adverse effects of mass shootings generate negative mental health outcomes on millions of Americans, including fear, anxiety, and ailments related to such afflictions. This study extends previous research that finds a strong positive relationship between income inequality and mass shootings by examining the effect of household income as well as the interaction between inequality and income. To conduct our analyses, we compile a panel dataset with information across 3,144 counties during the years 1990 to 2015. Mass shootings was measured using a broad definition of three or more victim injuries. Income inequality was calculated using the post-tax version of the Gini coefficient. Our results suggest that while inequality and income alone are both predictors of mass shootings, their impacts on mass shootings are stronger when combined via interaction. Specifically, the results indicate areas with the highest number of mass shootings are those that combine both high levels of inequality and high levels of income. Additionally, robustness checks incorporating various measures of mass shootings and alternative regression techniques had analogous results. Our findings suggest that to address the mass shootings epidemic at its core, it is essential to understand how to stem rising income inequality and the unstable environments that we argue are created by such inequality

FTIR Study of Copper Agglomeration during Atomic Layer Deposition of Copper

The growth of of metallic copper by atomic layer deposition (ALD) using copper(I) di-sec-butylacetamidinate $([Cu(^sBu-amd)]_2)$ and molecular hydrogen $(H_2)$ on $SiO_2/Si$ surfaces has been studied. The mechanisms for the initial surface reaction and chemical bonding evolutions with each ALD cycle are inferred from in situ Fourier transform infrared spectroscopy (FTIR) data. Spectroscopic evidence for Cu agglomeration on $SiO_2$ is presented involving the intensity variations of the $SiO_2$ LO/TO phonon modes after chemical reaction with the Cu precursor and after the $H_2$ precursor cycle. These intensity variations are observed over the first 20 ALD cycles at 185°C.Chemistry and Chemical Biolog

In-situ Infrared Characterization During Atomic Layer Deposition of Lanthanum Oxide

Mechanisms of atomic layer deposition (ALD) growth of lanthanum oxide on H-terminated Si(111) using lanthanum tris(N,N′-diisopropylacetamidinate) (La(iPr-MeAMD)3) are investigated using infrared (IR) absorption spectroscopy. The reactivity of this amidinate precursor is high, with almost all surface Si−H bonds consumed after 5 ALD cycles at 300 °C. Gas phase IR spectra show that, although most of the precursor (La(iPr-MeAMD)3) remains intact, a strong feature at 1665 cm−1, characteristic of a hydrogenated and dissociated free ligand with localized electrons in the N−CN bonds, is present. Such partial precursor dissociation in the gas phase is due to hydrolysis by traces of water vapor remaining in the reactor, even after purging. As a result, some Si−O−La bonds are formed upon reaction with the surface during the first La(iPr-MeAMD)3 pulse, prior to any water pulse. During film growth, acetate/carbonate and hydroxyl impurities are incorporated into the film. Annealing to 500 °C in dry N2 removes these impurities but fosters the growth of interfacial SiO2. Deposition at 300 °C leads to decomposition of adsorbed ligands, as evidenced by the formation of cyanamide or carbodiimide vibrational bands (or both) at 1990 and 2110 cm−1, respectively. Despite this decomposition, ideal self-limited ALD growth is maintained because the decomposed ligands are removed by the subsequent water pulse. Growth of pure lanthanum oxide films is often characterized by nonuniform film thickness if purging is not complete because of reversible absorption of water by the La2O3 film. Uniform ALD growth can be maintained without a rigorous dry purge by introducing alternating trimethylaluminum (TMA)/D2O ALD cycles between La/D2O cycles.Chemistry and Chemical Biolog

Asymmetrical domain wall propagation in bifurcated PMA wire structure due to the Dzyaloshinskii-Moriya interaction

Controlling domain wall (DW) motion in complex magnetic network structures is of paramount significance for the development of spin-based devices. Here, we report on the dynamics of a propagating DW in a bifurcated ferromagnetic wire with perpendicular magnetic anisotropy (PMA). The Dzyaloshinskii-Moriya interaction (DMI) in the wire structure induces a tilt angle to the injected DW, which leads to a quasi-selective propagation through the network branch. The DW tilting causes a field interval between DWs to arrive at Hall bars in the individual branches. Micromagnetic results further show that by tailoring the strength of the DMI, the control of DW dynamics in the PMA complex network structures can be achieved

REED: Chiplet-Based Scalable Hardware Accelerator for Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) has emerged as a promising technology for processing encrypted data without the need for decryption. Despite its potential, its practical implementation has faced challenges due to substantial computational overhead. To address this issue, we propose the $first$ chiplet-based FHE accelerator design `REED\u27, which enables scalability and offers high throughput, thereby enhancing homomorphic encryption deployment in real-world scenarios. It incorporates well-known wafer yield issues during fabrication which significantly impacts production costs. In contrast to state-of-the-art approaches, we also address data exchange overhead by proposing a non-blocking inter-chiplet communication strategy. We incorporate novel pipelined Number Theoretic Transform and automorphism techniques, leveraging parallelism and providing high throughput. Experimental results demonstrate that REED 2.5D integrated circuit consumes 177 mm$^2$ chip area, 82.5 W average power in 7nm technology, and achieves an impressive speedup of up to 5,982$\times$ compared to a CPU (24-core 2$\times$Intel X5690), and 2$\times$ better energy efficiency and 50\% lower development cost than state-of-the-art ASIC accelerator. To evaluate its practical impact, we are the $first$ to benchmark an encrypted deep neural network training. Overall, this work successfully enhances the practicality and deployability of fully homomorphic encryption in real-world scenarios

Bis(2-{[(9H-fluoren-2-yl)methyl­idene]amino}­phenolato-κ2 N,O)zinc methanol disolvate

In the title compound, [Zn(C20H14NO)2]·2CH3OH, the ZnII atom lies on a crystallographic twofold rotation axis and is coordinated by two O atoms and two N atoms from two bidentate 2-{[(9H-fluoren-2-yl)methyl­idene]amino}­phenolate ligands within a distorted tetra­hedral geometry. The dihedral angle between the two chelate rings is 82.92 (5)°. In the coordinated ligand, the phenol ring is twisted at 30.22 (9)° from the mean plane of the fluorene ring. In the crystal, O—H⋯O hydrogen bonds link the complex mol­ecules to the methanol solvent mol­ecules