Shape Evolution of the Interest Rate Term Structure

This paper adopts a novel approach to studying the evolution of interest rate term structure over the U.S. business cycles and to predicting recessions. Applying an effective algorithm, I classify the Treasury yield curve into distinct shapes and find the less frequent shapes intrinsically linked to the recessions in the post-WWII data. In forecasting recessions, the median-short yield spread trumps the long-short spread for horizons up to 17 months ahead and the yield curve shape is nearly impressive as the median-short spread. Overall, the yield curve shape is an informative but more succinct indicator than the spreads in studying the term structure. Key words: Business cycle, recession forecast, U.S. Treasury yield curve, yield spreads

Enhancing targeted transferability via feature space fine-tuning

Adversarial examples (AEs) have been extensively studied due to their potential for privacy protection and inspiring robust neural networks. Yet, making a targeted AE transferable across unknown models remains challenging. In this paper, to alleviate the overfitting dilemma common in an AE crafted by existing simple iterative attacks, we propose fine-tuning it in the feature space. Specifically, starting with an AE generated by a baseline attack, we encourage the features conducive to the target class and discourage the features to the original class in a middle layer of the source model. Extensive experiments demonstrate that only a few iterations of fine-tuning can boost existing attacks' targeted transferability nontrivially and universally. Our results also verify that the simple iterative attacks can yield comparable or even better transferability than the resource-intensive methods, which rest on training target-specific classifiers or generators with additional data. The code is available at: github.com/zengh5/TA_feature_FT.Comment: 9 pages, 10 figures, accepted by 2024ICASS

Distances to the Supernova Remnants in the Inner Disk

Distance measurements of supernova remnants (SNRs) are essential and important. Accurate estimates of physical size, dust masses, and some other properties of SNRs depend critically on accurate distance measurements. However, the determination of SNR distances is still a tough task. Red clump stars (RCs) have a long history been used as standard candles. In this work, we take RCs as tracers to determine the distances to a large group of SNRs in the inner disk. We first select RC stars based on the near-infrared (IR) color-magnitude diagram (CMD). Then, the distance to and extinction of RC stars are calculated. To extend the measurable range of distance, we combine near-IR photometric data from the 2MASS survey with the deeper UKIDSS and VVV surveys. With the help of the Gaia parallaxes, we also remove contaminants including dwarfs and giants. Because an SN explosion compresses the surrounding interstellar medium, the SNR region would become denser and exhibit higher extinction than the surroundings. The distance of a SNR is then recognized by the position where the extinction and its gradient is higher than that of the ambient medium. A total of 63 SNRs' distances in the Galactic inner disk are determined and divided into three Levels A, B, and C with decreasing reliability. The distances to 43 SNRs are well determined with reliability A or B. The diameters and dust masses of SNRs are estimated with the obtained distance and extinction.Comment: 31 pages, 25 figures, 2 tables, accepted for publication in A&

Shapes and Transitions of the Interest Rate Term Structure

I analyze different shapes of Treasury yield curves in order to better reflect and predict the U.S. economy. Since the late 1980s, macroeconomists have found that the slope of the yield curve predicts economic activity such as inflation, output growth, and recessions, but they have not fully examined the links between various shapes of yield curve and the macroeconomy. To fill the gap, I classify yield curve shapes with the U.S. Treasury yield data, detect the shape patterns over the business cycles, and map these shapes onto corresponding inflation and production states. Although the downward-sloping yield curve reliably predicts U.S. recessions, its signals were present during some recessions. Moreover, the hump, flat and bowl-shaped yield curve also demonstrate their ability to forecast recessions and the prediction becomes more accurate after the 1982 recession. However, it is still challenging to establish the link between each shape and the macroeconomic state. To forecast future economic states, I model and estimate the yield curve transition pro- cess, evaluate alternative models and perform validation tests. I find that the shape transition displays significant momentum and asymmetry. But the information from the shape transition is not quite helpful in forecasting macroeconomic states

Spatial Variations of Dust Opacity and Grain Growth in Dark Clouds: L1689, L1709 and L1712

The far-infrared (FIR) opacity of dust in dark clouds within the Ophiuchus molecular cloud is investigated through multi-wavelength infrared observations from UKIDSS, Spitzer and Herschel. Employing the infrared color excess technique with both near-infrared (NIR) and mid-infrared (MIR) photometric data, a high-resolution extinction map in the $K$ band ($A_K$) is constructed for three dark clouds: L1689, L1709, and L1712. The derived extinction map has a resolution of $1'$ and reaches a depth of $A_K\sim3$ mag. The FIR optical depths $\tau_{250}$ at a reference wavelength of $250\,\rm \mu m$ are obtained by fitting the Herschel PACS and SPIRE continuum data at 100, 160, 250, 350 and 500 $\rm \mu m$ using a modified blackbody model. The average dust opacity per unit gas mass at $250\rm \mu m$, $r\kappa_{250}$ is determined through a pixel-by-pixel correlation of $\tau_{250}$ with $A_K$, yielding a value of approximately $0.09\,\rm cm^2\,g^{-1}$, which is about 2-3 times higher than the typical value in the diffuse interstellar medium (ISM). Additionally, an independent analysis across 16 sub-regions within the Ophiuchus cloud indicates spatial variations in dust opacity, with values ranging from 0.07-0.12$\,\rm cm^2\,g^{-1}$. Although the observed trend of increasing dust opacity with higher extinction implies grain growth, our findings indicate that rapid grain growth clearly not yet occurred in the dark clouds studied in this work.Comment: Accepted for publication in ApJ (16 pages, 8 figures, 3 tables

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables

We investigate the challenging task of learning causal structure in the presence of latent variables, including locating latent variables and determining their quantity, and identifying causal relationships among both latent and observed variables. To address this, we propose a Generalized Independent Noise (GIN) condition for linear non-Gaussian acyclic causal models that incorporate latent variables, which establishes the independence between a linear combination of certain measured variables and some other measured variables. Specifically, for two observed random vectors $\bf{Y}$ and $\bf{Z}$, GIN holds if and only if $\omega^{\intercal}\mathbf{Y}$ and $\mathbf{Z}$ are independent, where $\omega$ is a non-zero parameter vector determined by the cross-covariance between $\mathbf{Y}$ and $\mathbf{Z}$. We then give necessary and sufficient graphical criteria of the GIN condition in linear non-Gaussian acyclic causal models. Roughly speaking, GIN implies the existence of an exogenous set $\mathcal{S}$ relative to the parent set of $\mathbf{Y}$ (w.r.t. the causal ordering), such that $\mathcal{S}$ d-separates $\mathbf{Y}$ from $\mathbf{Z}$. Interestingly, we find that the independent noise condition (i.e., if there is no confounder, causes are independent of the residual derived from regressing the effect on the causes) can be seen as a special case of GIN. With such a connection between GIN and latent causal structures, we further leverage the proposed GIN condition, together with a well-designed search procedure, to efficiently estimate Linear, Non-Gaussian Latent Hierarchical Models (LiNGLaHs), where latent confounders may also be causally related and may even follow a hierarchical structure. We show that the underlying causal structure of a LiNGLaH is identifiable in light of GIN conditions under mild assumptions. Experimental results show the effectiveness of the proposed approach