The semi-parametric midas models and some of their applications: the impact of news on the stock volatility

In the first essay, I examine whether the sign and magnitude of discretely sampled high frequency returns have impact on expected volatility over some future horizon. Technically speaking, I introduce semi-parametric MIxed DAta Sampling (henceforth MIDAS) regressions. I show that the asymptotic distribution of semi-parametric MIDAS regressions depends on mixture of sampling frequencies. Also novel is the parametric specification I consider to deal with (intra-daily) seasonality. In the empirical work, I find that moderately good (intra-daily) news reduces volatility (the next day), while both very good news (unusual high intra-daily positive returns) and bad news (negative returns) increase volatility, with the latter having a more severe impact. The asymmetries disappear over longer horizons. I also introduce a new class of parametric models with close ties to ARCH-type models, albeit applicable to high frequency data. In the second essay, I extend the semi-parametric MIDAS model to multivariate case and find that besides the asymmetric effect, the market-wide news and firm-specific news interactively affect the individual firm's future volatility and using both of them can increase the out-of-sample forecast performance. In the third essay, I propose a new type of semi-parametric MIDAS index model, which potentially applies in a variety of fields, and investigate its estimation and asymptotics

Method to detect gravitational waves from an ensemble of known pulsars

Combining information from weak sources, such as known pulsars, for gravitational wave detection, is an attractive approach to improve detection efficiency. We propose an optimal statistic for a general ensemble of signals and apply it to an ensemble of known pulsars. Our method combines F-statistic values from individual pulsars using weights proportional to each pulsar’s expected optimal signal-to-noise ratio to improve the detection efficiency. We also point out that to detect at least one pulsar within an ensemble, different thresholds should be designed for each source based on the expected signal strength. The performance of our proposed detection statistic is demonstrated using simulated sources, with the assumption that all pulsar ellipticities belong to a common (yet unknown) distribution. Comparing with an equal-weight strategy and with individual source approaches, we show that the weighted combination of all known pulsars, where weights are assigned based on the pulsars’ known information, such as sky location, frequency and distance, as well as the detector sensitivity, always provides a more sensitive detection statistic

Stellar Stream Candidates in the Solar Neighborhood Found in the LAMOST DR3 and TGAS

We have cross-matched the LAMOST DR3 with the Gaia DR1 TGAS catalogs and obtained a sample of 166,827 stars with reliable kinematics. A technique based on the wavelet transform was applied to detect significant overdensities in velocity space among five subsamples divided by spatial position. In total, 16 significant overdensities of stars with very similar kinematics were identified. Among these, four are new stream candidates and the rest are previously known groups. Both the U-V velocity and metallicity distributions of the local sample show a clear gap between the Hercules structure and the Hyades-Pleiades structure. The U-V positions of these peaks shift with the spatial position. Following a description of our analysis, we speculate on possible origins of our stream candidates.Comment: 16 pages, 5 figure

Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table Transformers

For machine learning with tabular data, Table Transformer (TabTransformer) is a state-of-the-art neural network model, while Differential Privacy (DP) is an essential component to ensure data privacy. In this paper, we explore the benefits of combining these two aspects together in the scenario of transfer learning -- differentially private pre-training and fine-tuning of TabTransformers with a variety of parameter-efficient fine-tuning (PEFT) methods, including Adapter, LoRA, and Prompt Tuning. Our extensive experiments on the ACSIncome dataset show that these PEFT methods outperform traditional approaches in terms of the accuracy of the downstream task and the number of trainable parameters, thus achieving an improved trade-off among parameter efficiency, privacy, and accuracy. Our code is available at github.com/IBM/DP-TabTransformer.Comment: submitted to ICASSP 202

ICStega: Image Captioning-based Semantically Controllable Linguistic Steganography

Nowadays, social media has become the preferred communication platform for web users but brought security threats. Linguistic steganography hides secret data into text and sends it to the intended recipient to realize covert communication. Compared to edit-based linguistic steganography, generation-based approaches largely improve the payload capacity. However, existing methods can only generate stego text alone. Another common behavior in social media is sending semantically related image-text pairs. In this paper, we put forward a novel image captioning-based stegosystem, where the secret messages are embedded into the generated captions. Thus, the semantics of the stego text can be controlled and the secret data can be transmitted by sending semantically related image-text pairs. To balance the conflict between payload capacity and semantic preservation, we proposed a new sampling method called Two-Parameter Semantic Control Sampling to cutoff low-probability words. Experimental results have shown that our method can control diversity, payload capacity, security, and semantic accuracy at the same time.Comment: 5 pages, 5 tables, 3 figures. Accepted by ICASSP 202

Method to detect gravitational waves from an ensemble of known pulsars

Combining information from weak sources, such as known pulsars, for gravitational wave detection, is an attractive approach to improve detection efficiency. We propose an optimal statistic for a general ensemble of signals and apply it to an ensemble of known pulsars. Our method combines ℱ-statistic values from individual pulsars using weights proportional to each pulsar’s expected optimal signal-to-noise ratio to improve the detection efficiency. We also point out that to detect at least one pulsar within an ensemble, different thresholds should be designed for each source based on the expected signal strength. The performance of our proposed detection statistic is demonstrated using simulated sources, with the assumption that all pulsar ellipticities belong to a common (yet unknown) distribution. Comparing with an equal-weight strategy and with individual source approaches, we show that the weighted combination of all known pulsars, where weights are assigned based on the pulsars’ known information, such as sky location, frequency and distance, as well as the detector sensitivity, always provides a more sensitive detection statistic

Hybrid PolyLingual Object Model: An Efficient and Seamless Integration of Java and Native Components on the Dalvik Virtual Machine

Copyright © 2014 Yukun Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. JNI in the Android platform is often observed with low efficiency and high coding complexity. Although many researchers have investigated the JNI mechanism, few of them solve the efficiency and the complexity problems of JNI in the Android platform simultaneously. In this paper, a hybrid polylingual object (HPO) model is proposed to allow a CAR object being accessed as a Java object and as vice in the Dalvik virtual machine. It is an acceptable substitute for JNI to reuse the CAR-compliant components in Android applications in a seamless and efficient way. The metadata injection mechanism is designed to support the automatic mapping and reflection between CAR objects and Java objects. A prototype virtual machine, called HPO-Dalvik, is implemented by extending the Dalvik virtual machine to support the HPO model. Lifespan management, garbage collection, and data type transformation of HPO objects are also handled in the HPO-Dalvik virtual machine automatically. The experimental result shows that the HPO model outweighs the standard JNI in lower overhead on native side, better executing performance with no JNI bridging code being demanded. 1

Intracranial Arteriovenous Malformation Combined with Multiple Aneurysms Diagnosed by CTA: A Case Report

Arteriovenous malformation (AVM) combined with aneurysms is not uncommon, but AVM of the basilar artery, brainstem, and right middle cerebral artery combined with multiple intracranial aneurysms (IAs) is rare. Cases of aneurysm protrusion into the optic canal are also rare. We report a distinctive case of intracranial AVM combined with multiple IAs and partial protrusion of a cavernous segment aneurysm of the right internal carotid artery into the optic nerve canal. Teaching Point: Cases of partial protrusion of a cavernous segment aneurysm of the right internal carotid artery into the optic canal, resulting in widening of the optic canal compared to the contralateral side, compression, thickening and swelling of the subocular veins, and obstruction of venous drainage warrant the clinician’s attention